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Assessment of Learning Outcomes

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• Hamish Coates 6  

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In most parts of the world higher education is in demand like never before. As systems and institutions expand and diversify, more energy must be invested in ensuring that sufficient learning has been achieved to warrant the award of a qualification. Yet traditional approaches to assessment do not scale well, and given that assessment has yet to be modernized there remains a pressing need to transform this core facet of education. Accordingly, this chapter starts by analysing imperatives for improving the assessment of student learning outcomes. It introduces a model for reviewing progress in the field, and applies this model to several case study initiatives. This exercise yields findings that are distilled into recommendations for advancing assessment and thereby enhancing transparency hence the quality and productivity of future higher education.

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• Learning outcomes
• Education quality
• Transparency

1 Introduction

Through a strategic analysis of assessment in higher education, this chapter clarifies rationales for assessment reform, critically evaluates progress to date, reviews knots tangling progress, and highlights change opportunities. The analysis concludes by advancing the need for serious work on assessment redesign that funnels improvement investments in the most effective ways. Taking stock of research and framed for a specific policy purpose, this chapter is necessarily brief and lightly referenced. Readers are referred to Coates ( 2014 ) for a more comprehensive treatment of major topics.

The assessment of higher education student learning outcomes is very important. Assessment provides essential assurance to a wide variety of stakeholders that people have attained various knowledge and skills, and that they are ready for employment or further study. More broadly, assessment signposts, often in a highly distilled way, the character of an institution and its educational programs. Much assessment is expensive, making it an important focus for analysis. Assessment shapes education and how people learn in powerful direct and also indirect ways. Of course, assessment is highly relevant to individuals, often playing a major role in defining life chances and directions.

Given such significance it is surprising that much assessment in higher education has not changed materially for a very long time, and that economically and technically unsustainable practice is rife. While there are, of course, an enormous number of innovative and high-quality developments, including those associated with technology advances, everyday around the world students still write exams using pen and paper, sitting in large halls at small desks in rows without talking. It is possible that this reflects the pinnacle of assessment, but given the lack of reflective technological advance over an extended period, this seems unlikely. Rather, given the enormous changes reshaping core facets of higher education, and pressures and prospects surrounding assessment, it is more likely that the ‘transformational moment’ has yet to come. As this chapter portends, however, with the right investment and intellect the revolution may be closer than ever.

This chapter provides contemporary insights into the assessment of higher education learning outcomes, surveying recent progress and clarifying prospects for further transformational advance. It begins by recapping rationales for reforming this facet of higher education. It then takes stock of progress through an evaluative review of several prominent assessment initiatives. While far from exhaustive, this review highlights the broad scope and pretext for growth. Two subsequent sections help channel future energy. First, using risk-assessment logic, the chapter reviews what would appear to be the major change blockers. Second, a broad cost/benefit logic is deployed to identify specific options for development. With these analyses to hand, the chapter concludes by advancing a program of assessment redesign, and sketching initial tactics for its development.

Assessment is a broad area, and this analysis could be progressed in a variety of ways, so it is helpful to clarify scope and assumptions. The term ‘assessment’ is interpreted very broadly as involving the measurement, reporting and interpretation of student learning and development. The analysis embraces formative and summative assessment, and ranges from in-class to cross-national practice, but emphasis is placed on formal assessment that is relevant to establishing the quality of individual learning. The analysis is pitched to be policy relevant regardless of whether local or large-scale practice is being addressed. Attention is focused specifically on assessment, rather than on a host of surrounding activities such as curriculum design, quality assurance or funding, though these are undoubtedly relevant and must be factored into any extended analysis. As these introductory remarks convey, the chapter adopts a critical stance in which it is assumed that assessment must be improved. It is assumed that the continued use of proxy measures for outcomes like statistics on graduate employment or further graduate study, or the use of qualification/organisation-level accreditation in place of robust measures of individual competence, is unsatisfactory (for analysis see: Coates 2010 ). The analysis is driven by a general desire to improve both the quality and productivity of education. To strengthen higher education, it is assumed that assessment must be done better and more efficiently, and it is assumed transparency plays an important role in this.

Throughout this chapter, mention is made to ‘routine’, or ‘conventional’, or ‘traditional’ assessment practice. This refers to a vast range of activities which are helpful to clarify at the outset given that this chapter is framed as a critique. Broadly, such assessment can be caricatured as involving academics working alone, and within single institutions to produce technically non-validated assessment materials that map to arbitrary parts of the curriculum of a single subject. Such assessment might be delivered in formats and practices unchanged for many decades, scored normatively by different markers without rubrics or training, analysed using basic additive methods, adjusted to fit percentile distributions, then reported using grades that offer thin feedback. It is assumed that together these attributes give rise to a syndrome which constricts the advance of higher education. Of course this is an accentuated and overly negative picture, and innovative and excellent practice abounds, but elements of such practice remain regrettably rife across all fields, including those which are subject to professional accreditation.

2 A Growing Imperative for Transforming Assessment

In most countries university education is in demand like never before. Yet many traditional approaches to higher education do not scale well, challenging the quality and productivity of supply. Meeting greater demand increasingly requires new and different ways of doing education. Also, as higher education expands and diversifies, more energy must be invested in ensuring that sufficient learning has been achieved to warrant the award of a qualification. Yet assessment would appear to be one of the final change frontiers in the contemporary reconfiguration of higher education. Much assessment has not changed for a century, yet other facets of education have transformed, and student learning is subjected to increasing scrutiny. To launch the discussion and frame subsequent analysis, it is helpful to explore imperatives for reforming the assessment of learning outcomes. The summary presented here draws on much more extensive analysis elsewhere (Coates 2014 ; Coates and Mahat 2013 , 2014 ), and necessarily takes for granted broader changes taking place in many higher education systems.

First, there is value in advancing assessment in the spirit of continuous improvement. There are intrinsic grounds for ongoing improvement, but also more contextual rationales so that assessment keeps pace with changes in knowledge, curriculum, teaching, institutions, and learning. Christensen and Eyring ( 2011 ) document how higher education is undergoing radical change with disruptive innovation at its core. Despite substantial improvement in many parts of higher education, student knowledge and skill is still most commonly measured in the traditional ways characterised above. A narrative flowing across this chapter is that assessment has yet to have its game-changing moment. Whether change is transformational or incremental, however, there are intrinsic grounds for ongoing improvement.

Second, there are strategic institutional rationales for finding innovative ways to assess student learning. Assessment resources and processes signify in non-trivial ways what an institution delivers—variations in assessment imply variations in education and graduates. In an industry dominated by research metrics, assessment offers fresh territory for institutions to showcase education activity and performance (Coates and Richardson 2012 ).

Third, there is enormous value for institutions, faculty, students and governments in finding cheaper ways to assess student learning. While methods and contexts vary, assessment typically has high fixed and variable costs and limited economies of scale, as with many other facets of conventional higher education teaching and learning (for a summary of relevant economics see Coates and Mahat 2014 ). Increasing cost- and revenue-constraints magnify pressure to develop more efficient forms of assessment without eroding quality. Through one lens, current assessment arrangements can be seen as standing in the path of broader productivity improvements in higher education.

Fourth, concerns about quality are prompting changes in assessment. Through projects such as OECD AHELO (Coates and Richardson 2012 ) governments signalled that conventional assessment approaches were not delivering required or sufficient information on what students know and can do. As well, more robust assessment would do much to address seemingly persistent employer concerns about graduate capability, if only by clarifying and advancing debate. Educators, too, have taken steps to advance or supplement work in their field (e.g. Edwards et al. 2012 ; MSC 2014 ). Quality pressures also provoke the need for more transparency regarding assessment, as in other academic functions.

Fifth, producing more cogent data on outcomes would help prove the returns from education. Currently, important economic debates about education proceed without reference to learning outcomes (DoE 2014 ; RAND 2014 ; Sullivan et al. 2012 ). The broad contribution of higher education is often measured through reference to the production of graduates, and the qualitative difference between graduates counted indirectly via differential employment, or further study outcomes (all else being equal, graduates with better transcripts from more reputable institutions in the field may be expected to secure better work or academic outcomes). The availability of better information on learning makes possible estimation based on the quality of outcomes, not just the quantity of outputs. Indeed, producing reasonable measures of productivity is extremely difficult without valid outcomes data, which carries obvious implications for institutional management and system steering.

Sixth, a further need to improve assessment flows from the limitations of prior quality-related initiatives. As discussed later, in the last few decades a suite of quality initiatives have attempted to address the paucity of information on education, but none have reaped promised change. Institution-level quality audits have failed to yield sufficient information on student learning (Dill 2014 ; Krzykowski and Kinser 2014 ). Rankings address partial performance in specific contexts, but focus on research (Federkeil et al. 2012 ; Van Vught 2012 ). Competency specification approaches, such as the Tuning Process (González and Wagenaar 2008 ), have considerable merit, but frame expected rather than actual outcomes. National qualification frameworks began as a move towards competency-based education, but have become policy instruments which often underemphasise specific contexts (McBride and Keevy 2010 ). Questionnaire-derived metrics (e.g. Coates and McCormick 2014 ) are valuable, but only deliver proxy information on student learning. Assessment projects have been initiated (Coates and Richardson 2012 ; Edwards et al. 2012 ; Canny and Coates 2014 ), but these have yet to yield required change.

Anyone working in or around higher education recognises that these reform pressures play out in varying ways at different moments, that assessment is only part of a very much larger story, and that the above analysis is inevitably broad and incomplete. Yet taken together, these pressures explain more than a little of the need to reform assessment, and hence, spur the need to advance work on assessing learning outcomes.

3 Taking Stock of Existing Change Initiatives

The lack of modernisation of assessment is not a result of lack of imagination or effort. In the last few decades many endeavours have sought to unblock the development of assessment. It is helpful to take evaluative stock of the field to showcase recent work and ground the analyses that follow. Clearly, taking critical stock of a field as large and diverse as higher education assessment is a useful though challenging task—there are an enormous number of actors and initiatives, each at varying stages of maturity and diffusion. Rather than conduct an exhaustive review of specific assessment initiatives, therefore it is feasible to survey a series of broad developments which have sought to move beyond routine practice.

Important seeds of a fruitful evaluation lie in finding a helpful frame and appropriate level at which to pitch the analysis. The Assessment Transparency Model (ATM) (Coates and Mahat forthcoming ) is deployed as a useful means for reflecting critically on the extent of formalisation and optimisation of assessment without assuming the maturation implies standardisation. Indeed, to avoid subsequent confusion it is helpful at this point to clarify a common misinterpretation of the term ‘standards’ and its various linguistic derivations. This chapter does indeed argue for the need to improve the standards of assessment design and practice. As in any area, it is contended that enhancing the standards of assessment will encourage diversification and excellence both in terms of education and outcomes. The chapter does not argue for the standardisation of assessment processes, resources or outcomes in everyday education contexts.

The ATM (Fig.  1 ) blends developmental and activity dimensions. The first dimension marks out a suite of academic phases, with these ordered according to a continuum of increasing transparency. At the foundation level there are ‘anarchical’ forms of truly collegial practice, reflecting what was characterised above as boutique or traditional forms of work. ‘Appreciation’ marks the next most transparent phase, reflecting awareness that new academic approaches are available. After this, the ‘articulation’ phase denotes the explicit documentation of new academic practices in a descriptive or normative sense. ‘Application’, the penultimate phase, signals that new practices have been actioned. ‘Amalgamation’ is the final phase, signalling the integration and sharing of academic processes and outcomes. The model charts the maturity of each of these five transparency phases along a second dimension. Each phase can be characterised as being at the formulation stage, the implementation stage, or the evaluation stage.

Assessment transparency model (ATM)

Building academics’ assessment skills and capacity is arguably the most significant intervention. Such work might incorporate supplementary programs for doctoral students, academic professional development, advanced graduate study, or project activities. Even though education is a core pillar of higher education, it would be reasonable to describe the training of prospective or current academics in assessment as spasmodic. Such development has the potential to lift practice beyond anarchy, and build appreciation of student learning and assessing outcomes. With a focus on individual or organisational rather than resource development, such training can tend to fall short of creating clearer articulation of outcome or task specifications, though it may result in diverse forms of applied work, and possibly even instil a milieu for benchmarking and other shared interpretative activities.

One broad line of development has involved specifying qualification-level outcomes. Examples include the European Qualifications Framework, the United Kingdom Subject Benchmark Statements, the Australian Qualifications Framework, and the United States Degree Qualification Profile. As the titles convey, this work is developed and owned by systems, and such initiatives have served as important policy instruments for shifting beyond an anarchic plethora of qualifications, generating conversations about finding more coherence, and indeed articulating the general outcomes graduates should expect from a qualification (Chakroun 2010 ). These system-wide structures can suffer from unhelpful collisions with fruitfully divergent local practice, but their inherent constraint is that they go no further than articulating very general graduate outcomes. They offer little beyond broad guidelines for improving the assessment of student learning.

Going one step further, a further line of work has sought to specify learning outcomes at the discipline level. The Tuning Process (González and Wagenaar 2008 ) is a prominent example which has been initiated in many education systems, and across many diverse disciplines. Broadly, Tuning involves supporting collaboration among academics with the aim of generating convergence and common understanding of generic and discipline-specific learning outcomes. Canada adapted this work in innovative ways, focusing the collaborations around sector-oriented discipline clusters rather than education fields (Lennon et al. 2014 ), while in Australia a more policy-based and regulatory-focused approach was deployed (ALTC 2010 ). Such collaboration stimulates appreciation and articulation of learning outcomes, going several steps further than qualification frameworks by engaging and building academic capacity within disciplinary contexts. Like the qualification frameworks, however, the work usually stops short of advancing assessment resources, and tends to focus instead on advancing case studies or best practice guidelines. Hence while it may arise in particular fields, there is no emphasis on the application of common procedures or amalgamation of shared results. In short—there is no ‘data on the table’. As well, it must be noted, while the Tuning Process has proliferated internationally there has been little if any summative evaluation, which would add to its traction.

A slightly deeper line of development involves the application of shared rubrics to moderate assessment tasks or student performance. Moderation in assessment can play out in many ways (Coates 2010 ) as indeed has been the case in recent higher education initiatives. The moderation of resources has involved rudimentary forms of peer review through to slightly more extensive forms of exchange. Mechanisms have also been developed to help moderate student performance. In the United States, for instance, the AAC&U (Rhodes and Finley 2013 ) has developed VALUE rubrics for helping faculty assess various general skills. The United Kingdom’s external examiner system (QAA 2014 ) is a further example. Several such schemes have been launched in Australia, including a Quality Verification System and a Learning and Teaching Standards Project, both of which involve peer review and moderation across disciplines (Marshall et al. 2013 ). This work travels deeper than qualification- or discipline-level specifications, for it involves the collation and sharing of evidence on student performance, often in ways that engage faculty in useful assurance and development activities. Such moderation work is limited, however, in being applied in isolation from other assessment activities and materials. Hence it implies various unsystematic forms of application and amalgamation.

Collaborative assessments build from the developments discussed so far to advance more coherent and expansive approaches to shared assessment. As with other developments addressed here, such work plays out in myriad ways. For instance, medical progress testing in the Netherlands (Schuwirth and Van De Vleuten 2012 ) involves the formation of shared assessment materials, and administration of these in a longitudinal sense. Other assessment collaborations have focused on the development of shared tasks, analytical or reporting activities (e.g. Edwards et al. 2012 ; Zlatkin-Troitschanskaia et al. 2014 ). Such work is impressive as it tends to involve the most extensive forms of outcome specification, task production, assessment administration, analysis and reporting, and at the same time develop faculty capacity. Typically it travels far beyond anarchical practice to include various forms of articulation, application and amalgamation. Work plays out in different ways, however, shaped by pertinent collegial, professional and academic factors. This can mean, for instance, that extensive work is done that leads to little if any benchmarking or transparent disclosure.

