Learning Portfolios as Means of Evaluating Futures Learning: A Case Study at Renaissance College
Bibliographic record
Abstract
This article evaluates a particular classroom improvement project. It contributes to answering three questions: (1) Does adding the (personal) futures perspective to our course change how learners think about and plan for the future? (2) Does an integrated learning portfolio help evaluating learners’ foresight capacity? (3) How can we know the answers to questions 1 and 2? I use the case study approach—describing our “teach the future” experience within an undergraduate course at a Canadian University—and a (computer aided) content analysis to evaluate the effectiveness of adding core elements of (personal) futures learning to an existing course. The results will be of interest to others who wonder whether “teaching the future” makes a difference in building foresight capacity. In particular, readers can glean the potential value of learning portfolios for this purpose. First, I describe the case study and how futures learning fits into this context. Second, I provide an overview of the course “RCLP 3030 Integrated Learning Portfolio” including the course outcomes, assessment, and futures-related content. Third, I describe the actual run of the course and how learners engaged with the material; this includes learners’ contributions to the online discussions that will help evaluate the learning that takes place and the effectiveness of the course design. Fourth, with the help of computer-aided content analysis I analyze the learning portfolio submissions of all learners at the end of the course. Fifth, I provide an evaluation summary, discuss next steps, and offer recommendations of general interest.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".