Examining the use of a Personalized Learning Management System (PLMS) to Increase Student Engaagement in High School Physics
Bibliographic record
Abstract
Motivated by the drive to impact the quality and diversity of students applying to engineering schools, this study evaluates a component of a Personalized Digital Learning Management System (PLMS) that has been designed to increase student engagement in K-12 Physics. In particular, a non-traditional project based learning module, with roots in game-based learning, has been developed and executed in grade 8 science classrooms. Pre and post survey data that includes attitudinal markers, learning style profiles, gender, and assessments of knowledge gained, are analyzed and presented. Results suggest that students who are more interested in science, physics and engineering tend to have learning styles that require programming that is more active and less sequential than traditionally delivered. This is particularly the case for female students. The non-traditional game based project acted to provide these types of learning opportunities and post survey data showed a very high level of student engagement. Results obtained will be used to further refine the PLMS.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".