Standardised assessment is easily the most extensive form of development, and would appear to be growing in scope and scale. Licensing examinations are the most longstanding and pervasive forms of assessment, though their use is cultural and they tend to be far more common in the United States than Europe, for example. A series of graduate outcomes tests have also been trailed in recent years, such as the OECD’s Assessment of Higher Education Learning Outcomes (AHELO) (Coates and Richardson 2012 ), the United States Collegiate Learning Assessment (Shavelson 2007 ) and the Proficiency Profile (ETS 2014 ). Standardised assessments are also promulgated via commercial textbooks (Pearson 2014 ). As the term ‘standardised’ implies, these assessments tend to tick many, if not all boxes in the top three rows of the assessment transparency model, though given the external sponsorship of such work, often at the expense of engaging with academics, and as part of the process shifting the workforce beyond anarchic to more sophisticated forms of practice. Though such exogenous intervention may in the longer run inject the shock required for reform, it also tends to balkanise internal from external interests and has little impact on learning or teaching practice.

4 Clearing Barriers to Progress

Clearly, there are myriad reasons why assessment has not been experienced its game-changing modernisation moment. While such reasons are invariably entwined in specific contexts and initiatives common themes can be isolated from review of several projects. These contextual challenges are considered with respect to the factors required to facilitate change. As with the preceding analysis, there is no claim that the list is exhaustive or the analysis universal. Thinking and practice in certain fields and institutions is more advanced than in others.

Obviously, people with vested interests in entrenched approaches are often significant obstacles to change. Today’s higher education leaders and faculty have often made significant institutional and individual investments in conventional assessment resources and practices. At the same time, these are the very professionals who are bearing the brunt of quality and productivity pressures. Reshaping their perspective on assessment would open myriad fresh opportunities. This is a challenging point to make, yet remains a task that cannot be ignored.

Relevant professional capability and capacity is required to change assessment practice, which in the field of higher education is in short supply. Higher education itself lacks dedicated assessment professionals, and there appear to be too few assessment specialists with relevant industry experience (Coates and Richardson 2012 ). As picked up in the conclusion to this chapter, the lack of a professional assessment community is an obvious impediment to change. Building a new profession of assessment experts or a community of faculty with interest in assessment requires investment by higher education institutions and stakeholders, yet can ultimately be addressed through training and development. This has already happened in certain contexts—the United States higher education and medical education are obvious examples—yet there is a need to broaden practice.

Academics require professional training and development to improve competence in assessment, yet such training has really only evolved over the last few decades, and as noted above, is spasmodic. It would be helpful to cite figures on the incidence of such training among academics, and while it affirms the point, it is regrettable that such figures do not exist. Most academics learn their trade via what could be characterised as an informal apprenticeship, and while competence in assessment is no exception, this does not discount the need for creating more systematic forms of professional development. Improving assessment capability among academics will do much to encourage diversification and excellence.

Inasmuch as academic autonomy, in its various encapsulations, provides faculty with a sense of private ownership over assessment it can be a significant impediment to change. Assessment by its nature is a very public and formal matter, and subject to any material constraints should be as transparent as any other academic activity. Research proposals and papers undergo peer review, and there is no reason why assessment tasks should not as well. Academic autonomy is invariably a contingent rather than absolute phenomenon, and it is likely that training and management could advance more sophisticated conceptualisations of professional practice.

Often the most profound shocks are exogenous to a system. The rise of online technology and policies impelling increasing marketization of higher education are two examples. By definition such shocks are highly significant to advancing education, yet are profoundly difficult to forecast or induce. Ultimately, as in many industries, new technologies and business processes are required to adapt.

Inherent security and confidentiality constraints play an obvious role in constraining assessment reform. The greater the stakes, the greater the security and confidentiality implications. In a host of ways such constraints hinder collaboration and drive-up costs, yet contribute to the value and impact of assessment. Engineering new technologies and assessment processes seems to be the most effective means of addressing such constraints.

As assessment like other facets of higher education becomes increasingly commercial in nature, various business considerations grow as greater obstacles to change. Non-trivial intellectual property considerations may be pertinent, for instance, by hindering the sharing and replication of materials. Working through such obstacles can be expensive and complex, yet in many instances is ultimately resolvable with appropriate negotiations and agreement.

It is likely the assessment of student learning doesn’t change given its low priority to institutions (surprisingly). From many perspectives the current system seems ‘good enough’, and besides pressure from accreditation or employers there can appear to be little impetus to change. Data from assessments are not included in international institutional rankings, for instance, and academic promotions practices typically favour research over education performance. As these remarks portend, sparking change on this front likely requires an external commercial or regulatory intervention.

Traditional higher education structures can hamper progress, creating confusion about who should own change. Individual faculty focus on assessing particular subjects, departments focus on majors, and students and institutions on qualifications. Fragmentation of curriculum and cohorts can further hinder the formation of coherent assessment schemes. This can create an ownership or agency problem, rendering change problematic. Changing this dynamic typically involves developing and managing more collaborative forms of academic practice.

Academics’ belief in the success of current practice is likely to be a major change barrier. Indeed, current practice may well work locally, yet be unsustainable in broader or different contexts. An assessment task may be perfectly aligned with an academic’s curriculum and teaching, for instance, yet fail to contribute to the qualification-level information required for external professional accreditation. Institutions have varying ways for leading change in academic practice, which ultimately must resonate with prevailing policies and norms.

In reviewing challenges in changing assessment practice in higher education it appears that change, in summary, hinges on further academic professional development, changed institutional management, ongoing technology and business process development, and external commercial or policy intervention. None of these facilitators are easy to plan or enact. Given the complexity and difficulty of the task to hand, there seems value in pushing on all fronts in synchrony, noting that even by passing through various tipping points, reform is likely to be haphazard and take time.

5 Making Progress that Counts

To yield the best outcomes it is essential to invest constrained time and resources in the most effective ways. What, then, are the major processes involved in assessment, and the benefits and challenges of changing each? In essence, what is the assessment supply and value chain, and how can it be improved? The emphasis on value chain (Porter 1985 ) as well as supply chain heralds the need to focus not just on technical and operational processes, but also on improving the quality and productivity of assessment for students, institutions and broader stakeholders.

Even the handful of very common forms of assessment play out in different ways, and rather than analyse academic activities such as exams or laboratory assignments, it is helpful to delve deeper to investigate more fundamental underpinnings. Key processes are organised into several phases in Table  1 . As a way forward the following analysis estimates the quality and productivity benefits that would arise from change in each phase, and the challenge associated with such change.

Assessment is underpinned by various forms of strategic and operational planning, which leads to specific governance, leadership, and management arrangements. Effective strategic planning is the key to improvement, of course, not least to build greater institutional rather than individual engagement in assessment to ensure higher-order capabilities are being assessed and more coordinated approaches to improvement. Operational planning is an area in which there would appear to be substantial grounds for development. Analysis reported elsewhere (Coates and Lennon 2014 ) suggests that collegial forms of governance appear most effective, though there is value in strengthening existing practice by adding further points of external reference. As earlier remarks convey, there would appear to be substantial benefit in adopting more advanced management of assessment, which appears to be instrumental in shifting practice beyond boutique forms of practice.

Assessment development hinges on a suite of technical, substantive and practical considerations, but fundamentally involves specification, development, validation of materials, as well as planning for their deployment. This is an area in which there are enormous quality and productivity advances to be made in re-engineering conventional practice. As discussed earlier, work is underway in particular fields and contexts on finding more collaborative and scalable approaches to specifying learning outcomes. This is important, for specifying learning outcomes is the work that links curriculum with assessment. Less advance has been made in improving the specification of concrete assessment tasks, however, with much practice still relying on convention rather than more scientific rationales. Similarly, there would appear to be substantial advance possible regarding assessment task production—feasibility has been demonstrated in large-scale initiatives, but diffusion of new techniques has been low. As well, research findings (see Coates 2014 ) affirm the need to improve the validation and production of materials. In short, beyond advances regarding definitional work, the development phase of assessment is almost entirely in need of reform.

Assessment implementation, like development, is an area in which reform would contribute significant value to higher education. As noted throughout this chapter, much assessment is delivered in highly dated ways which is particularly surprising given radical changes in other facets of higher education. This application of new technologies would appear to be instrumental for reform, as would better embrace of professional experts and organisations. Alignment with innovations in teaching may be fruitful. If specialist independent organisations can deliver assessment better and cheaper than higher education institutions, then expanding outsourcing will doubtless be seen by university executives as one among other feasible futures for this facet of higher education. As well, on transparency grounds there would appear to be value in moving beyond individual delivery to introduce more peer-reviewed or otherwise quality-assured forms of delivery. Obviously, the implications of such change for academic leadership, academic work and academic learning are in need of profound and imaginative reflection (Coates and Goedegebuure 2012 ). While such ideas may appear to collide with traditional beliefs about academic autonomy and more recent institutional competition and commerce, other facets of higher education have transformed in far more radical ways to the advantage of higher education.

The analysis and reporting phases involve significant administrative and technical work, and as with the development and implementation phases have the potential to benefit substantially from transformation. Faculty time is a major cost-driver in higher education, and particularly given the lack of specialist expertise regarding assessment, there is value in finding approaches that make the most prudent use of available resources. While various forms of peer review have been deployed via moderation systems that offer a form of cross-validation, for instance, other forms of verification exist that don’t require additional faculty resources. Substantial value would be added in any effort that further aligns assessment feedback with teaching and learning practice.

6 Assessment Redesign—A Tactic for Reform

In summary, it is concluded in this chapter that the quality and productivity of higher education would be improved by reforming almost every facet of assessment. Much assessment may be excellent and efficient, but most is not. Clearly, by this analysis extensive change is required which may seem overwhelming to plan or initiate. Much small- and large-scale work has proven the feasibility of change, yet substantial obstacles hinder the diffusion of reform. As the chapter has asserted, this is a difficult and messy area of higher education in which there are no perfect solutions. All approaches have advantages and limitations.

Building a program of work on ‘assessment redesign’ offers a way forward. Such work could adapt relevant existing institutional and governmental work (Nicol 2014 ; O’Neill and Noonan 2011 ; Twigg 2003 ). To be effective it would need to work across multiple levels and engage faculty, institutional managers and leaders, and relevant external stakeholders. Such work would need to dovetail with broader curriculum, workforce or other reform, though this is not essential and this chapter has asserted an independent need for assessment reform. To engender broad appeal and necessary faculty engagement assessment redesign must be easy to understand and implement, yet yield meaningful improvement.

Framed within the broader context of teaching and learning, a compelling research paper that resonates with both policy and practice is required to spark modernisation work on assessment redesign. Such work would in essence involve detailing:

contexts and rationales driving the need for reform, elaborating those in this chapter;

primary assessment activities such as those in Table  1 ;

assessment support activities—typically infrastructure, human resources, technology and procurement;

robust yet parsimonious processes for identifying cost drivers, and for reducing costs; and

quality and value criteria, and mechanisms for assurance and differentiation.

To have impact it is essential to carefully articulate the audience for this formative contribution. Clearly, to gain initial traction, the research paper must resonate with policymakers and institution leaders. But it must also resonate with faculty and academic managers, for the discussion in this chapter has affirmed that reform will be muted unless faculty change. Importantly, it is likely that the research paper will need to create and speak to a new audience. Looking broadly across various recent initiatives, serious assessment-related work on learning outcomes has been conducted by government officials, university academics, or researchers working in not-for-profit or commercial firms. Such hybrid arrangements are inevitable in the early days of technological adoption, but in synch with the development of the field it is necessary to produce a new kind of higher education assessment expertise and workforce.

With relevant infrastructure in place it would be feasible to review the primary and support activities with reference to the likelihood of working through each of the obstacles sketched above, and for each activity to estimate the costs and benefits for quality and productivity. Improvement resources could then be channelled in the most effective ways—nominally into reforming those activities where change looks feasible, and is likely to yield greater quality or productivity returns. The context and focus of the review would of course shape the recommendations made, and while these would be highly specific, a suite of case studies and collaborative supports could help streamline designs and plans for change. Building this modernisation program, however, is a substantial undertaking in itself, but given its potential to advance assessment, hence higher education, appears to be a worthwhile investment to make.

Australian Learning and Teaching Council (ALTC). (2010). Learning and teaching academic standards project final report . Sydney: Australian Learning and Teaching Council.

Google Scholar  

Canny, B., & Coates, H. (2014). Governance Models for Collaborations Involving Assessment . Sydney: Office for Learning and Teaching.

Chakroun, B. (2010). National qualification frameworks: From policy borrowing to policy learning. European Journal of Education, 45 (2), 199–216.

Article   Google Scholar  

Christensen, C., & Eyring, H. J. (2011). The innovative university: Changing the DNA of higher education from the inside out . New York: Wiley.

Coates, H. (2010). Defining and monitoring academic standards in Australian higher education. Higher Education Management and Policy, 22 (1), 1–17.

Coates, H. (2014). Higher education learning outcomes assessment: International perspectives . Frankfurt: Peter Lang.

Coates, H., & Goedegebuure, L. (2012). Recasting the academic workforce: Why the attractiveness of the academic profession needs to be increased and eight possible strategies for how to go about this from an Australian perspective. Higher Education, 64 (6), 875–889.

Coates, H., & Lennon, M. C. (2014). Propelling the field: Insights, trends and prospects. In H. Coates (Ed.), Higher education learning outcomes assessment: International perspectives . Frankfurt: Peter Lang.

Coates, H., & Mahat, M. (2013). Assessing student engagement and outcomes: Modelling insights from Australia and around the world. International Journal of Chinese Education, 2 (2), 241–264.

Coates, H., & Mahat, M. (2014). Threshold quality parameters in hybrid higher education. Higher Education, 68 (4), 577–590.

Coates, H. & Mahat, M. (forthcoming). Clarifying progress: Validation and application of the assessment transparency model (ATM) .

Coates, H., & McCormick, A. C. (2014). Engaging university students: International insights from system-wide studies . Dordrect: Springer.

Book   Google Scholar  

Coates, H., & Richardson, S. (2012). An international assessment of bachelor degree graduate’s learning outcomes. Higher Education Management and Policy, 23 (3), 51–69.

Dill, D. D. (2014). Ensuring academic standards in US higher education. Change: The Magazine of Higher Learning, 46 (3), 53–59.

Department of Education (DoE). (2014). The College Scorecard: College Affordability and Transparency Centre . Retrieved from: http://www.whitehouse.gov/issues/education/higher-education/college-score-card

Edwards, D., Wilkinson, D., Coates, H., & Canny, B. (2012). The Australian medical assessment collaboration: Developing the foundations for a national assessment of medical student learning outcomes . Sydney: Office of Learning and Teaching.

Educational Testing Service (ETS). (2014). Proficiency profile. Retrieved from: www.ets.org/proficiencyprofile/about

Federkeil, G., Van Vught, F., & Westerheijden, D. (2012). Classifications and rankings. In F. Van Vught & F. Ziegele (Eds.), Multidimensional ranking: The design and development of U-multirank . Dordrecht: Springer.

González, J., & Wagenaar, R. (2008). Universities’ contribution to the Bologna process: An introduction . Bilbao: Universidad de Deusto.

Krzykowski, L., & Kinser, K. (2014). Transparency in student learning assessment: Can accreditation standards make a difference? Change: The Magazine of Higher Learning, 46 (3), 67–73.

Lennon, M. C., Frank, B., Humphreys, J., Lenton, R., Madsen, K., Omri, A., & Turner, R. (2014). Tuning: Identifying and measuring sector-based learning outcomes in postsecondary education . Toronto: Higher Education Quality Council of Ontario.

Marshall, S., Henry, R., & Ramburuth, P. (2013). Improving assessment in higher education: A whole of institution approach . Sydney: New South Books.

McBride, V. & Keevy, J. (2010). Is the national qualifications framework a broken promise? A dialogue. Journal of Educational Change , 11 , 193–203.

Medical Schools Council (MSC). (2014). Medical schools council assessment alliance. Retreived from: http://www.medschools.ac.uk/MSCAA/Pages/default.aspx

Nicol, D. (2014). Re-engineering assessment practices in higher education . Retrieved from: www.reap.ac.uk

O’Neill, G. & Noonan, E. (2011). First year assessment redesign—a program approach . Retrieved from: www.ucd.ie/teaching/resources/assessment/firstyearassessment

Pearson. (2014). MyEconLab . Retrieved from: www.pearsonmylabandmastering.com/northamerica/myeconlab

Porter, M. (1985). Competitive advantage: Creating and sustaining superior performance . New York: Simon and Schuster.

