ENGAGEMENT WITH THE INVERTED CLASSROOM APPROACH: STUDENT CHARACTERISTICS AND IMPACT ON LEARNING OUTCOMES
Bibliographic record
Abstract
The focus of this paper is to take a closer look at this question of engagement with the inverted classroom approach and specifically address the following two research questions: 1) Do students with different degrees of engagement with the inverted classroom approach exhibit different academic characteristics?, and 2) How does the degree of engagement affect the learning of the course material?To assess these questions, the students preferred learning styles (ILS), their self-efficacy, and their academic performance prior to the course were assessed. As well, the students’ engagement with the approach was assessed through their lecture attendance and pre-class video viewing, and their learning was quantified through pre/post concept tests, in-class pop quizzes, and the final course grade.Using k-means clustering with the pre-class viewing and lecture attendance data, the cohort was divided into three groups: high, medium, and low degrees of engagement with the teaching approach. Some differences were noted in terms of the groups’ learning styles and prior academic performance, but no differences were found with their self-efficacy scores. Students in the high engagement group did significantly better than their peers in the other two groups, with final course mark averages of Mhigh = 81.8%, Mmedium = 74.0%, Mlow = 63.5%, F(2,323) = 67.4, p < .001. When prior academic performance and learning styles were controlled for, in comparison to the low engagement group, being part of the medium or high engagement group was a significant positive predictor for the final course grade, with medium = 0.168, p < .01, high = 0.349, p < .001.Since the inverted classroom approach requires a major shift in student attitudes and behaviors towards their learning, these results show that the degree of engagement with the process is an important metric to consider.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".