Evaluating the use of a Personalized Learning Management System to Increase Student Enrollment in High School Physics (Evaluation, Diversity)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».