Quality Assurance Agency (QAA). (2014). The UK quality code for higher education. Retreived from: www.qaa.ac.uk .

RAND. (2014). Learning gain in higher education . Cambridge: RAND Europe.

Rhodes, T., & Finley, A. (2013). Using the VALUE rubrics for improvement of learning and authentic assessment . Washington: AAC&U.

Schuwirth, L. W. T., & Van De Vleuten, C. P. M. (2012). The use of progress testing. Perspect Med Educ, 1 (1), 24–30.

Shavelson, R. (2007). A brief history of student learning assessment: How we got where we are and a proposal for where to go next . Washington: Association of American Colleges and Universities.

Sullivan, T. A., Mackie, C., Massy, W. F., & Sinha, E. (2012). Improving measurement of productivity in higher education . Washington: National Academics Press.

Twigg, C.A. (2003). Improving learning and reducing costs: New models for online learning. EDUCAUSE Review(September/October) , 28–38.

Van Vught, F. (2012). Diversity, transparency and institutional profiling in higher education . Paper presented at the LH Martin Institute Symposium on Strategic Differentiation and Sameness in Australian Higher Education: Institutional Profiling and Transparency, Melbourne.

Zlatkin-Troitschanskaia, O., Kuhn, C., & Toepper, M. (2014). Modelling and assessing higher education learning outcomes in Germany. In H. Coates (Ed.), Advancing higher education learning outcomes . Frankfurt: Peter Lang Publishers.

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Coates, H. (2015). Assessment of Learning Outcomes. In: Curaj, A., Matei, L., Pricopie, R., Salmi, J., Scott, P. (eds) The European Higher Education Area. Springer, Cham. https://doi.org/10.1007/978-3-319-20877-0_26

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SYSTEMATIC REVIEW article

A critical review of research on student self-assessment.

• Educational Psychology and Methodology, University at Albany, Albany, NY, United States

This article is a review of research on student self-assessment conducted largely between 2013 and 2018. The purpose of the review is to provide an updated overview of theory and research. The treatment of theory involves articulating a refined definition and operationalization of self-assessment. The review of 76 empirical studies offers a critical perspective on what has been investigated, including the relationship between self-assessment and achievement, consistency of self-assessment and others' assessments, student perceptions of self-assessment, and the association between self-assessment and self-regulated learning. An argument is made for less research on consistency and summative self-assessment, and more on the cognitive and affective mechanisms of formative self-assessment.

This review of research on student self-assessment expands on a review published as a chapter in the Cambridge Handbook of Instructional Feedback ( Andrade, 2018 , reprinted with permission). The timespan for the original review was January 2013 to October 2016. A lot of research has been done on the subject since then, including at least two meta-analyses; hence this expanded review, in which I provide an updated overview of theory and research. The treatment of theory presented here involves articulating a refined definition and operationalization of self-assessment through a lens of feedback. My review of the growing body of empirical research offers a critical perspective, in the interest of provoking new investigations into neglected areas.

Defining and Operationalizing Student Self-Assessment

Without exception, reviews of self-assessment ( Sargeant, 2008 ; Brown and Harris, 2013 ; Panadero et al., 2016a ) call for clearer definitions: What is self-assessment, and what is not? This question is surprisingly difficult to answer, as the term self-assessment has been used to describe a diverse range of activities, such as assigning a happy or sad face to a story just told, estimating the number of correct answers on a math test, graphing scores for dart throwing, indicating understanding (or the lack thereof) of a science concept, using a rubric to identify strengths and weaknesses in one's persuasive essay, writing reflective journal entries, and so on. Each of those activities involves some kind of assessment of one's own functioning, but they are so different that distinctions among types of self-assessment are needed. I will draw those distinctions in terms of the purposes of self-assessment which, in turn, determine its features: a classic form-fits-function analysis.

What is Self-Assessment?

Brown and Harris (2013) defined self-assessment in the K-16 context as a “descriptive and evaluative act carried out by the student concerning his or her own work and academic abilities” (p. 368). Panadero et al. (2016a) defined it as a “wide variety of mechanisms and techniques through which students describe (i.e., assess) and possibly assign merit or worth to (i.e., evaluate) the qualities of their own learning processes and products” (p. 804). Referring to physicians, Epstein et al. (2008) defined “concurrent self-assessment” as “ongoing moment-to-moment self-monitoring” (p. 5). Self-monitoring “refers to the ability to notice our own actions, curiosity to examine the effects of those actions, and willingness to use those observations to improve behavior and thinking in the future” (p. 5). Taken together, these definitions include self-assessment of one's abilities, processes , and products —everything but the kitchen sink. This very broad conception might seem unwieldy, but it works because each object of assessment—competence, process, and product—is subject to the influence of feedback from oneself.

What is missing from each of these definitions, however, is the purpose of the act of self-assessment. Their authors might rightly point out that the purpose is implied, but a formal definition requires us to make it plain: Why do we ask students to self-assess? I have long held that self-assessment is feedback ( Andrade, 2010 ), and that the purpose of feedback is to inform adjustments to processes and products that deepen learning and enhance performance; hence the purpose of self-assessment is to generate feedback that promotes learning and improvements in performance. This learning-oriented purpose of self-assessment implies that it should be formative: if there is no opportunity for adjustment and correction, self-assessment is almost pointless.

Why Self-Assess?

Clarity about the purpose of self-assessment allows us to interpret what otherwise appear to be discordant findings from research, which has produced mixed results in terms of both the accuracy of students' self-assessments and their influence on learning and/or performance. I believe the source of the discord can be traced to the different ways in which self-assessment is carried out, such as whether it is summative and formative. This issue will be taken up again in the review of current research that follows this overview. For now, consider a study of the accuracy and validity of summative self-assessment in teacher education conducted by Tejeiro et al. (2012) , which showed that students' self-assigned marks tended to be higher than marks given by professors. All 122 students in the study assigned themselves a grade at the end of their course, but half of the students were told that their self-assigned grade would count toward 5% of their final grade. In both groups, students' self-assessments were higher than grades given by professors, especially for students with “poorer results” (p. 791) and those for whom self-assessment counted toward the final grade. In the group that was told their self-assessments would count toward their final grade, no relationship was found between the professor's and the students' assessments. Tejeiro et al. concluded that, although students' and professor's assessments tend to be highly similar when self-assessment did not count toward final grades, overestimations increased dramatically when students' self-assessments did count. Interviews of students who self-assigned highly discrepant grades revealed (as you might guess) that they were motivated by the desire to obtain the highest possible grades.

Studies like Tejeiro et al's. (2012) are interesting in terms of the information they provide about the relationship between consistency and honesty, but the purpose of the self-assessment, beyond addressing interesting research questions, is unclear. There is no feedback purpose. This is also true for another example of a study of summative self-assessment of competence, during which elementary-school children took the Test of Narrative Language and then were asked to self-evaluate “how you did in making up stories today” by pointing to one of five pictures, from a “very happy face” (rating of five) to a “very sad face” (rating of one) ( Kaderavek et al., 2004 . p. 37). The usual results were reported: Older children and good narrators were more accurate than younger children and poor narrators, and males tended to more frequently overestimate their ability.

Typical of clinical studies of accuracy in self-evaluation, this study rests on a definition and operationalization of self-assessment with no value in terms of instructional feedback. If those children were asked to rate their stories and then revise or, better yet, if they assessed their stories according to clear, developmentally appropriate criteria before revising, the valence of their self-assessments in terms of instructional feedback would skyrocket. I speculate that their accuracy would too. In contrast, studies of formative self-assessment suggest that when the act of self-assessing is given a learning-oriented purpose, students' self-assessments are relatively consistent with those of external evaluators, including professors ( Lopez and Kossack, 2007 ; Barney et al., 2012 ; Leach, 2012 ), teachers ( Bol et al., 2012 ; Chang et al., 2012 , 2013 ), researchers ( Panadero and Romero, 2014 ; Fitzpatrick and Schulz, 2016 ), and expert medical assessors ( Hawkins et al., 2012 ).

My commitment to keeping self-assessment formative is firm. However, Gavin Brown (personal communication, April 2011) reminded me that summative self-assessment exists and we cannot ignore it; any definition of self-assessment must acknowledge and distinguish between formative and summative forms of it. Thus, the taxonomy in Table 1 , which depicts self-assessment as serving formative and/or summative purposes, and focuses on competence, processes, and/or products.

Table 1 . A taxonomy of self-assessment.

Fortunately, a formative view of self-assessment seems to be taking hold in various educational contexts. For instance, Sargeant (2008) noted that all seven authors in a special issue of the Journal of Continuing Education in the Health Professions “conceptualize self-assessment within a formative, educational perspective, and see it as an activity that draws upon both external and internal data, standards, and resources to inform and make decisions about one's performance” (p. 1). Sargeant also stresses the point that self-assessment should be guided by evaluative criteria: “Multiple external sources can and should inform self-assessment, perhaps most important among them performance standards” (p. 1). Now we are talking about the how of self-assessment, which demands an operationalization of self-assessment practice. Let us examine each object of self-assessment (competence, processes, and/or products) with an eye for what is assessed and why.

What is Self-Assessed?

Monitoring and self-assessing processes are practically synonymous with self-regulated learning (SRL), or at least central components of it such as goal-setting and monitoring, or metacognition. Research on SRL has clearly shown that self-generated feedback on one's approach to learning is associated with academic gains ( Zimmerman and Schunk, 2011 ). Self-assessment of the products , such as papers and presentations, are the easiest to defend as feedback, especially when those self-assessments are grounded in explicit, relevant, evaluative criteria and followed by opportunities to relearn and/or revise ( Andrade, 2010 ).

Including the self-assessment of competence in this definition is a little trickier. I hesitated to include it because of the risk of sneaking in global assessments of one's overall ability, self-esteem, and self-concept (“I'm good enough, I'm smart enough, and doggone it, people like me,” Franken, 1992 ), which do not seem relevant to a discussion of feedback in the context of learning. Research on global self-assessment, or self-perception, is popular in the medical education literature, but even there, scholars have begun to question its usefulness in terms of influencing learning and professional growth (e.g., see Sargeant et al., 2008 ). Eva and Regehr (2008) seem to agree in the following passage, which states the case in a way that makes it worthy of a long quotation:

Self-assessment is often (implicitly or otherwise) conceptualized as a personal, unguided reflection on performance for the purposes of generating an individually derived summary of one's own level of knowledge, skill, and understanding in a particular area. For example, this conceptualization would appear to be the only reasonable basis for studies that fit into what Colliver et al. (2005) has described as the “guess your grade” model of self-assessment research, the results of which form the core foundation for the recurring conclusion that self-assessment is generally poor. This unguided, internally generated construction of self-assessment stands in stark contrast to the model put forward by Boud (1999) , who argued that the phrase self-assessment should not imply an isolated or individualistic activity; it should commonly involve peers, teachers, and other sources of information. The conceptualization of self-assessment as enunciated in Boud's description would appear to involve a process by which one takes personal responsibility for looking outward, explicitly seeking feedback, and information from external sources, then using these externally generated sources of assessment data to direct performance improvements. In this construction, self-assessment is more of a pedagogical strategy than an ability to judge for oneself; it is a habit that one needs to acquire and enact rather than an ability that one needs to master (p. 15).

As in the K-16 context, self-assessment is coming to be seen as having value as much or more so in terms of pedagogy as in assessment ( Silver et al., 2008 ; Brown and Harris, 2014 ). In the end, however, I decided that self-assessing one's competence to successfully learn a particular concept or complete a particular task (which sounds a lot like self-efficacy—more on that later) might be useful feedback because it can inform decisions about how to proceed, such as the amount of time to invest in learning how to play the flute, or whether or not to seek help learning the steps of the jitterbug. An important caveat, however, is that self-assessments of competence are only useful if students have opportunities to do something about their perceived low competence—that is, it serves the purpose of formative feedback for the learner.

How to Self-Assess?

Panadero et al. (2016a) summarized five very different taxonomies of self-assessment and called for the development of a comprehensive typology that considers, among other things, its purpose, the presence or absence of criteria, and the method. In response, I propose the taxonomy depicted in Table 1 , which focuses on the what (competence, process, or product), the why (formative or summative), and the how (methods, including whether or not they include standards, e.g., criteria) of self-assessment. The collections of examples of methods in the table is inexhaustive.

I put the methods in Table 1 where I think they belong, but many of them could be placed in more than one cell. Take self-efficacy , for instance, which is essentially a self-assessment of one's competence to successfully undertake a particular task ( Bandura, 1997 ). Summative judgments of self-efficacy are certainly possible but they seem like a silly thing to do—what is the point, from a learning perspective? Formative self-efficacy judgments, on the other hand, can inform next steps in learning and skill building. There is reason to believe that monitoring and making adjustments to one's self-efficacy (e.g., by setting goals or attributing success to effort) can be productive ( Zimmerman, 2000 ), so I placed self-efficacy in the formative row.

It is important to emphasize that self-efficacy is task-specific, more or less ( Bandura, 1997 ). This taxonomy does not include general, holistic evaluations of one's abilities, for example, “I am good at math.” Global assessment of competence does not provide the leverage, in terms of feedback, that is provided by task-specific assessments of competence, that is, self-efficacy. Eva and Regehr (2008) provided an illustrative example: “We suspect most people are prompted to open a dictionary as a result of encountering a word for which they are uncertain of the meaning rather than out of a broader assessment that their vocabulary could be improved” (p. 16). The exclusion of global evaluations of oneself resonates with research that clearly shows that feedback that focuses on aspects of a task (e.g., “I did not solve most of the algebra problems”) is more effective than feedback that focuses on the self (e.g., “I am bad at math”) ( Kluger and DeNisi, 1996 ; Dweck, 2006 ; Hattie and Timperley, 2007 ). Hence, global self-evaluations of ability or competence do not appear in Table 1 .

Another approach to student self-assessment that could be placed in more than one cell is traffic lights . The term traffic lights refers to asking students to use green, yellow, or red objects (or thumbs up, sideways, or down—anything will do) to indicate whether they think they have good, partial, or little understanding ( Black et al., 2003 ). It would be appropriate for traffic lights to appear in multiple places in Table 1 , depending on how they are used. Traffic lights seem to be most effective at supporting students' reflections on how well they understand a concept or have mastered a skill, which is line with their creators' original intent, so they are categorized as formative self-assessments of one's learning—which sounds like metacognition.

In fact, several of the methods included in Table 1 come from research on metacognition, including self-monitoring , such as checking one's reading comprehension, and self-testing , e.g., checking one's performance on test items. These last two methods have been excluded from some taxonomies of self-assessment (e.g., Boud and Brew, 1995 ) because they do not engage students in explicitly considering relevant standards or criteria. However, new conceptions of self-assessment are grounded in theories of the self- and co-regulation of learning ( Andrade and Brookhart, 2016 ), which includes self-monitoring of learning processes with and without explicit standards.

However, my research favors self-assessment with regard to standards ( Andrade and Boulay, 2003 ; Andrade and Du, 2007 ; Andrade et al., 2008 , 2009 , 2010 ), as does related research by Panadero and his colleagues (see below). I have involved students in self-assessment of stories, essays, or mathematical word problems according to rubrics or checklists with criteria. For example, two studies investigated the relationship between elementary or middle school students' scores on a written assignment and a process that involved them in reading a model paper, co-creating criteria, self-assessing first drafts with a rubric, and revising ( Andrade et al., 2008 , 2010 ). The self-assessment was highly scaffolded: students were asked to underline key phrases in the rubric with colored pencils (e.g., underline “clearly states an opinion” in blue), then underline or circle in their drafts the evidence of having met the standard articulated by the phrase (e.g., his or her opinion) with the same blue pencil. If students found they had not met the standard, they were asked to write themselves a reminder to make improvements when they wrote their final drafts. This process was followed for each criterion on the rubric. There were main effects on scores for every self-assessed criterion on the rubric, suggesting that guided self-assessment according to the co-created criteria helped students produce more effective writing.

