Measuring students’ approach to learning and the development of higher order thinking skills in a large university class
Bibliographic record
Abstract
The Canadian higher education system is currently structured such that class sizes are large4 with limited resources, which have imposed constraints on teaching and assessment methods3. Traditional lecture delivery and multiple choice exams are common in large classes, despite evidence to suggest that these methods display a negative relationship with students’ development of higher order thinking skills (HOTS), such as problem solving, critical and creative thinking4. Furthermore, such traditional teaching and assessment methods typically reward memorization and recall, consistent with a surface approach to learning5, rather than encouraging students to deeply process the learning material.\nCognizant of the constraints imposed on teaching and assessment methods by large class sizes, an upper year physiology course has recently been redeveloped. Briefly, this course now takes place over two 12-week semesters, where students are taught with instructional scaffolded lecture methods. Assessments consist of short and long answer midterms/final exam, and a large scale (~400 students) weekly tutorial where students work in groups on problem solving assignments.\nIn this presentation, we will share how we evaluated students’ approaches to learning within a large, upper year physiology class, using the Revised Two Factor Study Process Questionnaire1, coupled with students’ academic performance on lower order and higher order assessment questions, according to Bloom’s Taxonomy2. Interesting data points from this study will be shared. By the end of this presentation, participants will be able to: 1. Identify teaching and assessment methods to promote HOTS and a deep approach to learning; 2. Implement a tool in their classes to evaluate students’ approaches to learning.\nReferences\n1 Biggs, J.B., Kember, D., & Leung, D.Y.P. (2001). The Revised Two Factor Study Process Questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71, 133-149.2 Bloom, B. S., Krathwohl, D. R., and Masia, B. B. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals, New York, NY: D. McKay. 3 Kerr, A. (2011). Teaching and Learning in large Classes at Ontario Universities: An Exploratory Study. Toronto: Higher Education Quality Council of Ontario.4Ontario Confederation of University Faculty Associations. (2014). Data check: Class sizes continue to grow at Ontario's universities. Retreived from http://ocufa.on.ca/blog-posts/data-check-class-sizes-continue-to-grow-at-ontarios-universities/5Trigwell, K., & Prosser, M. (1991). Relating approaches to study and the quality of learning outcomes at the course level. British Journal of Educational Psychology, 61(3), 265-275.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".