Doodly-Based Multimedia Instructional Intervention and Academic Achievement of Undergraduate Students with Learning Disabilities in Educational Technology
Bibliographic record
Abstract
Traditional instructional strategies may not provide the essential elements necessary for students with learning disabilities (LDs) to learn effectively. This ultimately leads to decreased motivation and underachievement. Since Doodly-based multimedia has been scientifically proven to enhance learning outcomes, one wonders if such effects could be replicated on the academic achievement of students with LDs. The study explored the impact of Doodly-based multimedia instructional intervention (DBMII) on the academic achievement of undergraduate students with LDs in Educational Technology (EdTech). The research employed a 2x2 quasi-experimental factorial design, with pre- and post-tests to explore the effects of DBMII. The census sampling technique was used to draw a sample of 38 (22 males and 16 females) third-year special education students with confirmed cases of LDs. The data collection used the Educational Technology Achievement Test (ETAT). The validation was conducted by three specialists and had a reliability coefficient of 0.82 using the Kuder-Richardson 21 formula, before it was administered, marked, scored, coded, and analyzed. Analysis of Co-Variance (ANCOVA) was employed to test the hypotheses, setting the significance threshold at the 0.05 level. The findings revealed a statistically significant beneficial effect of DBMII on the academic achievement of undergraduate students with learning disabilities in EdTech. Also, gender did not significantly influence educational achievement, and no interaction effects between gender and Doodly-based multimedia instructions were observed. It was concluded that Doodly-based multimedia instructions have a statistically significant beneficial effect on the academic achievement of undergraduate students with LDs in EdTech, without any significant influence of gender.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".