Panadero and his colleagues have also done quasi-experimental and experimental research on standards-referenced self-assessment, using rubrics or lists of assessment criteria that are presented in the form of questions ( Panadero et al., 2012 , 2013 , 2014 ; Panadero and Romero, 2014 ). Panadero calls the list of assessment criteria a script because his work is grounded in research on scaffolding (e.g., Kollar et al., 2006 ): I call it a checklist because that is the term used in classroom assessment contexts. Either way, the list provides standards for the task. Here is a script for a written summary that Panadero et al. (2014) used with college students in a psychology class:

• Does my summary transmit the main idea from the text? Is it at the beginning of my summary?

• Are the important ideas also in my summary?

• Have I selected the main ideas from the text to make them explicit in my summary?

• Have I thought about my purpose for the summary? What is my goal?

Taken together, the results of the studies cited above suggest that students who engaged in self-assessment using scripts or rubrics were more self-regulated, as measured by self-report questionnaires and/or think aloud protocols, than were students in the comparison or control groups. Effect sizes were very small to moderate (η 2 = 0.06–0.42), and statistically significant. Most interesting, perhaps, is one study ( Panadero and Romero, 2014 ) that demonstrated an association between rubric-referenced self-assessment activities and all three phases of SRL; forethought, performance, and reflection.

There are surely many other methods of self-assessment to include in Table 1 , as well as interesting conversations to be had about which method goes where and why. In the meantime, I offer the taxonomy in Table 1 as a way to define and operationalize self-assessment in instructional contexts and as a framework for the following overview of current research on the subject.

An Overview of Current Research on Self-Assessment

Several recent reviews of self-assessment are available ( Brown and Harris, 2013 ; Brown et al., 2015 ; Panadero et al., 2017 ), so I will not summarize the entire body of research here. Instead, I chose to take a birds-eye view of the field, with goal of reporting on what has been sufficiently researched and what remains to be done. I used the references lists from reviews, as well as other relevant sources, as a starting point. In order to update the list of sources, I directed two new searches 1 , the first of the ERIC database, and the second of both ERIC and PsychINFO. Both searches included two search terms, “self-assessment” OR “self-evaluation.” Advanced search options had four delimiters: (1) peer-reviewed, (2) January, 2013–October, 2016 and then October 2016–March 2019, (3) English, and (4) full-text. Because the focus was on K-20 educational contexts, sources were excluded if they were about early childhood education or professional development.

The first search yielded 347 hits; the second 1,163. Research that was unrelated to instructional feedback was excluded, such as studies limited to self-estimates of performance before or after taking a test, guesses about whether a test item was answered correctly, and estimates of how many tasks could be completed in a certain amount of time. Although some of the excluded studies might be thought of as useful investigations of self-monitoring, as a group they seemed too unrelated to theories of self-generated feedback to be appropriate for this review. Seventy-six studies were selected for inclusion in Table S1 (Supplementary Material), which also contains a few studies published before 2013 that were not included in key reviews, as well as studies solicited directly from authors.

The Table S1 in the Supplementary Material contains a complete list of studies included in this review, organized by the focus or topic of the study, as well as brief descriptions of each. The “type” column Table S1 (Supplementary Material) indicates whether the study focused on formative or summative self-assessment. This distinction was often difficult to make due to a lack of information. For example, Memis and Seven (2015) frame their study in terms of formative assessment, and note that the purpose of the self-evaluation done by the sixth grade students is to “help students improve their [science] reports” (p. 39), but they do not indicate how the self-assessments were done, nor whether students were given time to revise their reports based on their judgments or supported in making revisions. A sentence or two of explanation about the process of self-assessment in the procedures sections of published studies would be most useful.

Figure 1 graphically represents the number of studies in the four most common topic categories found in the table—achievement, consistency, student perceptions, and SRL. The figure reveals that research on self-assessment is on the rise, with consistency the most popular topic. Of the 76 studies in the table in the appendix, 44 were inquiries into the consistency of students' self-assessments with other judgments (e.g., a test score or teacher's grade). Twenty-five studies investigated the relationship between self-assessment and achievement. Fifteen explored students' perceptions of self-assessment. Twelve studies focused on the association between self-assessment and self-regulated learning. One examined self-efficacy, and two qualitative studies documented the mental processes involved in self-assessment. The sum ( n = 99) of the list of research topics is more than 76 because several studies had multiple foci. In the remainder of this review I examine each topic in turn.

Figure 1 . Topics of self-assessment studies, 2013–2018.

Consistency

Table S1 (Supplementary Material) reveals that much of the recent research on self-assessment has investigated the accuracy or, more accurately, consistency, of students' self-assessments. The term consistency is more appropriate in the classroom context because the quality of students' self-assessments is often determined by comparing them with their teachers' assessments and then generating correlations. Given the evidence of the unreliability of teachers' grades ( Falchikov, 2005 ), the assumption that teachers' assessments are accurate might not be well-founded ( Leach, 2012 ; Brown et al., 2015 ). Ratings of student work done by researchers are also suspect, unless evidence of the validity and reliability of the inferences made about student work by researchers is available. Consequently, much of the research on classroom-based self-assessment should use the term consistency , which refers to the degree of alignment between students' and expert raters' evaluations, avoiding the purer, more rigorous term accuracy unless it is fitting.

In their review, Brown and Harris (2013) reported that correlations between student self-ratings and other measures tended to be weakly to strongly positive, ranging from r ≈ 0.20 to 0.80, with few studies reporting correlations >0.60. But their review included results from studies of any self-appraisal of school work, including summative self-rating/grading, predictions about the correctness of answers on test items, and formative, criteria-based self-assessments, a combination of methods that makes the correlations they reported difficult to interpret. Qualitatively different forms of self-assessment, especially summative and formative types, cannot be lumped together without obfuscating important aspects of self-assessment as feedback.

Given my concern about combining studies of summative and formative assessment, you might anticipate a call for research on consistency that distinguishes between the two. I will make no such call for three reasons. One is that we have enough research on the subject, including the 22 studies in Table S1 (Supplementary Material) that were published after Brown and Harris's review (2013 ). Drawing only on studies included in Table S1 (Supplementary Material), we can say with confidence that summative self-assessment tends to be inconsistent with external judgements ( Baxter and Norman, 2011 ; De Grez et al., 2012 ; Admiraal et al., 2015 ), with males tending to overrate and females to underrate ( Nowell and Alston, 2007 ; Marks et al., 2018 ). There are exceptions ( Alaoutinen, 2012 ; Lopez-Pastor et al., 2012 ) as well as mixed results, with students being consistent regarding some aspects of their learning but not others ( Blanch-Hartigan, 2011 ; Harding and Hbaci, 2015 ; Nguyen and Foster, 2018 ). We can also say that older, more academically competent learners tend to be more consistent ( Hacker et al., 2000 ; Lew et al., 2010 ; Alaoutinen, 2012 ; Guillory and Blankson, 2017 ; Butler, 2018 ; Nagel and Lindsey, 2018 ). There is evidence that consistency can be improved through experience ( Lopez and Kossack, 2007 ; Yilmaz, 2017 ; Nagel and Lindsey, 2018 ), the use of guidelines ( Bol et al., 2012 ), feedback ( Thawabieh, 2017 ), and standards ( Baars et al., 2014 ), perhaps in the form of rubrics ( Panadero and Romero, 2014 ). Modeling and feedback also help ( Labuhn et al., 2010 ; Miller and Geraci, 2011 ; Hawkins et al., 2012 ; Kostons et al., 2012 ).

An outcome typical of research on the consistency of summative self-assessment can be found in row 59, which summarizes the study by Tejeiro et al. (2012) discussed earlier: Students' self-assessments were higher than marks given by professors, especially for students with poorer results, and no relationship was found between the professors' and the students' assessments in the group in which self-assessment counted toward the final mark. Students are not stupid: if they know that they can influence their final grade, and that their judgment is summative rather than intended to inform revision and improvement, they will be motivated to inflate their self-evaluation. I do not believe we need more research to demonstrate that phenomenon.

The second reason I am not calling for additional research on consistency is a lot of it seems somewhat irrelevant. This might be because the interest in accuracy is rooted in clinical research on calibration, which has very different aims. Calibration accuracy is the “magnitude of consent between learners' true and self-evaluated task performance. Accurately calibrated learners' task performance equals their self-evaluated task performance” ( Wollenschläger et al., 2016 ). Calibration research often asks study participants to predict or postdict the correctness of their responses to test items. I caution about generalizing from clinical experiments to authentic classroom contexts because the dismal picture of our human potential to self-judge was painted by calibration researchers before study participants were effectively taught how to predict with accuracy, or provided with the tools they needed to be accurate, or motivated to do so. Calibration researchers know that, of course, and have conducted intervention studies that attempt to improve accuracy, with some success (e.g., Bol et al., 2012 ). Studies of formative self-assessment also suggest that consistency increases when it is taught and supported in many of the ways any other skill must be taught and supported ( Lopez and Kossack, 2007 ; Labuhn et al., 2010 ; Chang et al., 2012 , 2013 ; Hawkins et al., 2012 ; Panadero and Romero, 2014 ; Lin-Siegler et al., 2015 ; Fitzpatrick and Schulz, 2016 ).

Even clinical psychological studies that go beyond calibration to examine the associations between monitoring accuracy and subsequent study behaviors do not transfer well to classroom assessment research. After repeatedly encountering claims that, for example, low self-assessment accuracy leads to poor task-selection accuracy and “suboptimal learning outcomes” ( Raaijmakers et al., 2019 , p. 1), I dug into the cited studies and discovered two limitations. The first is that the tasks in which study participants engage are quite inauthentic. A typical task involves studying “word pairs (e.g., railroad—mother), followed by a delayed judgment of learning (JOL) in which the students predicted the chances of remembering the pair… After making a JOL, the entire pair was presented for restudy for 4 s [ sic ], and after all pairs had been restudied, a criterion test of paired-associate recall occurred” ( Dunlosky and Rawson, 2012 , p. 272). Although memory for word pairs might be important in some classroom contexts, it is not safe to assume that results from studies like that one can predict students' behaviors after criterion-referenced self-assessment of their comprehension of complex texts, lengthy compositions, or solutions to multi-step mathematical problems.

The second limitation of studies like the typical one described above is more serious: Participants in research like that are not permitted to regulate their own studying, which is experimentally manipulated by a computer program. This came as a surprise, since many of the claims were about students' poor study choices but they were rarely allowed to make actual choices. For example, Dunlosky and Rawson (2012) permitted participants to “use monitoring to effectively control learning” by programming the computer so that “a participant would need to have judged his or her recall of a definition entirely correct on three different trials, and once they judged it entirely correct on the third trial, that particular key term definition was dropped [by the computer program] from further practice” (p. 272). The authors note that this study design is an improvement on designs that did not require all participants to use the same regulation algorithm, but it does not reflect the kinds of decisions that learners make in class or while doing homework. In fact, a large body of research shows that students can make wise choices when they self-pace the study of to-be-learned materials and then allocate study time to each item ( Bjork et al., 2013 , p. 425):

In a typical experiment, the students first study all the items at an experimenter-paced rate (e.g., study 60 paired associates for 3 s each), which familiarizes the students with the items; after this familiarity phase, the students then either choose which items they want to restudy (e.g., all items are presented in an array, and the students select which ones to restudy) and/or pace their restudy of each item. Several dependent measures have been widely used, such as how long each item is studied, whether an item is selected for restudy, and in what order items are selected for restudy. The literature on these aspects of self-regulated study is massive (for a comprehensive overview, see both Dunlosky and Ariel, 2011 and Son and Metcalfe, 2000 ), but the evidence is largely consistent with a few basic conclusions. First, if students have a chance to practice retrieval prior to restudying items, they almost exclusively choose to restudy unrecalled items and drop the previously recalled items from restudy ( Metcalfe and Kornell, 2005 ). Second, when pacing their study of individual items that have been selected for restudy, students typically spend more time studying items that are more, rather than less, difficult to learn. Such a strategy is consistent with a discrepancy-reduction model of self-paced study (which states that people continue to study an item until they reach mastery), although some key revisions to this model are needed to account for all the data. For instance, students may not continue to study until they reach some static criterion of mastery, but instead, they may continue to study until they perceive that they are no longer making progress.

I propose that this research, which suggests that students' unscaffolded, unmeasured, informal self-assessments tend to lead to appropriate task selection, is better aligned with research on classroom-based self-assessment. Nonetheless, even this comparison is inadequate because the study participants were not taught to compare their performance to the criteria for mastery, as is often done in classroom-based self-assessment.

The third and final reason I do not believe we need additional research on consistency is that I think it is a distraction from the true purposes of self-assessment. Many if not most of the articles about the accuracy of self-assessment are grounded in the assumption that accuracy is necessary for self-assessment to be useful, particularly in terms of subsequent studying and revision behaviors. Although it seems obvious that accurate evaluations of their performance positively influence students' study strategy selection, which should produce improvements in achievement, I have not seen relevant research that tests those conjectures. Some claim that inaccurate estimates of learning lead to the selection of inappropriate learning tasks ( Kostons et al., 2012 ) but they cite research that does not support their claim. For example, Kostons et al. cite studies that focus on the effectiveness of SRL interventions but do not address the accuracy of participants' estimates of learning, nor the relationship of those estimates to the selection of next steps. Other studies produce findings that support my skepticism. Take, for instance, two relevant studies of calibration. One suggested that performance and judgments of performance had little influence on subsequent test preparation behavior ( Hacker et al., 2000 ), and the other showed that study participants followed their predictions of performance to the same degree, regardless of monitoring accuracy ( van Loon et al., 2014 ).

Eva and Regehr (2008) believe that:

Research questions that take the form of “How well do various practitioners self-assess?” “How can we improve self-assessment?” or “How can we measure self-assessment skill?” should be considered defunct and removed from the research agenda [because] there have been hundreds of studies into these questions and the answers are “Poorly,” “You can't,” and “Don't bother” (p. 18).

I almost agree. A study that could change my mind about the importance of accuracy of self-assessment would be an investigation that goes beyond attempting to improve accuracy just for the sake of accuracy by instead examining the relearning/revision behaviors of accurate and inaccurate self-assessors: Do students whose self-assessments match the valid and reliable judgments of expert raters (hence my use of the term accuracy ) make better decisions about what they need to do to deepen their learning and improve their work? Here, I admit, is a call for research related to consistency: I would love to see a high-quality investigation of the relationship between accuracy in formative self-assessment, and students' subsequent study and revision behaviors, and their learning. For example, a study that closely examines the revisions to writing made by accurate and inaccurate self-assessors, and the resulting outcomes in terms of the quality of their writing, would be most welcome.

Table S1 (Supplementary Material) indicates that by 2018 researchers began publishing studies that more directly address the hypothesized link between self-assessment and subsequent learning behaviors, as well as important questions about the processes learners engage in while self-assessing ( Yan and Brown, 2017 ). One, a study by Nugteren et al. (2018 row 19 in Table S1 (Supplementary Material)), asked “How do inaccurate [summative] self-assessments influence task selections?” (p. 368) and employed a clever exploratory research design. The results suggested that most of the 15 students in their sample over-estimated their performance and made inaccurate learning-task selections. Nugteren et al. recommended helping students make more accurate self-assessments, but I think the more interesting finding is related to why students made task selections that were too difficult or too easy, given their prior performance: They based most task selections on interest in the content of particular items (not the overarching content to be learned), and infrequently considered task difficulty and support level. For instance, while working on the genetics tasks, students reported selecting tasks because they were fun or interesting, not because they addressed self-identified weaknesses in their understanding of genetics. Nugteren et al. proposed that students would benefit from instruction on task selection. I second that proposal: Rather than directing our efforts on accuracy in the service of improving subsequent task selection, let us simply teach students to use the information at hand to select next best steps, among other things.

