Student Involvement in Choice of Work in Progress: Course Activities and the Impact on Student Experience
Notice bibliographique
Résumé
Abstract Student involvement literature suggests that offering students choices in course activities can improve their experience for several reasons: 1. Students take ownership and bring their individual learning styles to their courses, 2. The design of the course shifts from teachers as designers to students as partners in their learning process, 3. Students engage with the course, instructors, and peers at a psychological level i.e., they are motivated, 4. Students interact and engage more with the content, instructors, and peers, and learn better, and 5. Students report satisfaction with course experience (Applicant, 2022; NSSE, 2018; Douglas et al., 2006; Razinkina et al., 2017). This project leverages a mandatory teaching assistant training program to explore the effects of choice of activities on the student experience as measured by student learning, course engagement and satisfaction. Quantitative analysis of surveys and course performance, as well as qualitative analysis of student and instructor reflections, will be used to create a professional development workshop for Engineering instructors who wish to strategically integrate meaningful choice of activities into their course designs. The research project underway has baseline data collected in the Fall 2022 and the intervention data to be collected in Winter 2023. The course offering is a two-week Teaching Assistant (TA) training program, which is a mandatory hiring requirement for teaching assistantships in the Faculty of Engineering (FOE). TA training in FOE is a pass/fail course and includes measurable deliverables such as pre-post quizzes, discussion posts, surveys, and open-ended responses to pass and receive a certificate. In Fall 2022, students enrolled in the TA training (n=364) are considered the 'control group' (fixed activities) having received asynchronous online content, quizzes, weekly activities, and surveys. Similarly, in Winter 2023, students enrolled in the training will be assigned as an 'intervention group' (choice of activities) to receive comparable asynchronous online content, and quizzes, with the main intervention being weekly activities governed by student choice. Both the control group (fixed activities) and the intervention group (choice of activities) will have student learning and student experience assessed via pre-training and post-training quizzes (to measure content learning) and a survey (to measure course engagement and satisfaction). The study received ethics clearance from the University Ethics Committee. After the Fall 2022 course was complete and grades uploaded, students were sent an initial invite and a personalized link with a consent form to access their coursework for research purposes, and a student engagement and satisfaction survey. Participation in the study is voluntary and had no impact on their opportunity to earn credit in the TA training as the call to participate went out after the course was completed. The same call for participation process will be repeated in the Winter 2023 offering of the TA training. Based on the findings from student responses and interviews with the TA training instructors, a professional development workshop will be created to share insights on the student-chosen activities as a pedagogical approach for meaningful student involvement and to facilitate students as partners in their own learning.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».