Evidence-Based Critique in Higher Education Quality Assurance: A Four-Type Policy Gap Analysis of the YÖKAK Institutional Self-Evaluation Rubric (v3.2.1)

This is a condensed English version of a four-part Turkish technical report (about 42,000 words). It presents the conceptual framework, the evidence summaries for each section, the four-type typology and the policy recommendations. The item-by-item analyses and unit coverage matrices are available in the original report.

1. Introduction

Over the past fifteen years the Turkish higher education quality assurance regime has gone through extensive institutionalisation. Milestones include the replacement of the Higher Education Academic Assessment and Quality Improvement Commission (YÖDEK) by the Higher Education Quality Council (YÖKAK) in 2015, YÖKAK’s legal administrative and financial autonomy in 2017, and full ENQA membership and listing on EQAR in 2020. YÖKAK’s Institutional Self-Evaluation Report Guide functions as the core reference document for institutions’ self-assessment and as the rubric used by external review teams during site visits. In its latest version, 3.2.1 (24 January 2025), the guide evaluates institutions on 45 sub-items under four main sections: A. Leadership, Governance and Quality; B. Education and Teaching; C. Research and Development; D. Social Contribution.

This study critically examines the four sections of Version 3.2.1 from the perspective of evidence-based policy, the management philosophy holding that public policies and regulatory frameworks should be systematically grounded in scientific evidence. The methodological framework is based on the evidence hierarchy systematised in evidence-based medicine by Sackett et al. (1996) and adapted to public policy. Evidence types are assessed from the highest to the lowest generalisability: randomised controlled trials (RCTs) and quasi-experimental designs; meta-analyses; systematic reviews; case studies; expert consensus; and legislation and normative foundations. For each sub-item, the existing literature was positioned within this six-type taxonomy, and the alignment, critical relationship or gaps between the rubric formulation and the literature were assessed systematically. A coverage analysis was also carried out for each item, identifying the institutional units and areas that are and are not concerned.

For every item, three questions were asked. First: what kind of scientific evidence supports the structure, process or practice the item requires? Second: if there is no high-level evidence, what is the item’s epistemic justification? Third: which administrative and academic units does the item cover, and which does it not concern directly? Answers to the second question typically fall into four categories: (1) legal obligation (for example, the Public Financial Management and Control Law No. 5018 or the Higher Education Law No. 2547); (2) international consensus documents (ESG-ENQA, ISO 9001, OECD principles); (3) empirical literature from management science, usually case studies and cross-sectional research; and (4) ethical and normative values (transparency, accountability, equal opportunity). These categories are legitimate, but they do not carry the reliability that RCTs and meta-analyses provide in the evidence hierarchy.

Producing evidence in higher education quality assurance involves three core difficulties: randomisation (assigning a university to a quality intervention at random is ethically and operationally problematic), heterogeneity (institutions differ so much in size, mission, resources, culture and national context that a single intervention cannot be expected to work the same everywhere) and multiple outputs with long lags (the effects of leadership, governance or quality culture on learning, employment or research emerge years later and indirectly). These difficulties partly explain the shortage of high-level evidence, but they are no reason to leave the real-world effects of normative propositions unquestioned (Pawson & Tilley, 1997; Pawson, 2006; Murad et al., 2016).

2. The Four-Type Typology of Evidence-Policy Gaps

The central conceptual contribution of the study is a four-type typology of evidence-policy gaps that emerged from examining the four sections. The typology classifies the different epistemological landscapes displayed by the relationship between evidence and policy in quality assurance rubrics, and is offered to the international literature as an original conceptual tool.

Cumulative quality assurance studies exist, such as Harvey and Williams’ (2010) fifteen-year review in Quality in Higher Education, but a typological analysis of the evidence-policy relationship has not yet been undertaken. The typology can be used not only for Türkiye but also for comparative analysis of international quality assurance systems (the ESG-ENQA European standards, the UK QAA framework, US regional accreditors such as HLC and NEASC, Australia’s TEQSA and New Zealand’s NZQA).

The evidence summary tables below use the following notation: ✓ = direct evidence available; ✓* = see the note under each table; P = partial, indirect or contested evidence; – = no or very limited evidence.

