An Exploration of Students’ Learning Motivation and Level of Participation through the Use of Mobile Tech in Classrooms
Bibliographic record
Abstract
This paper aims to combine Mobile Tech (Modible Technology) and the SAMR (Substitution, Augmentation, Modification, Redefinition) model to create suitable teaching materials to enhance students’ learning motivation and classroom participation. This course, “Classroom Management”, is a compulsory junior-year course. The department’s policy is to teach all compulsory courses in English only. However, students’ English ability may not be high enough to absorb teaching content successfully. In addition, with the availability of cellphones, students tend to become distracted easily if they have no access to their phones during class. It is apparent that the traditional teaching methods of using PPT and paper-based worksheets are not receiving enough attention from students. To enhance learning effectiveness and learning motivation, this study aims to design a course and relevant teaching materials with Mobile Tech following the SAMR model. The SAMR model by Dr. Roben Puentedura (2006, 2016) refers to using technology to perform substitution, augmentation, modification, and redefinition of the original teaching materials or activities. Following this model, this study hopes to design teaching materials combined with Mobile Tech that could better enhance students’ learning motivation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".