Using technology to encourage self-directed learning
Bibliographic record
Abstract
The rapidly-developing 21st century world of work and knowledge calls for self-directed lifelong (SDL) learners. While higher education must embrace the types of pedagogies that foster SDL skills in graduates, the pace of change in education can be glacial. This paper describes a social annotation technology, the Collaborative Lecture Annotation System (CLAS), that can be used to leverage existing teaching and learning practices for acquisition of 21st Century SDL skills. CLAS was designed to build upon the artifacts of traditional didactic modes of teaching, create enriched opportunities for student engagement with peers and learning materials, and offer learners greater control and ownership of their individual learning strategies. Adoption of CLAS creates educational experiences that promote and foster SDL skills: motivation, self-management and self-monitoring. In addition, CLAS incorporates a suite of learning analytics for learners to evaluate their progress, and allow instructors to monitor the development of SDL skills and identify the need for learning support and guidance. CLAS stands as an example of a simple tool that can bridge the gap between traditional transmissive pedagogy and the creation of authentic and collaborative learning spaces.
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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 | 0.004 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".