Butler (2018 , row 76 in Table S1 (Supplementary Material)) has conducted at least two studies of learners' processes of responding to self-assessment items and how they arrived at their judgments. Comparing generic, decontextualized items to task-specific, contextualized items (which she calls after-task items ), she drew two unsurprising conclusions: the task-specific items “generally showed higher correlations with task performance,” and older students “appeared to be more conservative in their judgment compared with their younger counterparts” (p. 249). The contribution of the study is the detailed information it provides about how students generated their judgments. For example, Butler's qualitative data analyses revealed that when asked to self-assess in terms of vague or non-specific items, the children often “contextualized the descriptions based on their own experiences, goals, and expectations,” (p. 257) focused on the task at hand, and situated items in the specific task context. Perhaps as a result, the correlation between after-task self-assessment and task performance was generally higher than for generic self-assessment.

Butler (2018) notes that her study enriches our empirical understanding of the processes by which children respond to self-assessment. This is a very promising direction for the field. Similar studies of processing during formative self-assessment of a variety of task types in a classroom context would likely produce significant advances in our understanding of how and why self-assessment influences learning and performance.

Student Perceptions

Fifteen of the studies listed in Table S1 (Supplementary Material) focused on students' perceptions of self-assessment. The studies of children suggest that they tend to have unsophisticated understandings of its purposes ( Harris and Brown, 2013 ; Bourke, 2016 ) that might lead to shallow implementation of related processes. In contrast, results from the studies conducted in higher education settings suggested that college and university students understood the function of self-assessment ( Ratminingsih et al., 2018 ) and generally found it to be useful for guiding evaluation and revision ( Micán and Medina, 2017 ), understanding how to take responsibility for learning ( Lopez and Kossack, 2007 ; Bourke, 2014 ; Ndoye, 2017 ), prompting them to think more critically and deeply ( van Helvoort, 2012 ; Siow, 2015 ), applying newfound skills ( Murakami et al., 2012 ), and fostering self-regulated learning by guiding them to set goals, plan, self-monitor and reflect ( Wang, 2017 ).

Not surprisingly, positive perceptions of self-assessment were typically developed by students who actively engaged the formative type by, for example, developing their own criteria for an effective self-assessment response ( Bourke, 2014 ), or using a rubric or checklist to guide their assessments and then revising their work ( Huang and Gui, 2015 ; Wang, 2017 ). Earlier research suggested that children's attitudes toward self-assessment can become negative if it is summative ( Ross et al., 1998 ). However, even summative self-assessment was reported by adult learners to be useful in helping them become more critical of their own and others' writing throughout the course and in subsequent courses ( van Helvoort, 2012 ).

Achievement

Twenty-five of the studies in Table S1 (Supplementary Material) investigated the relation between self-assessment and achievement, including two meta-analyses. Twenty of the 25 clearly employed the formative type. Without exception, those 20 studies, plus the two meta-analyses ( Graham et al., 2015 ; Sanchez et al., 2017 ) demonstrated a positive association between self-assessment and learning. The meta-analysis conducted by Graham and his colleagues, which included 10 studies, yielded an average weighted effect size of 0.62 on writing quality. The Sanchez et al. meta-analysis revealed that, although 12 of the 44 effect sizes were negative, on average, “students who engaged in self-grading performed better ( g = 0.34) on subsequent tests than did students who did not” (p. 1,049).

All but two of the non-meta-analytic studies of achievement in Table S1 (Supplementary Material) were quasi-experimental or experimental, providing relatively rigorous evidence that their treatment groups outperformed their comparison or control groups in terms of everything from writing to dart-throwing, map-making, speaking English, and exams in a wide variety of disciplines. One experiment on summative self-assessment ( Miller and Geraci, 2011 ), in contrast, resulted in no improvements in exam scores, while the other one did ( Raaijmakers et al., 2017 ).

It would be easy to overgeneralize and claim that the question about the effect of self-assessment on learning has been answered, but there are unanswered questions about the key components of effective self-assessment, especially social-emotional components related to power and trust ( Andrade and Brown, 2016 ). The trends are pretty clear, however: it appears that formative forms of self-assessment can promote knowledge and skill development. This is not surprising, given that it involves many of the processes known to support learning, including practice, feedback, revision, and especially the intellectually demanding work of making complex, criteria-referenced judgments ( Panadero et al., 2014 ). Boud (1995a , b) predicted this trend when he noted that many self-assessment processes undermine learning by rushing to judgment, thereby failing to engage students with the standards or criteria for their work.

Self-Regulated Learning

The association between self-assessment and learning has also been explained in terms of self-regulation ( Andrade, 2010 ; Panadero and Alonso-Tapia, 2013 ; Andrade and Brookhart, 2016 , 2019 ; Panadero et al., 2016b ). Self-regulated learning (SRL) occurs when learners set goals and then monitor and manage their thoughts, feelings, and actions to reach those goals. SRL is moderately to highly correlated with achievement ( Zimmerman and Schunk, 2011 ). Research suggests that formative assessment is a potential influence on SRL ( Nicol and Macfarlane-Dick, 2006 ). The 12 studies in Table S1 (Supplementary Material) that focus on SRL demonstrate the recent increase in interest in the relationship between self-assessment and SRL.

Conceptual and practical overlaps between the two fields are abundant. In fact, Brown and Harris (2014) recommend that student self-assessment no longer be treated as an assessment, but as an essential competence for self-regulation. Butler and Winne (1995) introduced the role of self-generated feedback in self-regulation years ago:

[For] all self-regulated activities, feedback is an inherent catalyst. As learners monitor their engagement with tasks, internal feedback is generated by the monitoring process. That feedback describes the nature of outcomes and the qualities of the cognitive processes that led to those states (p. 245).

The outcomes and processes referred to by Butler and Winne are many of the same products and processes I referred to earlier in the definition of self-assessment and in Table 1 .

In general, research and practice related to self-assessment has tended to focus on judging the products of student learning, while scholarship on self-regulated learning encompasses both processes and products. The very practical focus of much of the research on self-assessment means it might be playing catch-up, in terms of theory development, with the SRL literature, which is grounded in experimental paradigms from cognitive psychology ( de Bruin and van Gog, 2012 ), while self-assessment research is ahead in terms of implementation (E. Panadero, personal communication, October 21, 2016). One major exception is the work done on Self-regulated Strategy Development ( Glaser and Brunstein, 2007 ; Harris et al., 2008 ), which has successfully integrated SRL research with classroom practices, including self-assessment, to teach writing to students with special needs.

Nicol and Macfarlane-Dick (2006) have been explicit about the potential for self-assessment practices to support self-regulated learning:

To develop systematically the learner's capacity for self-regulation, teachers need to create more structured opportunities for self-monitoring and the judging of progression to goals. Self-assessment tasks are an effective way of achieving this, as are activities that encourage reflection on learning progress (p. 207).

The studies of SRL in Table S1 (Supplementary Material) provide encouraging findings regarding the potential role of self-assessment in promoting achievement, self-regulated learning in general, and metacognition and study strategies related to task selection in particular. The studies also represent a solution to the “methodological and theoretical challenges involved in bringing metacognitive research to the real world, using meaningful learning materials” ( Koriat, 2012 , p. 296).

Future Directions for Research

I agree with ( Yan and Brown, 2017 ) statement that “from a pedagogical perspective, the benefits of self-assessment may come from active engagement in the learning process, rather than by being “veridical” or coinciding with reality, because students' reflection and metacognitive monitoring lead to improved learning” (p. 1,248). Future research should focus less on accuracy/consistency/veridicality, and more on the precise mechanisms of self-assessment ( Butler, 2018 ).

An important aspect of research on self-assessment that is not explicitly represented in Table S1 (Supplementary Material) is practice, or pedagogy: Under what conditions does self-assessment work best, and how are those conditions influenced by context? Fortunately, the studies listed in the table, as well as others (see especially Andrade and Valtcheva, 2009 ; Nielsen, 2014 ; Panadero et al., 2016a ), point toward an answer. But we still have questions about how best to scaffold effective formative self-assessment. One area of inquiry is about the characteristics of the task being assessed, and the standards or criteria used by learners during self-assessment.

Influence of Types of Tasks and Standards or Criteria

Type of task or competency assessed seems to matter (e.g., Dolosic, 2018 , Nguyen and Foster, 2018 ), as do the criteria ( Yilmaz, 2017 ), but we do not yet have a comprehensive understanding of how or why. There is some evidence that it is important that the criteria used to self-assess are concrete, task-specific ( Butler, 2018 ), and graduated. For example, Fastre et al. (2010) revealed an association between self-assessment according to task-specific criteria and task performance: In a quasi-experimental study of 39 novice vocational education students studying stoma care, they compared concrete, task-specific criteria (“performance-based criteria”) such as “Introduces herself to the patient” and “Consults the care file for details concerning the stoma” to vaguer, “competence-based criteria” such as “Shows interest, listens actively, shows empathy to the patient” and “Is discrete with sensitive topics.” The performance-based criteria group outperformed the competence-based group on tests of task performance, presumably because “performance-based criteria make it easier to distinguish levels of performance, enabling a step-by-step process of performance improvement” (p. 530).

This finding echoes the results of a study of self-regulated learning by Kitsantas and Zimmerman (2006) , who argued that “fine-grained standards can have two key benefits: They can enable learners to be more sensitive to small changes in skill and make more appropriate adaptations in learning strategies” (p. 203). In their study, 70 college students were taught how to throw darts at a target. The purpose of the study was to examine the role of graphing of self-recorded outcomes and self-evaluative standards in learning a motor skill. Students who were provided with graduated self-evaluative standards surpassed “those who were provided with absolute standards or no standards (control) in both motor skill and in motivational beliefs (i.e., self-efficacy, attributions, and self-satisfaction)” (p. 201). Kitsantas and Zimmerman hypothesized that setting high absolute standards would limit a learner's sensitivity to small improvements in functioning. This hypothesis was supported by the finding that students who set absolute standards reported significantly less awareness of learning progress (and hit the bull's-eye less often) than students who set graduated standards. “The correlation between the self-evaluation and dart-throwing outcomes measures was extraordinarily high ( r = 0.94)” (p. 210). Classroom-based research on specific, graduated self-assessment criteria would be informative.

Cognitive and Affective Mechanisms of Self-Assessment

There are many additional questions about pedagogy, such as the hoped-for investigation mentioned above of the relationship between accuracy in formative self-assessment, students' subsequent study behaviors, and their learning. There is also a need for research on how to help teachers give students a central role in their learning by creating space for self-assessment (e.g., see Hawe and Parr, 2014 ), and the complex power dynamics involved in doing so ( Tan, 2004 , 2009 ; Taras, 2008 ; Leach, 2012 ). However, there is an even more pressing need for investigations into the internal mechanisms experienced by students engaged in assessing their own learning. Angela Lui and I call this the next black box ( Lui, 2017 ).

Black and Wiliam (1998) used the term black box to emphasize the fact that what happened in most classrooms was largely unknown: all we knew was that some inputs (e.g., teachers, resources, standards, and requirements) were fed into the box, and that certain outputs (e.g., more knowledgeable and competent students, acceptable levels of achievement) would follow. But what, they asked, is happening inside, and what new inputs will produce better outputs? Black and Wiliam's review spawned a great deal of research on formative assessment, some but not all of which suggests a positive relationship with academic achievement ( Bennett, 2011 ; Kingston and Nash, 2011 ). To better understand why and how the use of formative assessment in general and self-assessment in particular is associated with improvements in academic achievement in some instances but not others, we need research that looks into the next black box: the cognitive and affective mechanisms of students who are engaged in assessment processes ( Lui, 2017 ).

The role of internal mechanisms has been discussed in theory but not yet fully tested. Crooks (1988) argued that the impact of assessment is influenced by students' interpretation of the tasks and results, and Butler and Winne (1995) theorized that both cognitive and affective processes play a role in determining how feedback is internalized and used to self-regulate learning. Other theoretical frameworks about the internal processes of receiving and responding to feedback have been developed (e.g., Nicol and Macfarlane-Dick, 2006 ; Draper, 2009 ; Andrade, 2013 ; Lipnevich et al., 2016 ). Yet, Shute (2008) noted in her review of the literature on formative feedback that “despite the plethora of research on the topic, the specific mechanisms relating feedback to learning are still mostly murky, with very few (if any) general conclusions” (p. 156). This area is ripe for research.

Self-assessment is the act of monitoring one's processes and products in order to make adjustments that deepen learning and enhance performance. Although it can be summative, the evidence presented in this review strongly suggests that self-assessment is most beneficial, in terms of both achievement and self-regulated learning, when it is used formatively and supported by training.

What is not yet clear is why and how self-assessment works. Those of you who like to investigate phenomena that are maddeningly difficult to measure will rejoice to hear that the cognitive and affective mechanisms of self-assessment are the next black box. Studies of the ways in which learners think and feel, the interactions between their thoughts and feelings and their context, and the implications for pedagogy will make major contributions to our field.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2019.00087/full#supplementary-material

1. ^ I am grateful to my graduate assistants, Joanna Weaver and Taja Young, for conducting the searches.

Admiraal, W., Huisman, B., and Pilli, O. (2015). Assessment in massive open online courses. Electron. J. e-Learning , 13, 207–216.

Google Scholar

Alaoutinen, S. (2012). Evaluating the effect of learning style and student background on self-assessment accuracy. Comput. Sci. Educ. 22, 175–198. doi: 10.1080/08993408.2012.692924

CrossRef Full Text | Google Scholar

Al-Rawahi, N. M., and Al-Balushi, S. M. (2015). The effect of reflective science journal writing on students' self-regulated learning strategies. Int. J. Environ. Sci. Educ. 10, 367–379. doi: 10.12973/ijese.2015.250a

Andrade, H. (2010). “Students as the definitive source of formative assessment: academic self-assessment and the self-regulation of learning,” in Handbook of Formative Assessment , eds H. Andrade and G. Cizek (New York, NY: Routledge, 90–105.

Andrade, H. (2013). “Classroom assessment in the context of learning theory and research,” in Sage Handbook of Research on Classroom Assessment , ed J. H. McMillan (New York, NY: Sage), 17–34. doi: 10.4135/9781452218649.n2

Andrade, H. (2018). “Feedback in the context of self-assessment,” in Cambridge Handbook of Instructional Feedback , eds A. Lipnevich and J. Smith (Cambridge: Cambridge University Press), 376–408.

PubMed Abstract

Andrade, H., and Boulay, B. (2003). The role of rubric-referenced self-assessment in learning to write. J. Educ. Res. 97, 21–34. doi: 10.1080/00220670309596625

Andrade, H., and Brookhart, S. (2019). Classroom assessment as the co-regulation of learning. Assessm. Educ. Principles Policy Pract. doi: 10.1080/0969594X.2019.1571992

Andrade, H., and Brookhart, S. M. (2016). “The role of classroom assessment in supporting self-regulated learning,” in Assessment for Learning: Meeting the Challenge of Implementation , eds D. Laveault and L. Allal (Heidelberg: Springer), 293–309. doi: 10.1007/978-3-319-39211-0_17

Andrade, H., and Du, Y. (2007). Student responses to criteria-referenced self-assessment. Assess. Evalu. High. Educ. 32, 159–181. doi: 10.1080/02602930600801928

Andrade, H., Du, Y., and Mycek, K. (2010). Rubric-referenced self-assessment and middle school students' writing. Assess. Educ. 17, 199–214. doi: 10.1080/09695941003696172

Andrade, H., Du, Y., and Wang, X. (2008). Putting rubrics to the test: The effect of a model, criteria generation, and rubric-referenced self-assessment on elementary school students' writing. Educ. Meas. 27, 3–13. doi: 10.1111/j.1745-3992.2008.00118.x

Andrade, H., and Valtcheva, A. (2009). Promoting learning and achievement through self- assessment. Theory Pract. 48, 12–19. doi: 10.1080/00405840802577544

Andrade, H., Wang, X., Du, Y., and Akawi, R. (2009). Rubric-referenced self-assessment and self-efficacy for writing. J. Educ. Res. 102, 287–302. doi: 10.3200/JOER.102.4.287-302

Andrade, H. L., and Brown, G. T. L. (2016). “Student self-assessment in the classroom,” in Handbook of Human and Social Conditions in Assessment , eds G. T. L. Brown and L. R. Harris (New York, NY: Routledge), 319–334.

