Student Behaviour and Performance in Relation to Interaction with On-line Activities in a Postsecondary Music Course
Bibliographic record
Abstract
This study is an extension of previous research on the infusion of technology into a postsecondary music course to promote the skill of close-listening of music.Due to many in-class hindrances (e.g., time, equipment, acoustics, class size) students in postsecondary music courses do not often experience quality listening opportunities to be able to detect important musical elements.For this study, we developed on-line, supplemental listening activities using Articulate Storyline, Adobe Connect and the virtual world OpenSim.We pretested students on music experience, computer experience and level of self-regulation.At the end of the course, students answered a survey on their enjoyment, tendency to recommend, engagement, perceived increase in understanding of material and whether the activities were worthwhile.In a comparison of 2014 and 2015 results, we found that students with high selfregulation levels rated the above items more positively when the content included more advanced musical concepts.In addition, we found that students who interacted with the supplemental on-line activities and materials achieved higher grades than those who did not.Students who accessed the comprehensive on-line activities close to when the material was presented in class performed better than those who waited until exam time.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".