Developing and Assessing Lifelong Learning Skills through a Self-Determined Learning Approach
Bibliographic record
Abstract
In a Bachelor of Engineering program, educators have developed learning activities and assessment techniques that promote self-directed learning and improve students’ lifelong learning and investigation skills. Inspired by heutagogy and transformative learning theory, two analytical assignments and one annotated bibliography were designed to foster autonomy, resilience, self-efficacy, and motivation, and to accurately assess the associated graduate attributes. To complete analytical assignments, students study peer-reviewed papers on electrostatic transduction and piezoelectricity. They analyze microelectromechanical structures presented in the papers using classical physics and verify their results through computational methods. For the annotated bibliography, students review and assess critically peer-reviewed papers to gain in-depth knowledge in the field of Microelectromechanical Systems (MEMS), particularly sensors, and discuss how the current research contributes to the technology advancements. The developed tasks allow students to reflect, think metacognitively, and adapt learning strategies to address their educational needs. Beyond their role in competency development, these assignments serve as authentic assessments for evaluating graduate attributes, with a particular focus on Life-long Learning.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".