The Impact of Flipgrid in Students’ Learning Experience at Higher Learning Institution
Bibliographic record
Abstract
Student-generated videos have been accepted as part of interactive learning activities in the classroom. The aim of this study is to provide insights into the impact of Flipgrid, which is an interactive social learning platform for student-generated videos, on students’ learning experience. The research design follows five principles of the Scholarship of Teaching and Learning (SoTL). This study focused on how students generate their own understanding of the concepts they learnt and shared through the Flipgrid application, using generative learning theory as its underlying foundation. A total number of 117 students who enrolled in the Accounting System Analysis and Design course in a university participated in this study. Data were collected using the students’ written reflections. The findings of this study revealed that the student-generated video through Flipgrid contributed to the positive students’ learning experience. In this regard, it boosted the confidence level, improved the understanding of the topic’s content, a fun learning activity, and others, such as improved students’ video editing skills. The outcomes offer insights into how Flipgrid can be used and beneficial for the learning activity.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".