Using the mTSES to Evaluate and Optimize mLearning Professional Development
Bibliographic record
Abstract
The impact of targeted professional development activities on teachers’ perceptions of self-efficacy with mobile learning remains understudied. Power (2015a) used the Mobile Teacher’s Sense of Efficacy Scale (mTSES) survey instrument to measure the effects of a mobile learning themed professional development course on teachers’ confidence with and interest in mobile learning. The current study looks at changes in perceptions of self-efficacy amongst participants in another open course about mobile learning called Instructional Design for Mobile Learning (ID4ML), which took place from May 4 – June 6, 2015 (Power, Bartoletti & Kilgore, 2015). The purpose of this study is to verify the reliability and construct validity of the mTSES instrument developed by Power (2015a, 2015b) and Power, Cristol and Gimbert (2014), and to explore trends in self-efficacy changes amongst a more diversified participant population. This paper reports on the findings from the analysis of data collected using the mTSES tool. The findings provide useful feedback on the impacts of participating in the ID4ML course. They also provide further support for the utility of the mTSES instrument as a measure of perceptions of self-efficacy with mobile learning. These findings point to the potential utility of the mTSES as a tool for both planning and evaluating mLearning professional development training for teachers.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".