11. Learning Portfolios: Creative Connections Between Formal and Informal Learning
Bibliographic record
Abstract
How do you know what students in your course “took away” with them? Why not ask? Through a learning portfolio assignment, I invited students to show: how they met the course objectives; connections they made to other courses as well as aspects of their lives; and their views and perspectives about the course material and processes. They were asked to include tangible evidence, examples, connections, and reflections from all class sessions, discussions, and other assignments. They were also required to express themselves through a creative variety of styles and formats, including a concept map and a world map. What were their reactions to the assignment? Many noted that it encouraged them to think critically and that it was a fun way to show the links between the course and their own lives. Might you like to use or adapt some or all parts of my learning portfolio assignment in a course (any discipline) that you teach? Through reading this paper and trying the described activities, you will have completed your own mini-learning portfolio and explored methods of assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".