Effects of Personalized Video Feedback on Learning Among Postsecondary Students: A Matched-Case Control Study
Bibliographic record
Abstract
The purpose of this study was to evaluate the impact of personalized video feedback on academic performance in first math, chemistry and physics courses.A quasi-experimental design using a mixed-method approach with pre-test/post-test measures and control conditions was planned.Each student in the experimental group having failed an intra-semester exam received video feedback on their own exam from their teacher.For analytical purposes, each of these students was matched to a control student based on the similarity of their academic profile.The results indicate that video feedback is generally connected to better final course results compared with traditional feedback.The accessibility to learning that this practice promotes is discussed using feedback from student and teacher participants.Keywords: video feedback, college students, learning, interest, matched case-control sampling Every year, one-quarter of new post-secondary students enrolled in a science program in Quebec leave after a year of study [1, 2] compared with a dropout rate of 10% for the other programs of study.The main discouragement is the difficulty in passing courses [1, 3].In this regard, the transition from secondary to post-secondary school is a shock for many [4].Future intervention should therefore aim directly at understanding the contents to be evaluated in the courses in order to promote success in intrasemester examinations.Moreover, many science students do not dare to ask for help, mainly out of embarrassment or fear of being judged [3,5], which leaves them feeling discouraged and left to their own devices.Intervention that focuses on learning through benevolent feedback, given without the student's request, may be beneficial for some.Cormier and Pronovost [1] identified another important withdrawal factor: a loss of interest in science by students in the program.Consequently, this study aims (1) to develop a pedagogical practice that allows for personalized feedback at the first sign of difficulty, without students having to ask for it, and to ensure that this help remains accessible according to the needs of each student; and (2) to evaluate the effectiveness of this practice with regard to the performance of first-year science students.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".