3. Section A: Leadership, Governance and Quality (Silent Absence)

The central question of Part A was whether the structures, processes and practices required of institutions in the name of quality assurance rest on high-level scientific evidence. The answer is largely negative. Only one item (A.4.2 Student Feedback) has a direct and dense meta-analytic and systematic review literature, and that literature, ironically, weakens rather than supports the validity of current practice. For seven items (A.1.2 Leadership, A.2.2 Strategic Plan, A.2.3 Performance Management, A.3.1 Information Management System, A.3.2 Human Resources, A.4.1 Stakeholder Engagement, A.5.1 Internationalisation Management) there is moderate empirical evidence from general management science, but no systematic reviews or RCTs in the higher education context. The remaining items rest entirely on normative consensus, legislation or expert opinion.

The justification of YÖKAK criteria is drawn largely from consensus-based frameworks such as the European Standards and Guidelines (ESG-ENQA), ISO 9001, the EFQM Excellence Model, the Baldrige framework and the PDCA cycle. These are internally consistent and internationally accepted normative documents, but in the evidence hierarchy they sit at the level of expert opinion and consensus.

Table 1. Evidence types available for Section A items

Note: ✓* = moderate evidence from general management science, but limited evidence specific to higher education.

The student feedback paradox

A.4.2 Student Feedback has the richest meta-analytic literature in Section A, yet methodologically rigorous studies over the past two decades (Carrell & West, 2010; Boring, Ottoboni & Stark, 2016; Uttl, White & Gonzalez, 2017) show that the valid relationship between student evaluations of teaching and learning outcomes is weak, that the evaluations carry systematic biases and that they should not be used alone in high-stakes decisions. The YÖKAK guide does not yet reflect this body of evidence, which is a significant gap for evidence-based higher education policy.

Implications for Section A

The task is not to abandon the YÖKAK criteria but to report evidence gaps transparently and to keep the hierarchy in mind when requesting quality evidence from institutions. The “evidence” requested from institutions often remains at the level of documenting that a process exists (minutes, survey samples, directive texts); the effect of the process on outcomes (effectiveness) is not questioned. A real rise in maturity requires moving from process evidence to outcome evidence, which is possible only through multi-year, multi-institution, controlled designs such as quasi-experimental studies, interrupted time series, natural experiments and cluster RCTs. The tools of evidence-based health management (the evidence hierarchy, systematic review methodology and PRISMA) can be transferred to higher education policy (Davies, Nutley & Smith, 2000).

4. Section B: Education and Teaching (Silent Omission)

Unlike Section A, Section B rests on a far richer evidence base. Decades of research on the effectiveness of university teaching, with many meta-analyses and systematic reviews, provide direct and strong evidence for several items: the active learning meta-analysis of 225 studies by Freeman et al. (2014), Hattie’s (2009) synthesis of more than 800 meta-analyses, Black and Wiliam’s (1998, 2009) foundational work on formative assessment, Hattie and Timperley’s (2007) review of feedback, the BEME systematic reviews on faculty development by Steinert et al. (2006, 2016), the meta-analysis of academic support by Robbins et al. (2004) and the synthesis by Pascarella and Terenzini (2005).

Of the 18 items, six have direct meta-analytic support (B.2.1 teaching methods, B.2.2 assessment, B.3.1 learning resources, B.3.2 academic support, B.3.4 disadvantaged groups, B.4.2 teaching competence development), five have systematic review support (B.1.3, B.1.5, B.2.3, B.3.5, B.4.3), three are positioned with moderate evidence and Bologna consensus (B.1.1, B.1.4, B.1.6), and four rest mainly on legislation and consensus (B.1.2, B.2.4, B.3.3, B.4.1). The core criticism of Section B is therefore not absence, but the fact that existing evidence has not been internalised in the YÖKAK criteria.

Table 2. Evidence types available for Section B items

Note: ✓* = moderate evidence from general management science, with limited higher-education-specific evidence.

Three paradoxes

“Where the evidence is strongest, the rubric is weakest.” B.2.1 Teaching Methods and Techniques covers the area with the strongest evidence in the literature: Freeman et al. (2014) showed that active learning raises student performance in STEM by about 6 percentage points over traditional lecturing. Yet the rubric does not require active learning; it only asks whether student-centred teaching methods are applied in line with defined processes. The same paradox appears in B.2.2 with Black and Wiliam’s formative assessment literature (effect sizes of roughly 0.4 to 0.7) and in B.4.2 with the BEME reviews of Steinert et al., whose consistent question (do faculty development activities actually improve student learning?) the rubric never asks. All three items settle for process definition and push outcome verification into the background.

