Evaluating the use of a Personalized Learning Management System to Increase Student Enrollment in High School Physics (Evaluation, Diversity)
Bibliographic record
Abstract
Abstract Consistent with the majority of provinces across Canada, the enrollment in senior level high school physics in Alberta has significantly lagged behind enrollment in commensurate chemistry and biology classes. Furthermore, there is significant gender disparity in high school physics classrooms; the majority of students are male. Since engineering faculties depend on these students for their enrollment, to foster diversity in their own student population, engineering outreach programs are often aimed at providing university led in-class presentations / demonstrations in K-12 classrooms. Although met with some anecdotal success, enrolment issues may be more effectively addressed by engineering academics if their efforts were directed toward providing research support in the evaluation of educational tools that may support school teachers in delivering content themselves in a manner that appeals to their daily instructional needs and to the learning needs of the diverse student population. An award winning personalized learning management system (PLMS) developed by leading STEM not-for-profit organization is under investigation. This system is a personalized curriculum based hypermedia instructional tool for K-12 educators and students. Being digitally based, it has the potential for rapid integration into classrooms. The tool appeals to digital natives (students), and incorporates: mind mapping (discovery based learning), experts on call, gamification, all integrated through teacher views that produce dynamic project-based lesson plans. The system encourages an interdisciplinary approach that requires students to draw on multiple subject areas simultaneously to solve real world problems. Previous research conducted by the authors has indicated that in the context of learning style models, the PLMS provides a balanced approach to learning and therefore should be a very useful learning tool in the physics curriculum. This study will first present the results of attitudinal and learning style surveys that were conducted in local junior high schools that correlate learning style profiles, gender, and attitudes towards Physics. Informed by these results, specific physics modules designed by the authors have integrated into the PLMS and are used to enhance learning in targeted junior high school classrooms. Pre and post survey data that includes attitudinal markers, learning style profiles, gender, and assessments of knowledge gained, will be analyzed and presented. It is expected that these results will be used to further refine the PLMS. Ultimately, the potential to improve the engagement in physics by a more diverse student group by the adoption of the PLMS in the classroom will be highlighted.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".