PubMed Abstract | Google Scholar

Baars, M., Vink, S., van Gog, T., de Bruin, A., and Paas, F. (2014). Effects of training self-assessment and using assessment standards on retrospective and prospective monitoring of problem solving. Learn. Instruc. 33, 92–107. doi: 10.1016/j.learninstruc.2014.04.004

Balderas, I., and Cuamatzi, P. M. (2018). Self and peer correction to improve college students' writing skills. Profile. 20, 179–194. doi: 10.15446/profile.v20n2.67095

Bandura, A. (1997). Self-efficacy: The Exercise of Control . New York, NY: Freeman.

Barney, S., Khurum, M., Petersen, K., Unterkalmsteiner, M., and Jabangwe, R. (2012). Improving students with rubric-based self-assessment and oral feedback. IEEE Transac. Educ. 55, 319–325. doi: 10.1109/TE.2011.2172981

Baxter, P., and Norman, G. (2011). Self-assessment or self deception? A lack of association between nursing students' self-assessment and performance. J. Adv. Nurs. 67, 2406–2413. doi: 10.1111/j.1365-2648.2011.05658.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Bennett, R. E. (2011). Formative assessment: a critical review. Assess. Educ. 18, 5–25. doi: 10.1080/0969594X.2010.513678

Birjandi, P., and Hadidi Tamjid, N. (2012). The role of self-, peer and teacher assessment in promoting Iranian EFL learners' writing performance. Assess. Evalu. High. Educ. 37, 513–533. doi: 10.1080/02602938.2010.549204

Bjork, R. A., Dunlosky, J., and Kornell, N. (2013). Self-regulated learning: beliefs, techniques, and illusions. Annu. Rev. Psychol. 64, 417–444. doi: 10.1146/annurev-psych-113011-143823

Black, P., Harrison, C., Lee, C., Marshall, B., and Wiliam, D. (2003). Assessment for Learning: Putting it into Practice . Berkshire: Open University Press.

Black, P., and Wiliam, D. (1998). Inside the black box: raising standards through classroom assessment. Phi Delta Kappan 80, 139–144; 146–148.

Blanch-Hartigan, D. (2011). Medical students' self-assessment of performance: results from three meta-analyses. Patient Educ. Counsel. 84, 3–9. doi: 10.1016/j.pec.2010.06.037

Bol, L., Hacker, D. J., Walck, C. C., and Nunnery, J. A. (2012). The effects of individual or group guidelines on the calibration accuracy and achievement of high school biology students. Contemp. Educ. Psychol. 37, 280–287. doi: 10.1016/j.cedpsych.2012.02.004

Boud, D. (1995a). Implementing Student Self-Assessment, 2nd Edn. Australian Capital Territory: Higher Education Research and Development Society of Australasia.

Boud, D. (1995b). Enhancing Learning Through Self-Assessment. London: Kogan Page.

Boud, D. (1999). Avoiding the traps: Seeking good practice in the use of self-assessment and reflection in professional courses. Soc. Work Educ. 18, 121–132. doi: 10.1080/02615479911220131

Boud, D., and Brew, A. (1995). Developing a typology for learner self-assessment practices. Res. Dev. High. Educ. 18, 130–135.

Bourke, R. (2014). Self-assessment in professional programmes within tertiary institutions. Teach. High. Educ. 19, 908–918. doi: 10.1080/13562517.2014.934353

Bourke, R. (2016). Liberating the learner through self-assessment. Cambridge J. Educ. 46, 97–111. doi: 10.1080/0305764X.2015.1015963

Brown, G., Andrade, H., and Chen, F. (2015). Accuracy in student self-assessment: directions and cautions for research. Assess. Educ. 22, 444–457. doi: 10.1080/0969594X.2014.996523

Brown, G. T., and Harris, L. R. (2013). “Student self-assessment,” in Sage Handbook of Research on Classroom Assessment , ed J. H. McMillan (Los Angeles, CA: Sage), 367–393. doi: 10.4135/9781452218649.n21

Brown, G. T. L., and Harris, L. R. (2014). The future of self-assessment in classroom practice: reframing self-assessment as a core competency. Frontline Learn. Res. 3, 22–30. doi: 10.14786/flr.v2i1.24

Butler, D. L., and Winne, P. H. (1995). Feedback and self-regulated learning: a theoretical synthesis. Rev. Educ. Res. 65, 245–281. doi: 10.3102/00346543065003245

Butler, Y. G. (2018). “Young learners' processes and rationales for responding to self-assessment items: cases for generic can-do and five-point Likert-type formats,” in Useful Assessment and Evaluation in Language Education , eds J. Davis et al. (Washington, DC: Georgetown University Press), 21–39. doi: 10.2307/j.ctvvngrq.5

CrossRef Full Text

Chang, C.-C., Liang, C., and Chen, Y.-H. (2013). Is learner self-assessment reliable and valid in a Web-based portfolio environment for high school students? Comput. Educ. 60, 325–334. doi: 10.1016/j.compedu.2012.05.012

Chang, C.-C., Tseng, K.-H., and Lou, S.-J. (2012). A comparative analysis of the consistency and difference among teacher-assessment, student self-assessment and peer-assessment in a Web-based portfolio assessment environment for high school students. Comput. Educ. 58, 303–320. doi: 10.1016/j.compedu.2011.08.005

Colliver, J., Verhulst, S, and Barrows, H. (2005). Self-assessment in medical practice: a further concern about the conventional research paradigm. Teach. Learn. Med. 17, 200–201. doi: 10.1207/s15328015tlm1703_1

Crooks, T. J. (1988). The impact of classroom evaluation practices on students. Rev. Educ. Res. 58, 438–481. doi: 10.3102/00346543058004438

de Bruin, A. B. H., and van Gog, T. (2012). Improving self-monitoring and self-regulation: From cognitive psychology to the classroom , Learn. Instruct. 22, 245–252. doi: 10.1016/j.learninstruc.2012.01.003

De Grez, L., Valcke, M., and Roozen, I. (2012). How effective are self- and peer assessment of oral presentation skills compared with teachers' assessments? Active Learn. High. Educ. 13, 129–142. doi: 10.1177/1469787412441284

Dolosic, H. (2018). An examination of self-assessment and interconnected facets of second language reading. Read. Foreign Langu. 30, 189–208.

Draper, S. W. (2009). What are learners actually regulating when given feedback? Br. J. Educ. Technol. 40, 306–315. doi: 10.1111/j.1467-8535.2008.00930.x

Dunlosky, J., and Ariel, R. (2011). “Self-regulated learning and the allocation of study time,” in Psychology of Learning and Motivation , Vol. 54 ed B. Ross (Cambridge, MA: Academic Press), 103–140. doi: 10.1016/B978-0-12-385527-5.00004-8

Dunlosky, J., and Rawson, K. A. (2012). Overconfidence produces underachievement: inaccurate self evaluations undermine students' learning and retention. Learn. Instr. 22, 271–280. doi: 10.1016/j.learninstruc.2011.08.003

Dweck, C. (2006). Mindset: The New Psychology of Success. New York, NY: Random House.

Epstein, R. M., Siegel, D. J., and Silberman, J. (2008). Self-monitoring in clinical practice: a challenge for medical educators. J. Contin. Educ. Health Prof. 28, 5–13. doi: 10.1002/chp.149

Eva, K. W., and Regehr, G. (2008). “I'll never play professional football” and other fallacies of self-assessment. J. Contin. Educ. Health Prof. 28, 14–19. doi: 10.1002/chp.150

Falchikov, N. (2005). Improving Assessment Through Student Involvement: Practical Solutions for Aiding Learning in Higher and Further Education . London: Routledge Falmer.

Fastre, G. M. J., van der Klink, M. R., Sluijsmans, D., and van Merrienboer, J. J. G. (2012). Drawing students' attention to relevant assessment criteria: effects on self-assessment skills and performance. J. Voc. Educ. Train. 64, 185–198. doi: 10.1080/13636820.2011.630537

Fastre, G. M. J., van der Klink, M. R., and van Merrienboer, J. J. G. (2010). The effects of performance-based assessment criteria on student performance and self-assessment skills. Adv. Health Sci. Educ. 15, 517–532. doi: 10.1007/s10459-009-9215-x

Fitzpatrick, B., and Schulz, H. (2016). “Teaching young students to self-assess critically,” Paper presented at the Annual Meeting of the American Educational Research Association (Washington, DC).

Franken, A. S. (1992). I'm Good Enough, I'm Smart Enough, and Doggone it, People Like Me! Daily affirmations by Stuart Smalley. New York, NY: Dell.

Glaser, C., and Brunstein, J. C. (2007). Improving fourth-grade students' composition skills: effects of strategy instruction and self-regulation procedures. J. Educ. Psychol. 99, 297–310. doi: 10.1037/0022-0663.99.2.297

Gonida, E. N., and Leondari, A. (2011). Patterns of motivation among adolescents with biased and accurate self-efficacy beliefs. Int. J. Educ. Res. 50, 209–220. doi: 10.1016/j.ijer.2011.08.002

Graham, S., Hebert, M., and Harris, K. R. (2015). Formative assessment and writing. Elem. Sch. J. 115, 523–547. doi: 10.1086/681947

Guillory, J. J., and Blankson, A. N. (2017). Using recently acquired knowledge to self-assess understanding in the classroom. Sch. Teach. Learn. Psychol. 3, 77–89. doi: 10.1037/stl0000079

Hacker, D. J., Bol, L., Horgan, D. D., and Rakow, E. A. (2000). Test prediction and performance in a classroom context. J. Educ. Psychol. 92, 160–170. doi: 10.1037/0022-0663.92.1.160

Harding, J. L., and Hbaci, I. (2015). Evaluating pre-service teachers math teaching experience from different perspectives. Univ. J. Educ. Res. 3, 382–389. doi: 10.13189/ujer.2015.030605

Harris, K. R., Graham, S., Mason, L. H., and Friedlander, B. (2008). Powerful Writing Strategies for All Students . Baltimore, MD: Brookes.

Harris, L. R., and Brown, G. T. L. (2013). Opportunities and obstacles to consider when using peer- and self-assessment to improve student learning: case studies into teachers' implementation. Teach. Teach. Educ. 36, 101–111. doi: 10.1016/j.tate.2013.07.008

Hattie, J., and Timperley, H. (2007). The power of feedback. Rev. Educ. Res. 77, 81–112. doi: 10.3102/003465430298487

Hawe, E., and Parr, J. (2014). Assessment for learning in the writing classroom: an incomplete realization. Curr. J. 25, 210–237. doi: 10.1080/09585176.2013.862172

Hawkins, S. C., Osborne, A., Schofield, S. J., Pournaras, D. J., and Chester, J. F. (2012). Improving the accuracy of self-assessment of practical clinical skills using video feedback: the importance of including benchmarks. Med. Teach. 34, 279–284. doi: 10.3109/0142159X.2012.658897

Huang, Y., and Gui, M. (2015). Articulating teachers' expectations afore: Impact of rubrics on Chinese EFL learners' self-assessment and speaking ability. J. Educ. Train. Stud. 3, 126–132. doi: 10.11114/jets.v3i3.753

Kaderavek, J. N., Gillam, R. B., Ukrainetz, T. A., Justice, L. M., and Eisenberg, S. N. (2004). School-age children's self-assessment of oral narrative production. Commun. Disord. Q. 26, 37–48. doi: 10.1177/15257401040260010401

Karnilowicz, W. (2012). A comparison of self-assessment and tutor assessment of undergraduate psychology students. Soc. Behav. Person. 40, 591–604. doi: 10.2224/sbp.2012.40.4.591

Kevereski, L. (2017). (Self) evaluation of knowledge in students' population in higher education in Macedonia. Res. Pedag. 7, 69–75. doi: 10.17810/2015.49

Kingston, N. M., and Nash, B. (2011). Formative assessment: a meta-analysis and a call for research. Educ. Meas. 30, 28–37. doi: 10.1111/j.1745-3992.2011.00220.x

Kitsantas, A., and Zimmerman, B. J. (2006). Enhancing self-regulation of practice: the influence of graphing and self-evaluative standards. Metacogn. Learn. 1, 201–212. doi: 10.1007/s11409-006-9000-7

Kluger, A. N., and DeNisi, A. (1996). The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychol. Bull. 119, 254–284. doi: 10.1037/0033-2909.119.2.254

Kollar, I., Fischer, F., and Hesse, F. (2006). Collaboration scripts: a conceptual analysis. Educ. Psychol. Rev. 18, 159–185. doi: 10.1007/s10648-006-9007-2

Kolovelonis, A., Goudas, M., and Dermitzaki, I. (2012). Students' performance calibration in a basketball dribbling task in elementary physical education. Int. Electron. J. Elem. Educ. 4, 507–517.

Koriat, A. (2012). The relationships between monitoring, regulation and performance. Learn. Instru. 22, 296–298. doi: 10.1016/j.learninstruc.2012.01.002

Kostons, D., van Gog, T., and Paas, F. (2012). Training self-assessment and task-selection skills: a cognitive approach to improving self-regulated learning. Learn. Instruc. 22, 121–132. doi: 10.1016/j.learninstruc.2011.08.004

Labuhn, A. S., Zimmerman, B. J., and Hasselhorn, M. (2010). Enhancing students' self-regulation and mathematics performance: the influence of feedback and self-evaluative standards Metacogn. Learn. 5, 173–194. doi: 10.1007/s11409-010-9056-2

Leach, L. (2012). Optional self-assessment: some tensions and dilemmas. Assess. Evalu. High. Educ. 37, 137–147. doi: 10.1080/02602938.2010.515013

Lew, M. D. N., Alwis, W. A. M., and Schmidt, H. G. (2010). Accuracy of students' self-assessment and their beliefs about its utility. Assess. Evalu. High. Educ. 35, 135–156. doi: 10.1080/02602930802687737

Lin-Siegler, X., Shaenfield, D., and Elder, A. D. (2015). Contrasting case instruction can improve self-assessment of writing. Educ. Technol. Res. Dev. 63, 517–537. doi: 10.1007/s11423-015-9390-9

Lipnevich, A. A., Berg, D. A. G., and Smith, J. K. (2016). “Toward a model of student response to feedback,” in The Handbook of Human and Social Conditions in Assessment , eds G. T. L. Brown and L. R. Harris (New York, NY: Routledge), 169–185.

Lopez, R., and Kossack, S. (2007). Effects of recurring use of self-assessment in university courses. Int. J. Learn. 14, 203–216. doi: 10.18848/1447-9494/CGP/v14i04/45277

Lopez-Pastor, V. M., Fernandez-Balboa, J.-M., Santos Pastor, M. L., and Aranda, A. F. (2012). Students' self-grading, professor's grading and negotiated final grading at three university programmes: analysis of reliability and grade difference ranges and tendencies. Assess. Evalu. High. Educ. 37, 453–464. doi: 10.1080/02602938.2010.545868

Lui, A. (2017). Validity of the responses to feedback survey: operationalizing and measuring students' cognitive and affective responses to teachers' feedback (Doctoral dissertation). University at Albany—SUNY: Albany NY.

Marks, M. B., Haug, J. C., and Hu, H. (2018). Investigating undergraduate business internships: do supervisor and self-evaluations differ? J. Educ. Bus. 93, 33–45. doi: 10.1080/08832323.2017.1414025

Memis, E. K., and Seven, S. (2015). Effects of an SWH approach and self-evaluation on sixth grade students' learning and retention of an electricity unit. Int. J. Prog. Educ. 11, 32–49.