“A formulation unaware of contested evidence.” B.4.3 Incentives and Rewards is one of the rare areas where evidence exists but is contested. Deci, Koestner and Ryan’s (1999) meta-analysis of 128 studies showed that extrinsic rewards can erode intrinsic motivation in creative or open-ended tasks, while the counter meta-analysis by Cameron, Banko and Pierce (2001) reported condition-dependent results. Self-determination theory (Ryan & Deci, 2000) holds that rewards have conditional effects. The rubric positions incentives and rewards as an unquestionable good, as if unaware of this twenty-year scientific debate.

“Ignoring cross-links.” Theobald et al. (2020) showed in PNAS that active learning halves the achievement gap for under-represented students. This builds a direct evidence bridge between B.2.1 (teaching methods) and B.3.4 (disadvantaged groups): active learning is not only a pedagogical preference but also an equity intervention. Capp’s (2017) meta-analysis of Universal Design for Learning and Bowman’s (2010) meta-analysis of diversity experiences support the same link. The rubric nonetheless treats these items separately and reduces B.3.4 to a rights-based normative discourse without reference to evidence-based interventions. Similarly, B.3.5 could draw on four decades of student engagement research (Astin, 1984, 1993; Tinto, 1975, 1993), but relies on an indirect formulation that does not link activities to learning outcomes.

The common source of these paradoxes is the rubric’s basic assumption: maturity-level criteria track whether institutional processes are defined and sustained, not whether learning outcomes actually improve. When maturity levels 4 and 5 (“monitoring and improvement” and “internalised exemplary practice”) are not linked to learning outcomes, the rubric risks formal “quality theatre” (Harvey & Williams, 2010). A further gap concerns digital pedagogy: despite frequent references to distance education, the rubric does not refer to evidence-based frameworks such as TPACK (Mishra & Koehler, 2006) or to evidence maps of educational technology (Bond et al., 2020).

Structurally, the operational owners of Section B are the academic units: in 14 of the 18 items academic units are highly involved, compared with only four items for the Quality Commission and five for top management. Successful implementation of Section B therefore depends on institutionalising quality culture at the academic unit level rather than on central quality management.

5. Section C: Research and Development (Active Contradiction)

Section C occupies a third position. It has a methodologically strong but normatively highly contested evidence base drawn from the sociology and economics of science, bibliometrics and research policy. All seven items have a rich literature, but a substantial part of that literature directly criticises the approach on which the rubric is built.

Table 3. Evidence types available for Section C items

Note: ✓* = evidence exists but directly contradicts the rubric’s approach (the DORA, Leiden Manifesto and Metric Tide critique for C.3.1 and C.3.2; the academic inbreeding evidence for C.1.3).

Three paradoxes

The metrics paradox. C.3.1 and C.3.2 position quantitative performance indicators, mean and dispersion comparisons and benchmarking as unquestionable tools. Binding policy documents such as the San Francisco Declaration on Research Assessment (DORA, 2013), the Leiden Manifesto (Hicks et al., 2015) and The Metric Tide (Wilsdon et al., 2015) synthesise precisely the limits and side effects of this approach and defend the centrality of qualitative expert judgement. DORA has been signed by more than 20,000 researchers and over 700 research organisations, including several Turkish universities, and the Coalition for Advancing Research Assessment (CoARA), launched in 2022, commits its signatories to a balanced qualitative-quantitative approach. The rubric’s formulations, such as “performance-based incentive and recognition mechanisms” and “competition with rivals, benchmarking with selected institutions”, maintain the traditional metrics-based paradigm.

The inbreeding paradox. C.1.3 treats academic inbreeding only at the level of “defining a transparent policy”. Yet the large-scale study by Horta, Veloso and Grediaga (2010) in Management Science shows a significant negative relationship between inbreeding and research productivity, and Inanç and Tuncer (2011) report parallel findings for Türkiye, where inbreeding rates are high by international comparison. The rubric takes a neutral stance despite this evidence.

