Applying Video Modeling to Promote the Handwriting Accuracy of Students with Low Vision Using Mobile Technology
Bibliographic record
Abstract
Introduction: In Taiwan, although many school-aged students with low vision struggle with poor handwriting, there is a lack of evidence-based educational practices for handwriting enhancement. Since the use of mobile technology has increased recently, iPads have been identified as an effective tool to deliver video-based instruction to individuals. In this study, iPads were used to deliver video modeling to provide instruction designed to enhance the handwriting accuracy among students with low vision. Methods: A multiple-baseline-across-participants-probe design was used in this study to assess the success of the intervention with three individuals with low vision who were 9, 12, and 14 years of age. Results: During the baseline phase, the accuracy for each of the participants was under 50%. After intervention, their handwriting accuracy increased. The participants were also able to maintain their performance during follow-up sessions. Moreover, the study demonstrated good social validity, since the stakeholders all indicated a high level of treatment acceptability for this intervention. Discussion: The outcome of this study demonstrates that iPads used with video modeling can lead to improved accuracy in handwriting for students with low vision. The findings also support video modeling as an effective strategy for teaching new skills and may be applied to students with different special learning needs. Implications for practitioners: Video modeling is an effective and feasible instructional strategy for practitioners as it can be easily implemented. Additionally, given its built-in visual support, the iPad is an effective instrument that can help students with low vision reach a higher potential for handwriting accuracy. It is worth noting that, in addition to the presence of video modeling, proper instruction, as well as opportunities to practice, is needed for students to produce accurate word formation. Thus, short daily practices combined with the intervention method presented in this study are likely to gain better results for students with low vision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".