Autonomy, Competence, Relatedness: Evaluating a Course Through Self-Determination Theory

In brief

Self-determination theory (SDT) argues that students learn best when a course supports three basic psychological needs: autonomy (a sense of volition and ownership), competence (feeling effective and able to succeed), and relatedness (feeling connected to teachers and peers). Meta-analytic evidence links support for these needs to autonomous motivation, and autonomous motivation to persistence, well-being and achievement. Building a handful of need-support items into a course evaluation reframes the instrument from "was the teacher good?" — a global judgement vulnerable to charisma and halo effects — to "did this course create the conditions that motivate learning?", which is both more diagnostic and more closely tied to the outcomes quality assurance ultimately cares about.

What the research says

SDT, developed over four decades by Edward Deci and Richard Ryan, distinguishes autonomous motivation (acting out of interest, value or identity) from controlled motivation (acting out of pressure or reward), and holds that the former is fostered when the social environment satisfies the needs for autonomy, competence and relatedness. Ryan & Deci (2000), in American Psychologist, set out the core framework; Ryan & Deci (2020), in Contemporary Educational Psychology, review its application to education, concluding that both intrinsic and well-internalised extrinsic motivation predict a range of positive outcomes across education levels and cultures, and are enhanced by need support — while controlling, pressuring environments undermine them.

The quantitative backbone comes from meta-analysis. Bureau, Howard, Chong & Guay (2022), in Review of Educational Research, synthesised 144 studies covering more than 79,000 students to map the antecedents of autonomous and controlled motivation. They found that need satisfaction predicts self-determined motivation, and — notably — that teacher autonomy support predicts students' need satisfaction and self-determined motivation more strongly than parental autonomy support, underscoring how much the instructor and course design matter. Broader syntheses report that competence tends to be the strongest positive predictor of self-determined motivation, followed by autonomy and then relatedness, though all three contribute and their relative weight varies by context. Intervention meta-analyses further indicate that need-supportive teaching can be trained and that doing so improves motivational and learning outcomes.

For evaluation, the key point is that SDT identifies mechanisms, not just impressions. "The teaching was clear" is a perception; "I had genuine choices in how I approached the work" (autonomy), "the course was structured so I could succeed with effort" (competence), and "I felt I belonged in this class" (relatedness) are appraisals of the conditions that motivation research shows actually drive engagement and learning.

Why it matters for course evaluation in practice

Conventional student evaluation of teaching is dominated by global quality and satisfaction items, which are well documented to be contaminated by instructor charisma, warmth and presentation fluency — factors that inflate ratings without necessarily improving learning. SDT-based items shift the focus from the instructor's persona to the course's conditions. That reframing has three practical advantages.

First, it is diagnostic and prescriptive. A low autonomy score points to specific design levers — offering meaningful choices, providing rationales for requirements, reducing needless control. A low competence score points to scaffolding, pacing, formative feedback and assessment clarity. A low relatedness score points to community, belonging and teacher-student interaction. Unlike "overall satisfaction 3.4," each need maps to a known, trainable set of teaching practices.

Second, it is outcome-relevant. Because need support predicts autonomous motivation, and autonomous motivation predicts persistence and achievement, a need-support profile is a plausible leading indicator of the outcomes an institution is accountable for — more so than a satisfaction score whose link to learning is weak and contested.

Third, it is equity-aware. Relatedness and belonging are unevenly distributed; students from under-represented groups often report lower belonging, and a course that scores well on average teaching quality can still be failing to include part of its cohort. Measuring relatedness explicitly surfaces that gap where a satisfaction mean hides it. This matters for European quality frameworks that increasingly expect programmes to evidence inclusion and belonging, not merely aggregate satisfaction: a need-support profile disaggregated by student group provides exactly the kind of targeted, actionable evidence a review panel looks for, and it does so in the language of established motivation science rather than a bespoke local metric that an external assessor has no way to interpret or benchmark.

