Pedagogy for Conceptual Thinking in the Digital Age: Enhancing Learning Outcomes with Meaning Equivalence Reusable Learning Objects (MERLO) Formative Assessments
Bibliographic record
Abstract
[EN] The research presented in this paper is the fruit of an ongoing international \ncollaboration with the goal of enhancing students learning outcomes by \nimplementing and sharing a novel pedagogy for conceptual thinking, and use \nof an innovative didactical and methodological tool: Meaning Equivalence \nReusable Learning Objects (MERLO) that provide student-centered, weekly \nformative assessments for exploring and discussing conceptual situations in \nsmall groups. It was developed, tested, and implemented in Canada at \nUniversity of Toronto and Ryerson University, as well as in Israel, Italy, \nRussia, and Australia, in different knowledge domains, including: physics; \nbiology; mathematics; mathematics teacher education; teacher training; \ndevelopmental psychology; English as a second language; architecture; \nmanagement; business; project management. Statistical analysis of MERLO \ndata collected since 2002, shows that conceptual thinking enhance learning \noutcomes and deepens students’ comprehension of the conceptual content of \nlearned material. Conceptual thinking is learnable, and provide metrics to \ndocument continuous increase in higher-order thinking skills such as critical \nconceptual thinking, transfer of knowledge, and problem solving. Pedagogy \nfor conceptual thinking is currently implemented with Brightspace \n(http://www.brightspace.com/), Integrated Learning Platform (ILP) offered \nby D2L (http://www.d2l.com/) that supports customizable online pedagogy.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".