Team science evidence left unused. C.2.2 is formulated in the right direction to support international collaboration, but does not draw on the team science literature (Wuchty, Jones & Uzzi, 2007; Adams, 2013), which shows with fifty years of data that team-based and internationally collaborative research produces significantly higher citation impact than individual research.

A further gap is the complete absence of open science, reproducibility, preregistration and FAIR data principles from the rubric, despite the UNESCO Recommendation on Open Science (2021), Plan S and the evidence from the large replication projects in psychology, economics and cancer biology that the reproducibility crisis is critical for the credibility of research. Research quality is measured only through production volume (numbers of publications and projects).

These paradoxes rest on three assumptions: that research quality can be adequately represented by quantitative indicators; that institutional policies should address structural problems (inbreeding, underfunding) only through process definition; and that evidence-based policy design is an optional luxury rather than a requirement. All three are disconnected from fifteen years of international science policy literature.

6. Section D: Social Contribution (Ceremonial Recognition)

Section D presents a fourth landscape. The rubric nominally recognises international concepts, especially the UN Sustainable Development Goals (SDGs), but does not provide the structural integration they require (operational criteria, links to appointment and promotion, sustainability indicators). With only three sub-items (about 3% of the rubric’s sub-headings), the “third mission” that the European Higher Education Area treats as a core function appears reduced to a symbolic presence.

Table 4. Evidence types available for Section D items

Note: ✓* = evidence exists but structural integration is missing (for D.2.1, the relationship between SDG indicators and responsible metrics).

Four paradoxes

The Boyer paradox. Boyer’s (1990, 1996) Carnegie Foundation work, Scholarship Reconsidered and The Scholarship of Engagement, argued that scholarship has four dimensions (discovery, integration, application and teaching) and has formed the conceptual backbone of social contribution for thirty-five years. The Carnegie Community Engagement Classification has certified more than 260 US institutions since 2006. The rubric makes no structural reference to this framework; social contribution is handled only at the level of process definition.

The SDG paradox. D.2.1 explicitly requires social contribution “aligned with the UN Sustainable Development Goals”, one of the rare points where the rubric refers directly to an international framework. However, it does not say how SDG indicators are to be operationalised or with which methodological framework (the SDSN channels, THE Impact Rankings, the sustainability dimensions in Leal Filho et al., 2020, GRI reporting standards) they are to be measured.

The internal inconsistency paradox. While C.3.1 and C.3.2 position quantitative indicators as unquestionable without the reformulation required by the responsible metrics movement, D.2.1 proposes a similar quantitative indicator system (SDG monitoring). A consistent approach would require a shift to responsible metrics in both sections.

The structural incentive gap. O’Meara (2008) and Bloomgarden and O’Meara (2007) show that social contribution cannot be institutionalised without structural incentives such as recognition in appointment and promotion. The rubric requests social contribution in Section D but builds no link with C.3.2 (individual researcher evaluation). In Türkiye, the Academic Incentive Allowance and associate professorship criteria are designed mainly around publications and projects, leaving very limited structural incentives for social contribution. The evolution of the third mission from the Triple Helix to the Quadruple and Quintuple Helix (Carayannis & Campbell, 2009; Carayannis, Barth & Campbell, 2012) is not represented at this structural level.

7. Policy Recommendations

For YÖKAK and rubric designers

1. Make epistemic justification transparent. For each item, state the type of evidence behind it (RCT, meta-analysis, systematic review, case study, expert consensus, legislation). Describing the current criteria as evidence-based would not be scientifically accurate.
2. Revise student feedback guidance (A.4.2). Reflect the validity and bias literature on student evaluations of teaching and guide institutions towards multi-method approaches, bias control and limited use of the data in high-stakes decisions.
3. Define maturity levels through outcome criteria where evidence is strong (B.2.1, B.2.2, B.3.4, B.4.2). For example, maturity level 4 for B.2.1 could be reformulated as “active learning is observed at course level and its effect on student performance is monitored in a controlled way”.
4. Reflect contested evidence in B.4.3. Add a self-determination-based clause prioritising support for intrinsic motivation, and a caution about potential side effects of rewards (Deci et al., 1999; Cameron et al., 2001; Ryan & Deci, 2000).
5. Link B.2.1 and B.3.4. Present active learning as an intervention that closes achievement gaps for disadvantaged groups (Theobald et al., 2020).
6. Rewrite C.3.1 and C.3.2 within the DORA, Leiden Manifesto and Metric Tide framework. Place qualitative expert judgement at the centre, give quantitative indicators a supporting role and support alternatives such as narrative CVs.
7. Turn C.1.3 into concrete targets. Replace “defining a transparent policy” with monitoring the inbreeding rate, encouraging external appointments and building postdoctoral capacity.
8. Add team science and open science to Section C. Monitor the share of international co-authorship, support team-based research and include open science, reproducibility and FAIR data principles.
9. Restructure Section D. Refer structurally to Boyer’s scholarship of engagement, the Carnegie Community Engagement Classification, HEInnovate and the Quadruple and Quintuple Helix; give D.2.1 operational SDG measurement frameworks within a responsible metrics approach; and redesign C.3.2, the Academic Incentive Allowance regulation and promotion criteria so that social contribution carries real weight.

