Impact of blended learning approach on students’ achievement and mental effort
Bibliographic record
Abstract
Most researchers are interested in investigating the impact of a blended learning approach (BLA) on students’ performance, yet this approach’s instructional efficiency has not yet been quantified. Therefore, this research aims to determine the impact of teacher-created online Moodle-based materials in combination with face-to-face learning on student achievement and mental effort, that is, to determine the instructional efficiency of applied teaching approaches at physics classes in high school. For this research, we chose to teach students physical principles of direct current, which involves abstract concepts. Using BLA, students can prepare better for a real experiments in the lab, and this approach also creates a safe environment for students while allowing them to demonstrate the learned physical phenomena. The Moodle platform course is developed for this purpose and implemented in a BLA environment. Students are gradually guided from easier to more difficult concepts in this research, considering the working memory’s capacity and the teaching material requirements. Our results show that the students who used BLA achieved higher scores on the knowledge test, and they also used less mental effort than students that used a conventional teaching approach. We also show that instructional efficiency for BLA is positive and significantly higher than the instructional efficiency of the conventional approach. This research’s results are primarily designed for physics teachers to better understand the effects of the BLA and apply teaching approaches that respect the principles of children’s cognitive development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".