Navigating the Importance of Selected Educational Technology Tools in Enhancing and Transforming the Task-Based Learning Method: Reflective Insights from Teaching Graduate Educational Psychology Students at one Ugandan University
Bibliographic record
Abstract
The study aimed at navigating how edtech tools enhance and transform task-based learning method among university students pursuing graduate studies in Educational Psychology. Basing on Blooms Taxonomy especially the Higher Order Thinking Skills (HOTS), Laurillard's Conversational Framework, TPCK model, SAMR design and the Teaching Change Framework, the researcher motivated the graduate students to utilise Mindmups, Google Slides, WhatsApp texts and Turnitin educational technology (EdTech) tools to navigate if these tools, due to their respective affordances, enhance and transform task-based learning. The study was a visual ethnographic one by design with visual narratives for analysis purposes. It was established that the mentioned tools enhanced learner motivation, interest, participation and ultimately, academic performance. It was concluded that edtech tools, if selected due to their respective affordances, based on the right models, can improve learning and cause changes in the methods in place to transform low-technology-lecturer-centred learning to high-technology-learner-centred learning.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".