For universities

• Clarify responsibility using an item-unit coverage matrix: some items are meaningful only at top management and strategy unit level, others at the operational level of academic units. Failing to make this distinction creates extra workload and weakens the legitimacy of processes.
• Expand teaching and learning centres, support department-level programme review cycles and develop digital pedagogy competence (Section B).
• Strengthen research infrastructure units (scientific research projects coordination, technology transfer offices) and train quality and promotion committees in responsible metrics (Section C).
• Prepare an institutional responsible metrics policy, report inbreeding rates transparently, set international co-authorship targets and develop internal panels supporting qualitative expert review.
• Establish a sustainability coordination office, build reporting infrastructure for THE Impact Rankings and UI GreenMetric, join the SDSN network, develop an internal community engagement certification and give social contribution explicit weight in appointment and promotion (Section D).

8. Limitations and Future Research

The critique is based on a single quality assurance document (YÖKAK Version 3.2.1); comparative international analysis, for example applying the same typology to the ESG-ENQA standards, the QAA framework and AACSB or EQUIS accreditation, would test the validity of the typology. The evidence assessment reflects a broad literature review but does not constitute a PRISMA-compliant systematic review, and it was carried out from the author’s disciplinary perspective (health management, quality management, public administration); other disciplines might emphasise different aspects. The findings are specific to Version 3.2.1 and future versions may invalidate or strengthen some of them. The unit coverage matrix reflects the typical organisational chart of Turkish public universities; foundation universities, art academies and open and distance education institutions may require adaptations.

The study offers a conceptual platform for academics in higher education policy, quality assurance, science policy, health management and public administration, a source of concrete recommendations for YÖKAK and the Council of Higher Education (YÖK), practical steps for university quality, strategic planning and promotion committees, and a methodological reference for graduate students as an applied example of evidence-based policy analysis.

How to cite

This article is a condensed English version of the technical report: Kurutkan, M. N. (2026). Yükseköğretim kalite güvencesinde kanıt-temelli eleştiri: YÖKAK Kurum İç Değerlendirme Rubriğinin (Sürüm 3.2.1) dört tipli politika açığı analizi (Technical report). Düzce University, Department of Health Management. https://www.researchgate.net/publication/404315041

Selected References

The original report cites more than 200 sources; the list below contains the works cited in this English summary.

Adams, J. (2013). Collaborations: The fourth age of research. Nature, 497(7451), 557–560.

Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25(4), 297–308.

Astin, A. W. (1993). What matters in college? Four critical years revisited. Jossey-Bass.

Black, P., & Wiliam, D. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80(2), 139–148.

Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21(1), 5–31.

Bloomgarden, A. H., & O’Meara, K. (2007). Faculty role integration and community engagement: Harmony or cacophony? Michigan Journal of Community Service Learning, 13(2), 5–18.

Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17, Article 2. https://doi.org/10.1186/s41239-019-0176-8

Boring, A., Ottoboni, K., & Stark, P. B. (2016). Student evaluations of teaching (mostly) do not measure teaching effectiveness. ScienceOpen Research, 1–11.

Bowman, N. A. (2010). College diversity experiences and cognitive development: A meta-analysis. Review of Educational Research, 80(1), 4–33.

Boyer, E. L. (1990). Scholarship reconsidered: Priorities of the professoriate. Carnegie Foundation for the Advancement of Teaching.

