Digital learning tools in the classroom: comparing their impacts on student motivation to learn and academic achievement in post-secondary students from Canada, Germany, and India
Bibliographic record
Abstract
Use of digital learning tools (DLTs) in classrooms has markedly increased since the COVID-19 pandemic began. Concerns exist that some DLTs were integrated without careful consideration of their impacts on student motivation to learn and/or academic achievement. Moreover, differences in student demographic profiles and the learning environment may also impact potential relationships. We surveyed post-secondary students from Canada, Germany, and India to determine if DLT use, effectiveness, and/or mode of course delivery differed across jurisdictions, and if any relationships exist between use of different types of DLTs and student GPA. Results indicate that although students from all countries examined preferred classes utilising particular types of DLTs, increased use of DLTs did not improve academic achievement. Nevertheless, it is crucial that DLTs continue to play key roles in modernising our pedagogical approaches given their impacts on course satisfaction and general appeal to the sensibilities of today’s 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".