Limitations and honest caveats

A rigorous reader will not accept SDT as a turnkey replacement, and several caveats are important. Self-reported need satisfaction is still perception, correlated with mood, prior interest and achievement; a student who is doing well may report more competence support partly because they are succeeding, creating a reverse-causality risk that cross-sectional evaluation cannot resolve. Constructs can blur into existing items — "competence support" overlaps with clarity and feedback, "relatedness" with instructor warmth — so poorly written need items risk simply relabelling what evaluations already (imperfectly) measure, including the warmth halo SDT is meant to see past. Cultural variation is real: the expression and even the weighting of autonomy differ across cultures, so need-support items require measurement-invariance testing before scores are compared across international cohorts. The evidence base is largely correlational and motivation-focused; while intervention studies support causal claims about need-supportive teaching, the leap from a course's need-support ratings to its measured learning gains is an inference, not a direct measurement. Finally, adding a validated SDT scale lengthens the survey, and — as with any framework — measuring need support changes nothing unless the institution acts on it.

How Koji incorporates this

Koji is designed to operationalise the three needs as concrete, actionable evaluation content rather than leaving them buried inside a generic "teaching quality" score.

• Need-focused structured items. Koji supports scale, single_choice and yes_no questions, so a compact, theory-grounded set of autonomy, competence and relatedness items can be fielded without a full battery — for example, one or two items per need targeting choice/rationale, scaffolding/attainability, and belonging/connection.
• Conversational probing of the mechanism. Because need support is about specific conditions, Koji's AI-moderated conversational interview can follow a low rating with an open_ended probe — asking what removed a sense of autonomy or where a student stopped feeling they could succeed — surfacing the design lever behind the number.
• Thematic analysis mapped to the three needs. Koji's automatic thematic analysis can organise open-text feedback under autonomy, competence and relatedness, converting free comments into a need-support diagnosis that maps to known teaching practices.
• Equity-aware, subgroup reporting. Koji's reporting compares responses across cohorts and subgroups, so a relatedness or belonging gap affecting part of the class becomes visible rather than being averaged away — the exact blind spot SDT warns about.
• Formative, mid-cycle use. Because need support is a leading indicator, Koji's mid-cycle collection lets a course check autonomy, competence and relatedness while there is still time to adjust, rather than only at the end.

These capabilities are intended to help institutions measure and act on need support, not to certify a formal SDT research administration; validated multi-item SDT scales with invariance testing remain the standard for research claims. Koji's core research platform at koji.so applies the same AI-moderated interview engine to customer and product research, where understanding the conditions behind motivation and behaviour is an equally central goal.

Frequently asked questions

How is an SDT-based evaluation different from a normal teaching-quality survey? A quality survey asks for a global judgement of the instructor, which is vulnerable to charisma and halo effects. SDT items ask about the course's conditions — autonomy, competence and relatedness support — which motivation research links more directly to engagement and learning.

Which of the three needs matters most? All three contribute, but syntheses generally find competence to be the strongest positive predictor of self-determined motivation, followed by autonomy and then relatedness. The relative weight varies by context, so all three are worth measuring.

Does the instructor really move these needs, or is it mostly the students? Bureau, Howard, Chong & Guay (2022) found that teacher autonomy support predicts students' need satisfaction and motivation more strongly than parental support, indicating the instructor and course design have substantial leverage.

Can I compare SDT scores across international cohorts? Only with care. The expression and weighting of autonomy in particular vary across cultures, so need-support items should undergo measurement-invariance testing before scores are compared across groups.

Will measuring need support prove my course improved learning? No. The link from need support to learning runs through motivation and is largely correlational in evaluation settings. Treat a need-support profile as a strong leading indicator and a diagnostic guide, not as a direct measurement of learning gains.

Related resources

• Beyond the Lecturer: What the Course Experience Questionnaire (CEQ) Measures
• Is Your Course Pushing Students Toward Deep or Surface Learning? The R-SPQ-2F
• SALG: Can Students Reliably Report Their Own Learning Gains?
• Teacher Clarity Predicts Learning Better Than Charisma
• Stop Asking "Was the Lecturer Clear?": The Case for Low-Inference Items
• What Do Student Evaluations Actually Measure? Marsh, the SEEQ, and Multidimensional Feedback

References

• Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
• 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. https://doi.org/10.1037/0003-066X.55.1.68
• Bureau, J. S., Howard, J. L., Chong, J. X. Y., & Guay, F. (2022). Pathways to Student Motivation: A Meta-Analysis of Antecedents of Autonomous and Controlled Motivations. Review of Educational Research, 92(1), 46-72. https://doi.org/10.3102/00346543211042426