Boyer, E. L. (1996). The scholarship of engagement. Journal of Public Service and Outreach, 1(1), 11–20.

Cameron, J., Banko, K. M., & Pierce, W. D. (2001). Pervasive negative effects of rewards on intrinsic motivation: The myth continues. The Behavior Analyst, 24(1), 1–44.

Capp, M. J. (2017). The effectiveness of universal design for learning: A meta-analysis of literature between 2013 and 2016. International Journal of Inclusive Education, 21(8), 791–807.

Carayannis, E. G., Barth, T. D., & Campbell, D. F. J. (2012). The Quintuple Helix innovation model: Global warming as a challenge and driver for innovation. Journal of Innovation and Entrepreneurship, 1(1), 2.

Carayannis, E. G., & Campbell, D. F. J. (2009). ‘Mode 3’ and ‘Quadruple Helix’: Toward a 21st century fractal innovation ecosystem. International Journal of Technology Management, 46(3/4), 201–234.

Carrell, S. E., & West, J. E. (2010). Does professor quality matter? Evidence from random assignment of students to professors. Journal of Political Economy, 118(3), 409–432.

Davies, H. T. O., Nutley, S. M., & Smith, P. C. (Eds.). (2000). What works? Evidence-based policy and practice in public services. Policy Press.

Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–668.

DORA. (2013). San Francisco Declaration on Research Assessment. American Society for Cell Biology. https://sfdora.org

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415.

Harvey, L., & Williams, J. (2010). Fifteen years of quality in higher education. Quality in Higher Education, 16(1), 3–36.

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.

Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431.

Horta, H., Veloso, F. M., & Grediaga, R. (2010). Navel gazing: Academic inbreeding and scientific productivity. Management Science, 56(3), 414–429.

Inanç, Ö., & Tuncer, O. (2011). The effect of academic inbreeding on scientific effectiveness. Scientometrics, 88(3), 885–898.

Leal Filho, W., Salvia, A. L., Pretorius, R. W., Brandli, L. L., Manolas, E., Alves, F., Azeiteiro, U., Rogers, J., Shiel, C., & Do Paco, A. (Eds.). (2020). Universities as living labs for sustainable development. Springer.

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

Murad, M. H., Asi, N., Alsawas, M., & Alahdab, F. (2016). New evidence pyramid. Evidence-Based Medicine, 21(4), 125–127.

O’Meara, K. (2008). Motivation for faculty community engagement: Learning from exemplars. Journal of Higher Education Outreach and Engagement, 12(1), 7–29.

Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). Jossey-Bass.

Pawson, R. (2006). Evidence-based policy: A realist perspective. Sage.

Pawson, R., & Tilley, N. (1997). Realistic evaluation. Sage.

Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130(2), 261–288.

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.

Sackett, D. L., Rosenberg, W. M. C., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: What it is and what it isn’t. BMJ, 312(7023), 71–72.

Steinert, Y., Mann, K., Anderson, B., Barnett, B. M., Centeno, A., Naismith, L., Prideaux, D., Spencer, J., Tullo, E., Viggiano, T., Ward, H., & Dolmans, D. (2016). A systematic review of faculty development initiatives designed to enhance teaching effectiveness: A 10-year update: BEME Guide No. 40. Medical Teacher, 38(8), 769–786.

Steinert, Y., Mann, K., Centeno, A., Dolmans, D., Spencer, J., Gelula, M., & Prideaux, D. (2006). A systematic review of faculty development initiatives designed to improve teaching effectiveness in medical education: BEME Guide No. 8. Medical Teacher, 28(6), 497–526.

Theobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., et al. (2020). Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math. Proceedings of the National Academy of Sciences, 117(12), 6476–6483.

Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). University of Chicago Press.

Uttl, B., White, C. A., & Gonzalez, D. W. (2017). Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related. Studies in Educational Evaluation, 54, 22–42.

Wilsdon, J., Allen, L., Belfiore, E., Campbell, P., Curry, S., Hill, S., Jones, R., Kain, R., Kerridge, S., Thelwall, M., Tinkler, J., Viney, I., Wouters, P., Hill, J., & Johnson, B. (2015). The metric tide: Report of the independent review of the role of metrics in research assessment and management. HEFCE.

Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.