Examining the effects of digital devices on students’ learning performance and motivation in an enhanced one-to-one environment: a longitudinal perspective
Bibliographic record
Abstract
The electronic schoolbag (eSchoolbag) is an enhanced one-to-one computing environment compatible with various digital devices. The eSchoolbag provides instructors with an intelligent teaching management system. It also offers students a highly interactive learning environment. However, findings of students’ performance in the eSchoolbag environment are mixed due to the great variety of digital devices. Moreover, the research on the temporal change in students’ motivation in the eSchoolbag environment is still in its infancy. This study addressed these gaps by examining the longitudinal changes in students’ performance and motivation in two types of eSchoolbag environments: a computer class and a tablet class. A total of 102 students participated in this study for one year. Findings from this research suggested that the tablet class performed better than the computer class, and students’ utility value and cognitive cost towards the eSchoolbag changed over time. These findings inform the successful implementation of eSchoolbag programmes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".