The teaching and learning of mathematics at university level : an ICMI study
Bibliographic record
Abstract
Preface. Section I: Introduction. Why the Professor Must be a Stimulating Teacher: Towards a New Paradigm of Teaching Mathematics at University Level C. Alsina. Changing Contexts in Tertiary Mathematics: Implications for Diversity and Equity R. Zevenbergen. Policy Issues J. Thomas. Policy Case Studies. Policy Issues Concerning Teaching at University Level in France J.-L. Dorier, V. Durand-Guerrier. Mathematics Education in Chinese Universities X. Longwan. Policy in Sweden A. Tengstrand. Section 2: Practice. Trends in Curriculum: A Working Group Report J. Hillel. Mathematical Teaching Practices at Tertiary Level: Working Group Report J. Mason. The Secondary-Tertiary Interface L. Wood. The Warwick Analysis Project: Practice and Theory L. Alcock, A. Simpson. Professional Development for Changing Undergraduate Mathematics Instruction H. Keynes, A. Olson. Scientific Debate in Mathematics Courses M. Legrand. Making Large Lectures Effective: An Effort to Increase Student Success K. Millett. University Mathematics Based on Problem-Oriented Student Projects: 25 Years of Experience with the Roskilde Model M. Niss. The Active/Interactive Classroom D. Smith. Departmental Profiles. Concordia University, Montreal, Canada J. Hillel. Eidgenossische Technische Hochschule, Zurich, Switzerland U. Kirchgraber. Universidad Nacional Del Literal, Santa Fe, Argentina N. Aguilera, R. Marcias. Universiti Teknologi Malaysia, Malaysia. University of Joensuu, Finland M. Pesonen. Section 3: Research. What Can We Learn from Educational Research at the University Level? M. Artigue. Purposes and Methods of Research in Mathematics Education A. Schoenfeld. TertiaryMathematics Education Research and its Future A. Selden, J. Selden. Research into the Teaching and Learning of Linear Algebra J.-L. Dorier, A. Sierpinska. APOS: A Constructivist Theory of Learning in Undergraduate Mathematics Education Research E. Dubinski, M. McDonald. Research on the Teaching and Learning of Calculus/Elementary Analysis A. Robert, N. Speer. Section 4: Mathematics and Other Disciplines. Revolution by Stealth: Redefining University Mathematics L. Steen. Mathematics and Other Subjects J.-P. Bourguignon. Trying the Impossible: Teaching Mathematics to Physicists and Engineers B. Kummerer. Do Not Ask What Mathematics Can do for Modelling. Ask What Modelling Can do for Mathematics! J. Ottesen. Section 5: Technology. Technology: A Working Group Report K. King, et al. Technology in College Statistics Courses J. Garfield, et al. Computer Algebra Systems in the Learning and Teaching of Linear Algebra: Some Examples J. Hillel. Reflections on the Sustained Use of Technology in Undergraduate Mathematics Education E. Muller. Finding a Role for Technology in Service Mathematics for Engineers and Scientists P. Kent, R. Noss. Section 6: Assessment. Assessing Undergraduate Mathematics Students K. Houston. Assessing Mathematical Thinking Via FLAG J. Ridgway, et al. Assessing Student Project Work C. Haines, K. Houston. Section 7: Teacher Education. Preparation of Primary and Secondary Mathematics Teachers: A Working Group Report H. Williams. Using Research to Inform Pre-Service Teacher Education Programmes T. Cooney. Mathematicians and the Preparation of Elementary Teachers C. Kessel, L. Ma. Mathematics Teachers' Education in France: From Academic Training to
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".