Bringing First Year Engineering Students To Reflect On Their Learning Strategies Via A Learning Journal And An End Of Semester Essay In The Context Of A Problem Based And Project Based Learning Curriculum
Bibliographic record
Abstract
This paper reports on a qualitative appraisal of the ability of first-year engineering students to engage in a metacognitive process about their learning strategies. At the beginning of the semester, texts on learning strategies, reading, concept mapping, emotional competencies, change and stress were distributed to each student and discussed in the classroom. We emphasized the importance for students not only to monitor their performance during the semester but also to look back on their learning strategies and, if necessary, to improve them. To that end, we asked students to periodically write in a personal learning journal their thoughts about their learning strategies. As an incentive, we told them that, as one of their final exams, they would have to write a 7 to 10-page essay about their learning strategies. They were also informed that they would be graded according to their ability to analyse their strategies, whether these strategies were optimal or not. Results of a preliminary analysis of these essays confirm that it is possible to bring first-year engineeering students to reflect on their learning strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".