The integration of print and digital content for providing learners with constructive feedback using smartphones
Bibliographic record
Abstract
Abstract Timely feedback is considered essential for supporting professional growth and personal development. However, it is difficult to employ such feedback in traditional learning environments. Recently, smartphones are considered as educational tools for supporting instructional activities. Therefore, this study attempts to leverage the advantages of physical textbooks and mobile devices. A pedagogical strategy called constructive feedback was proposed to provide learners with real‐time and personalized suggestions according to the results of electronic assessment. Two types of connectivity techniques, namely QR C odes and hyperlinking, were applied for integrating printed materials and digital content. An experiment was conducted in a university course entitled C omputer N etworks, and a total of 80 students were recruited to participate in this experiment. The findings revealed that the strategy of constructive feedback had a significant influence on learning performance. However, no significant differences on learning performance were found between using QR C odes and using hyperlinking. Finally, implications of the findings were discussed for further research directions and practical applications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".