Student Involvement in Choice of Work in Progress: Course Activities and the Impact on Student Experience
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".