Metcalfe, J., and Kornell, N. (2005). A region of proximal learning model of study time allocation. J. Mem. Langu. 52, 463–477. doi: 10.1016/j.jml.2004.12.001

Meusen-Beekman, K. D., Joosten-ten Brinke, D., and Boshuizen, H. P. A. (2016). Effects of formative assessments to develop self-regulation among sixth grade students: results from a randomized controlled intervention. Stud. Educ. Evalu. 51, 126–136. doi: 10.1016/j.stueduc.2016.10.008

Micán, D. A., and Medina, C. L. (2017). Boosting vocabulary learning through self-assessment in an English language teaching context. Assess. Evalu. High. Educ. 42, 398–414. doi: 10.1080/02602938.2015.1118433

Miller, T. M., and Geraci, L. (2011). Training metacognition in the classroom: the influence of incentives and feedback on exam predictions. Metacogn. Learn. 6, 303–314. doi: 10.1007/s11409-011-9083-7

Murakami, C., Valvona, C., and Broudy, D. (2012). Turning apathy into activeness in oral communication classes: regular self- and peer-assessment in a TBLT programme. System 40, 407–420. doi: 10.1016/j.system.2012.07.003

Nagel, M., and Lindsey, B. (2018). The use of classroom clickers to support improved self-assessment in introductory chemistry. J. College Sci. Teach. 47, 72–79.

Ndoye, A. (2017). Peer/self-assessment and student learning. Int. J. Teach. Learn. High. Educ. 29, 255–269.

Nguyen, T., and Foster, K. A. (2018). Research note—multiple time point course evaluation and student learning outcomes in an MSW course. J. Soc. Work Educ. 54, 715–723. doi: 10.1080/10437797.2018.1474151

Nicol, D., and Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: a model and seven principles of good feedback practice. Stud. High. Educ. 31, 199–218. doi: 10.1080/03075070600572090

Nielsen, K. (2014), Self-assessment methods in writing instruction: a conceptual framework, successful practices and essential strategies. J. Res. Read. 37, 1–16. doi: 10.1111/j.1467-9817.2012.01533.x.

Nowell, C., and Alston, R. M. (2007). I thought I got an A! Overconfidence across the economics curriculum. J. Econ. Educ. 38, 131–142. doi: 10.3200/JECE.38.2.131-142

Nugteren, M. L., Jarodzka, H., Kester, L., and Van Merriënboer, J. J. G. (2018). Self-regulation of secondary school students: self-assessments are inaccurate and insufficiently used for learning-task selection. Instruc. Sci. 46, 357–381. doi: 10.1007/s11251-018-9448-2

Panadero, E., and Alonso-Tapia, J. (2013). Self-assessment: theoretical and practical connotations. When it happens, how is it acquired and what to do to develop it in our students. Electron. J. Res. Educ. Psychol. 11, 551–576. doi: 10.14204/ejrep.30.12200

Panadero, E., Alonso-Tapia, J., and Huertas, J. A. (2012). Rubrics and self-assessment scripts effects on self-regulation, learning and self-efficacy in secondary education. Learn. Individ. Differ. 22, 806–813. doi: 10.1016/j.lindif.2012.04.007

Panadero, E., Alonso-Tapia, J., and Huertas, J. A. (2014). Rubrics vs. self-assessment scripts: effects on first year university students' self-regulation and performance. J. Study Educ. Dev. 3, 149–183. doi: 10.1080/02103702.2014.881655

Panadero, E., Alonso-Tapia, J., and Reche, E. (2013). Rubrics vs. self-assessment scripts effect on self-regulation, performance and self-efficacy in pre-service teachers. Stud. Educ. Evalu. 39, 125–132. doi: 10.1016/j.stueduc.2013.04.001

Panadero, E., Brown, G. L., and Strijbos, J.-W. (2016a). The future of student self-assessment: a review of known unknowns and potential directions. Educ. Psychol. Rev. 28, 803–830. doi: 10.1007/s10648-015-9350-2

Panadero, E., Jonsson, A., and Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: four meta-analyses. Educ. Res. Rev. 22, 74–98. doi: 10.1016/j.edurev.2017.08.004

Panadero, E., Jonsson, A., and Strijbos, J. W. (2016b). “Scaffolding self-regulated learning through self-assessment and peer assessment: guidelines for classroom implementation,” in Assessment for Learning: Meeting the Challenge of Implementation , eds D. Laveault and L. Allal (New York, NY: Springer), 311–326. doi: 10.1007/978-3-319-39211-0_18

Panadero, E., and Romero, M. (2014). To rubric or not to rubric? The effects of self-assessment on self-regulation, performance and self-efficacy. Assess. Educ. 21, 133–148. doi: 10.1080/0969594X.2013.877872

Papanthymou, A., and Darra, M. (2018). Student self-assessment in higher education: The international experience and the Greek example. World J. Educ. 8, 130–146. doi: 10.5430/wje.v8n6p130

Punhagui, G. C., and de Souza, N. A. (2013). Self-regulation in the learning process: actions through self-assessment activities with Brazilian students. Int. Educ. Stud. 6, 47–62. doi: 10.5539/ies.v6n10p47

Raaijmakers, S. F., Baars, M., Paas, F., van Merriënboer, J. J. G., and van Gog, T. (2019). Metacognition and Learning , 1–22. doi: 10.1007/s11409-019-09189-5

Raaijmakers, S. F., Baars, M., Schapp, L., Paas, F., van Merrienboer, J., and van Gog, T. (2017). Training self-regulated learning with video modeling examples: do task-selection skills transfer? Instr. Sci. 46, 273–290. doi: 10.1007/s11251-017-9434-0

Ratminingsih, N. M., Marhaeni, A. A. I. N., and Vigayanti, L. P. D. (2018). Self-assessment: the effect on students' independence and writing competence. Int. J. Instruc. 11, 277–290. doi: 10.12973/iji.2018.11320a

Ross, J. A., Rolheiser, C., and Hogaboam-Gray, A. (1998). “Impact of self-evaluation training on mathematics achievement in a cooperative learning environment,” Paper presented at the annual meeting of the American Educational Research Association (San Diego, CA).

Ross, J. A., and Starling, M. (2008). Self-assessment in a technology-supported environment: the case of grade 9 geography. Assess. Educ. 15, 183–199. doi: 10.1080/09695940802164218

Samaie, M., Nejad, A. M., and Qaracholloo, M. (2018). An inquiry into the efficiency of whatsapp for self- and peer-assessments of oral language proficiency. Br. J. Educ. Technol. 49, 111–126. doi: 10.1111/bjet.12519

Sanchez, C. E., Atkinson, K. M., Koenka, A. C., Moshontz, H., and Cooper, H. (2017). Self-grading and peer-grading for formative and summative assessments in 3rd through 12th grade classrooms: a meta-analysis. J. Educ. Psychol. 109, 1049–1066. doi: 10.1037/edu0000190

Sargeant, J. (2008). Toward a common understanding of self-assessment. J. Contin. Educ. Health Prof. 28, 1–4. doi: 10.1002/chp.148

Sargeant, J., Mann, K., van der Vleuten, C., and Metsemakers, J. (2008). “Directed” self-assessment: practice and feedback within a social context. J. Contin. Educ. Health Prof. 28, 47–54. doi: 10.1002/chp.155

Shute, V. (2008). Focus on formative feedback. Rev. Educ. Res. 78, 153–189. doi: 10.3102/0034654307313795

Silver, I., Campbell, C., Marlow, B., and Sargeant, J. (2008). Self-assessment and continuing professional development: the Canadian perspective. J. Contin. Educ. Health Prof. 28, 25–31. doi: 10.1002/chp.152

Siow, L.-F. (2015). Students' perceptions on self- and peer-assessment in enhancing learning experience. Malaysian Online J. Educ. Sci. 3, 21–35.

Son, L. K., and Metcalfe, J. (2000). Metacognitive and control strategies in study-time allocation. J. Exp. Psychol. 26, 204–221. doi: 10.1037/0278-7393.26.1.204

Tan, K. (2004). Does student self-assessment empower or discipline students? Assess. Evalu. Higher Educ. 29, 651–662. doi: 10.1080/0260293042000227209

Tan, K. (2009). Meanings and practices of power in academics' conceptions of student self-assessment. Teach. High. Educ. 14, 361–373. doi: 10.1080/13562510903050111

Taras, M. (2008). Issues of power and equity in two models of self-assessment. Teach. High. Educ. 13, 81–92. doi: 10.1080/13562510701794076

Tejeiro, R. A., Gomez-Vallecillo, J. L., Romero, A. F., Pelegrina, M., Wallace, A., and Emberley, E. (2012). Summative self-assessment in higher education: implications of its counting towards the final mark. Electron. J. Res. Educ. Psychol. 10, 789–812.

Thawabieh, A. M. (2017). A comparison between students' self-assessment and teachers' assessment. J. Curri. Teach. 6, 14–20. doi: 10.5430/jct.v6n1p14

Tulgar, A. T. (2017). Selfie@ssessment as an alternative form of self-assessment at undergraduate level in higher education. J. Langu. Linguis. Stud. 13, 321–335.

van Helvoort, A. A. J. (2012). How adult students in information studies use a scoring rubric for the development of their information literacy skills. J. Acad. Librarian. 38, 165–171. doi: 10.1016/j.acalib.2012.03.016

van Loon, M. H., de Bruin, A. B. H., van Gog, T., van Merriënboer, J. J. G., and Dunlosky, J. (2014). Can students evaluate their understanding of cause-and-effect relations? The effects of diagram completion on monitoring accuracy. Acta Psychol. 151, 143–154. doi: 10.1016/j.actpsy.2014.06.007

van Reybroeck, M., Penneman, J., Vidick, C., and Galand, B. (2017). Progressive treatment and self-assessment: Effects on students' automatisation of grammatical spelling and self-efficacy beliefs. Read. Writing 30, 1965–1985. doi: 10.1007/s11145-017-9761-1

Wang, W. (2017). Using rubrics in student self-assessment: student perceptions in the English as a foreign language writing context. Assess. Evalu. High. Educ. 42, 1280–1292. doi: 10.1080/02602938.2016.1261993

Wollenschläger, M., Hattie, J., Machts, N., Möller, J., and Harms, U. (2016). What makes rubrics effective in teacher-feedback? Transparency of learning goals is not enough. Contemp. Educ. Psychol. 44–45, 1–11. doi: 10.1016/j.cedpsych.2015.11.003

Yan, Z., and Brown, G. T. L. (2017). A cyclical self-assessment process: towards a model of how students engage in self-assessment. Assess. Evalu. High. Educ. 42, 1247–1262. doi: 10.1080/02602938.2016.1260091

Yilmaz, F. N. (2017). Reliability of scores obtained from self-, peer-, and teacher-assessments on teaching materials prepared by teacher candidates. Educ. Sci. 17, 395–409. doi: 10.12738/estp.2017.2.0098

Zimmerman, B. J. (2000). Self-efficacy: an essential motive to learn. Contemp. Educ. Psychol. 25, 82–91. doi: 10.1006/ceps.1999.1016

Zimmerman, B. J., and Schunk, D. H. (2011). “Self-regulated learning and performance: an introduction and overview,” in Handbook of Self-Regulation of Learning and Performance , eds B. J. Zimmerman and D. H. Schunk (New York, NY: Routledge), 1–14.

Keywords: self-assessment, self-evaluation, self-grading, formative assessment, classroom assessment, self-regulated learning (SRL)

Citation: Andrade HL (2019) A Critical Review of Research on Student Self-Assessment. Front. Educ. 4:87. doi: 10.3389/feduc.2019.00087

Received: 27 April 2019; Accepted: 02 August 2019; Published: 27 August 2019.

Reviewed by:

Copyright © 2019 Andrade. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Heidi L. Andrade, handrade@albany.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• DOI: 10.1080/09695940903319661
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Research on diagnosis and assessment processes and methods for existing residential buildings based on intelligent assistance models.

1. Introduction

2. literature review, 2.1. evaluation of building renovation strategies and pre-assisted decision-making, 2.2. status of artificial intelligence—aided modeling in architecture, 3. methodology, 3.1. diagnostic evaluation process based on intelligent assistive modeling, 3.2. selection and definition of key elements, 3.3. data collection and integration, 3.4. classification of assessment levels and rulemaking, 3.5. construction of intelligent auxiliary model and interaction app design, 4. case study—residence a in shenzhen as an example, 4.1. selection of research subjects, 4.2. diagnosis of the current status of the case, 4.3. intelligent assessment results and analysis, 4.4. exploration of regeneration design decisions, 5. conclusions and future outlook, author contributions, data availability statement, conflicts of interest.

• Liu, Y.; Xu, P.; Liu, N. Building Material Carbon Emission Prediction Models for Reinforced-Concrete Shear-Wall Urban Residential Buildings in Northern China. Buildings 2024 , 14 , 1812. [ Google Scholar ] [ CrossRef ]
• Chen, S.; Hu, J.; Huang, Y.; Huang, Z.; Yang, S.; Wu, P. Characteristic clustering of residential energy use behavior and its impact on energy demand in Guangzhou. HVAC 2022 , 52 , 151–150+157. [ Google Scholar ]
• Jia, L.; Qian, Q.K.; Meijer, F.; Visscher, H. Strategies for Mitigating Risks of Government-Led Energy Retrofitting Projects in China. Buildings 2024 , 14 , 1663. [ Google Scholar ] [ CrossRef ]
• Wang, Z.; Shen, M.; Huang, Y. Exploring the Impact of Facade Color Elements on Visual Comfort in Old Residential Buildings in Shanghai. Insights from Eye-Tracking Technology. Buildings 2024 , 14 , 1758. [ Google Scholar ] [ CrossRef ]
• Arbulu, M.; Perez-Bezos, S.; Figueroa-Lopez, A.; Oregi, X. Opportunities and Barriers of Calibrating Residential Building Performance Simulation Models Using Monitored and Survey-Based Occupant Behavioural Data. A Case Study in Northern Spain. Buildings 2024 , 14 , 1911. [ Google Scholar ] [ CrossRef ]
• Thuvander, L.; Femenías, P.; Mjörnell, K.; Meiling, P. Unveiling the Process of Sustainable Renovation. Sustainability 2012 , 4 , 1188–1213. [ Google Scholar ] [ CrossRef ]
• Zhang, B.; Lei, Z. Research on the investigation of architectural pathology in Qiongkushtai village based on GIS platform and infrared thermal imaging technology. Cent. China Archit. 2019 , 37 , 36–39. [ Google Scholar ]
• Sitzia, F.; Lisci, C.; Mirão, J. Building pathology and environment: Weathering and decay of stone construction materials subjected to a Csa mediterranean climate laboratory simulation. Constr. Build. Mater. 2021 , 300 , 124311. [ Google Scholar ] [ CrossRef ]
• Nowogońska, B. A Methodology for Determining the Rehabilitation Needs of Buildings. Appl. Sci. 2020 , 10 , 3873. [ Google Scholar ] [ CrossRef ]
• Nowogońska, B.; Korentz, J. Value of Technical Wear and Costs of Restoring Performance Characteristics to Residential Buildings. Buildings 2020 , 10 , 9. [ Google Scholar ] [ CrossRef ]
• Matsumura, S.; Lee, S. Development Trends of Settlement Renewal in Japan. Time Archit. 2020 , 1 , 10–13. [ Google Scholar ] [ CrossRef ]
• Aikivuori, A.M. Critical Loss of Performance—What Fails before Durability. In Proceedings of the 8th International Conference on Durability of Building Materials and Components, Vancouver, BC, Canada, 30 May–3 June 1999; pp. 1369–1376. [ Google Scholar ]
• Brand, S. How Buildings Learn—What Happens after They’re Built ; Orion Books: London, UK, 1997. [ Google Scholar ]
• Petrenko, L.; Manjilevskaja, S. Housing operation taking into account obsolescence and physical deterioration. IOP Conf. Ser. Mater. Sci. Eng. 2017 , 262 , 012077. [ Google Scholar ] [ CrossRef ]
• Silva, A.; de Brito, J.; Thomsen, A.; Straub, A.; Prieto, A.J.; Lacasse, M.A. Causal effects between criteria that Establish the end of service life of buildings and components. Buildings 2022 , 12 , 88. [ Google Scholar ] [ CrossRef ]
• Abramson, D.M. Obsolescence: An Architectural History ; University of Chicago Press: Chicago, IL, USA, 2012. [ Google Scholar ]
• Meng, Y.; Hao, Y.; Que, Y.; Ren, J.; Liu, Y. Multi-Objective Optimization of Morphology in High-Rise Residential Areas for Outdoor Thermal Comfort in Yulin City, Northwest China. Buildings 2024 , 14 , 1688. [ Google Scholar ] [ CrossRef ]
• Benz, A.; Voelker, C.; Daubert, S.; Rodehorst, V. Towards an automated image-based estimation of building age as input for Building Energy Modeling (BEM). Energy Build. 2023 , 292 , 113166. [ Google Scholar ] [ CrossRef ]
• Konstantinidis, D.; Argyriou, V.; Stathaki, T.; Grammalidis, N. A modular CNN-based building detector for remote sensing images. Comput. Netw. 2020 , 168 , 107034. [ Google Scholar ] [ CrossRef ]
• Qing, Y.; Ming, D.; Wen, Q.; Weng, Q.; Xu, L.; Chen, Y.; Zhang, Y.; Zeng, B. Operational earthquake-induced building damage assessment using CNN-based direct remote sensing change detection on superpixel level. Int. J. Appl. Earth Obs. Geoinf. 2022 , 112 , 102899. [ Google Scholar ] [ CrossRef ]
• Sarkhosh, M.; Najafpoor, A.A.; Alidadi, H.; Shamsara, J.; Amiri, H.; Andrea, T.; Kariminejad, F. Indoor Air Quality associations with sick building syndrome: An application of decision tree technology. Build. Environ. 2021 , 188 , 107446. [ Google Scholar ] [ CrossRef ]
• Ramakrishnan, J.; Liu, T.; Zhang, F.; Seshadri, K.; Yu, R.; Gou, Z. A decision tree-based modeling approach for evaluating the green performance of airport buildings. Environ. Impact Assess. Rev. 2023 , 100 , 107070. [ Google Scholar ] [ CrossRef ]
• Hoła, A.; Czarnecki, S. Random forest algorithm and support vector machine for nondestructive assessment of mass moisture content of brick walls in historic buildings. Autom. Constr. 2023 , 149 , 104793. [ Google Scholar ] [ CrossRef ]
• Hussien, A.; Khan, W.; Hussain, A.; Liatsis, P.; Al-Shamma’A, A.; Al-Jumeily, D. Predicting energy performances of buildings’ envelope wall materials via the random forest algorithm. J. Build. Eng. 2023 , 69 , 106263. [ Google Scholar ] [ CrossRef ]
• Pan, Y.; Shen, Y.; Qin, J.; Zhang, L.; Pan, Y.; Shen, Y.; Qin, J.; Zhang, L.; Pan, Y.; Shen, Y.; et al. Deep reinforcement learning for multi-objective optimization in BIM-based green building design. Autom. Constr. 2024 , 166 , 105598. [ Google Scholar ] [ CrossRef ]
• Tavares, I.; Manfredini, R.; Almeida, J.; Soares, J.; Ramos, S.; Foroozandeh, Z.; Vale, Z. Comparison of PV power generation forecasting in a residential building using ANN and DNN. IFAC-Pap. 2022 , 55 , 291–296. [ Google Scholar ] [ CrossRef ]
• Lee, K.; Lim, H.; Hwang, J.; Lee, D. Evaluating missing data handling methods for developing building energy benchmarking models. Energy 2024 , 308 , 132979. [ Google Scholar ] [ CrossRef ]
• Szulc, J.; Piekarczuk, A. Diagnostics and technical condition assessment of large-panel residential buildings in Poland. J. Build. Eng. 2022 , 50 , 104144. [ Google Scholar ] [ CrossRef ]
• Peiris, S.; Lai, J.H.; Kumaraswamy, M.M.; Hou, H. Smart retrofitting for existing buildings: State of the art and future research directions. J. Build. Eng. 2023 , 76 , 107354. [ Google Scholar ] [ CrossRef ]
• Mercader-Moyano, P.; Camporeale, P.; Serrano-Jiménez, A. Integrated urban regeneration for high-rise multi-family buildings by providing a multidimensional assessment model and decision support system. J. Build. Eng. 2023 , 76 , 107359. [ Google Scholar ] [ CrossRef ]
• Nielsen, A.N.; Jensen, R.L.; Larsen, T.S.; Nissen, S.B. Early stage decision support for sustainable building renovation. Build. Environ. 2016 , 8 , 117. [ Google Scholar ] [ CrossRef ]
• Tejedor, B.; Lucchi, E.; Bienvenido-Huertas, D.; Nardi, I. Non-destructive techniques (NDT) for the diagnosis of heritage buildings: Traditional procedures and futures perspectives. Energy Build. 2022 , 263 , 112029. [ Google Scholar ] [ CrossRef ]
• Volk, R.; Stengel, J.; Schultmann, F. Building Information Modeling (BIM) for existing buildings—Literature review and future needs. Autom. Constr. 2014 , 38 , 109–127. [ Google Scholar ] [ CrossRef ]
• Fai, S.; Graham, K.; Duckworth, T.; Wood, N.; Attar, R. Building information modelling and heritage documentation. In Proceedings of the 23rd International Symposium, International Scientific Committee for Documentation of Cultural Heritage (CIPA), Prague, Czech Republic, 12–16 September 2011. [ Google Scholar ]
• Alavi, H.; Bortolini, R.; Forcada, N. BIM-based decision support for building condition assessment. Autom. Constr. 2022 , 135 , 104117. [ Google Scholar ] [ CrossRef ]
• Johansson, P.; Femenías, P.; Thuvander, L.; Wahlgren, P. Pending for renovations: Understanding the conditions of the multi-family housing stock from before 1945. Energy Procedia 2016 , 96 , 70–179. [ Google Scholar ] [ CrossRef ]
• Pereira, C.; Silva, A.; Ferreira, C.; de Brito, J.; Flores-Colen, I.; Silvestre, J.D. Uncertainty in building inspection and diagnosis: A Probabilistic model Quantification. Infrastructure 2021 , 6 , 124. [ Google Scholar ] [ CrossRef ]
• Valença, J.; Santos, B.; Araújo, A.; Júlio, E. Aerial Crack View: Crack Monitoring in Concrete Bridges through Image Processing Acquired by UAV. Congr. IABSE Evol. Metrop. 2019 , 10 , 2458. [ Google Scholar ] [ CrossRef ]
• Lerma, J.L.; Cabrelles, M.; Portalés, C. Portalés Multitemporal thermal analysis to detect moisture on a building façade. Constr. Build. Mater. 2011 , 25 , 2190–2197. [ Google Scholar ] [ CrossRef ]
• Zong, Y.; Li, D.; Teng, J.; Wu, X. Energy saving and economic analysis of external window glazing selection scheme for residential windows in Wuhan. New Build. Mater. 2014 , 41 , 36–38+79. [ Google Scholar ] [ CrossRef ]
• Zhao, L.; Zhao, W.; Yao, H.; Chen, J.; Zhang, D.; Wu, Y. Research on energy-saving performance-based design of residential buildings based on solar energy utilization. Ind. Build. 2023 , 53 , 57-64+157. [ Google Scholar ] [ CrossRef ]
• Liu, X.; Lin, X.; Feng, G. Optimization of energy-saving retrofit of existing building envelopes in severe cold regions. Build. Technol. 2023 , 54 , 78–82. [ Google Scholar ]
• Galimshina, A.; Moustapha, M.; Hollberg, A.; Padey, P.; Lasvaux, S.; Sudret, B.; Habert, G. Statistical method to identify robust building renovation choices for environmental and economic performance. Build. Environ. 2020 , 183 , 107143. [ Google Scholar ] [ CrossRef ]
• Decorte, Y.; Steeman, M.; Van Den Bossche, N.; Calle, K. Environmental evaluation of pareto optimal renovation strategies: A multidimensional life-cycle analysis. E3S Web Conf. 2020 , 172 , 18003. [ Google Scholar ] [ CrossRef ]
• Rostamiasl, V. Integrating Building Information Modeling (BIM) and Virtual Reality (VR) for Sustainable Universal Design at the Conceptual Stage of Housing Projects. Ph.D. Dissertation, Université d’Ottawa, Ottawa, ON, Canada, 2024. [ Google Scholar ] [ CrossRef ]
• Omran, J.Y.; Wassouf, M.A. Using Building Information Modelling (BIM) by studying building orientation during design to achieve more sustainable buildings. Vestn. MGSU 2024 , 19 , 436–455. [ Google Scholar ] [ CrossRef ]
• Shestakova, E.B.; Shestakov, P.D.; Novikov, A.L. Efficient building practices: 3D models for process optimization. E3S Web Conf. 2024 , 549 , 09029. [ Google Scholar ] [ CrossRef ]
• Matsumura, S.; Wu, Z. Theoretical Changes and Technological Evolution of Prefabricated Building Methods for Residential Production in Japan. J. Archit. 2020 , 5 , 6–11. [ Google Scholar ]
• Hai, M.; Zhou, B.; Fan, Y.; Li, Z. Localized modeling of the public-community-private hierarchy approach for quality improvement of old settlements. New Archit. 2022 , 3 , 117–121. [ Google Scholar ]
• Zhang, Y.; Du, Z. Urban Modern Transformation and Architectural Type Innovation: Exploring the Characteristics of Large-scale Collective Residential Design in Shenzhen in the 1980s. New Archit. 2022 , 6 , 110–116. [ Google Scholar ]
• Farsäter, K.; Strandberg, P.; Wahlström, Å. Building status obtained before renovating multifamily buildings in SwedenJ. Build. Eng 2019 , 24 , 100723. [ Google Scholar ] [ CrossRef ]
• Garrido-piñero, J.; Mercader-moyano, P. EIAMUO methodology for environmental assessment of the post-war housing estates renovation: Practical application in Seville (Spain). Environ. Impact Assess. Rev. 2017 , 67 , 124–133. [ Google Scholar ] [ CrossRef ]
• Ge, J.; Li, S.; Chen, S.; Wang, X.; Jiang, Z.; Shen, C. Energy-efficiency strategies of residential envelope in China’s Hot Summer–Cold Winter Zone based on intermittent thermal regulation behaviour. J. Build. Eng. 2021 , 44 , 103028. [ Google Scholar ] [ CrossRef ]
• Mjörnell, K.; Femenías, P.; Annadotter, K. Renovation strategies for multi-residential buildings from the record years in Sweden—Profit-Driven or socioeconomically responsible? Sustainability 2019 , 11 , 6988. [ Google Scholar ] [ CrossRef ]
• Invidiata, A.; Lavagna, M.; Ghisi, E. Selecting design strategies using multi-criteria decision making to improve the sustainability of buildings. Build. Environ. 2018 , 139 , 58–68. [ Google Scholar ] [ CrossRef ]
• Li, H.; Zhang, X.; Ng, S.T.; Skitmore, M. Quantifying stakeholder influence in decision/evaluations relating to sustainable construction in China—A Delphi approach. J. Clean. Prod. 2018 , 173 , 160–170. [ Google Scholar ] [ CrossRef ]
• Moghtadernejad, S.; Chouinard, L.E.; Mirza, M.S. Enhanced façade design: A data-driven approach for decision analysis based on past experiencesDev. Built Environ. 2021 , 5 , 100038. [ Google Scholar ] [ CrossRef ]
• Huang, Z.; Gou, Z.; Cheng, B. An investigation of outdoor thermal environments with different ground surfaces in the hot summer-cold winter climate region. J. Build. Eng. 2020 , 27 , 100994. [ Google Scholar ] [ CrossRef ]
• Giretti, A.; Corneli, A.; Naticchia, B. A decision support system for scenario analysis in energy refurbishment of residential buildings. Energies 2021 , 14 , 4738. [ Google Scholar ] [ CrossRef ]
• Kamaruzzaman, S.N.; Lou, E.C.W.; Wong, P.F.; Wood, R.; Che-Ani, A.I. Developing weighting system for refurbishment building assessment scheme in Malaysia through analytic hierarchy process (AHP) approach. Energy Pol. 2018 , 112 , 280–290. [ Google Scholar ] [ CrossRef ]
• Monzón, M.; López-Mesa, B. Buildings performance indicators to prioritise multi-family housing renovations. Sustain. Cities Soc. 2018 , 38 , 109–122. [ Google Scholar ] [ CrossRef ]
• José, P.; Luís, B.; Ricardo, M. Automating building sustainability assessment using building information modelling: A case study. J. Build. Eng. 2023 , 76 , 107228. [ Google Scholar ] [ CrossRef ]

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Share and Cite

Liu, C.; Zhang, Q.; Fan, Y.; Lin, G.; Huang, Z. Research on Diagnosis and Assessment Processes and Methods for Existing Residential Buildings Based on Intelligent Assistance Models. Buildings 2024 , 14 , 3062. https://doi.org/10.3390/buildings14103062

Liu C, Zhang Q, Fan Y, Lin G, Huang Z. Research on Diagnosis and Assessment Processes and Methods for Existing Residential Buildings Based on Intelligent Assistance Models. Buildings . 2024; 14(10):3062. https://doi.org/10.3390/buildings14103062

Liu, Chang, Qiong Zhang, Yue Fan, Guanfeng Lin, and Zhengyao Huang. 2024. "Research on Diagnosis and Assessment Processes and Methods for Existing Residential Buildings Based on Intelligent Assistance Models" Buildings 14, no. 10: 3062. https://doi.org/10.3390/buildings14103062

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Study enhancing learning methods for AI and machine learning systems wins IEEE award

By Peter Murphy

Published May 21, 2024

A paper authored by Seyyedali Hosseinalipour (Ali Alipour) received the Institute of Electrical and Electronics Engineers (IEEE) Communications Society William R. Bennett Prize. The research could enhance learning methods used by artificial intelligence (AI) and machine learning (ML) systems.

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• Seyyedali Hosseinalipour (Ali Alipour) , PhD
• 9/24/24 Department of Electrical Engineering

According to IEEE, the award “recognizes outstanding original papers published in IEEE/ACM Transactions on Networking or the IEEE Transactions on Network and Service Management” within the last three years.

Alipour’s research enhances federated learning, a method researchers use to secure private information while collecting data to better train AI and ML systems.  

AI and ML systems collect data from various sources to enhance their capabilities. The data collection associated with each of these methods, however, come with privacy concerns. Data collected from personal devices like smartphones and other electronics could be stored in a single location, like a cloud server. Federated learning allows the data to remain on the device. The only data sent to a central server during federated learning is the AI or ML model parameters.

“Federated learning was first developed by researchers at Google, initially aimed at enhancing next-word prediction for smartphone keyboards,” Alipour says. “Today, technology giants like NVIDIA apply federated learning to sectors such as healthcare, where protecting patient data privacy is crucial,” Alipour says.

Alipour’s new method, multi-stage hybrid federated learning (MH-FL), allows devices to interact with each other before sending any information to a server. The devices can work together to refine learning methods associated with the AI and ML systems before sharing information to a central server.

“MH-FL introduces an additional layer of flexibility by enabling client-to-client interactions, also known as device-to-device interactions. Our findings indicate that incorporating this degree of freedom can significantly enhance model prediction performance in federated learning,” Alipour says. “Additionally, it contributes to reduced energy consumption and latency, optimizing both the efficiency and effectiveness of the learning process.”

The work in this award-winning paper has set the foundation for Alipour’s work with federated learning. He has continued to develop and explore different federated learning techniques.

“Ali is a leading researcher in the analysis and modeling of modern wireless networks, specifically in the application of machine-learning techniques to the design and implementation of next-generation wireless networks,” says Jon Bird, professor and chair in the Department of Electrical Engineering. “This achievement is especially remarkable for Ali, given his current position as a junior assistant professor.”

The William R. Bennett Prize is competitive. Out of the potentially thousands of papers considered, just one is selected for the award.

“As researchers, we are always thrilled when our ideas are well-received. I hope to use this excitement as motivation to continue contributing to the rapidly evolving fields of AI and ML,” Alipour says. “I also hope that this enthusiasm not only drives our current research but inspires further innovations and discoveries.”

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