Education Application Testing Perspective to Empower Students' Higher Order Thinking Skills Related to The Concept of Adaptive Learning Media
Bibliographic record
Abstract
This article aims at arguing for the importance of the testing step when designing an educational application by taking a case study from the development of adaptive learning media. The media contains a set of in-struments that are specifically built to empower students' critical thinking skills. Three aspects that are con-sidered in testing this educational application are application validity at each stage of system development, measurement of the final system feasibility test for user needs, and system implementation by running learning media on the test sample. Implementation of testing on application products is carried out accord-ing to system requirements and models. The existence of the characteristics of adaptive media and the diver-sity of menus in the application implies the importance of doing a lot of improvisation when carrying out tests, such as determining the right test cases, choosing the appropriate test model and method, determining a suitable test environment, and considering several other aspects aimed at optimizing test results. obtained in order to ensure the quality of learning media products. This study analyzed the test data using Likert scale as an interpretation of the results of the validation assessment from the experts by referring to certain perceived standards of assessment. Meanwhile, the analysis of the data from the feasibility test results from a sample of 20 students using the system usability scale (SUS) instrument. The technique to test the effec-tiveness was using a pretest-posttest control group design with a sample of 98 students. Parametric/non-parametric data analysis was then applied to analyze the data on the results of testing the effectiveness or efficacy of adaptive media products in improving students' higher order thinking skills (HOTS). Based on the testing steps applied to the application of adaptive learning media, the results obtained that the product was considered feasible and effective in empowering students' HOTS. The study concludes that the educa-tional application testing that has been carried out is able to provide an objective and independent view of the application of adaptive learning media which will be useful in operational functions to understand the level of effectiveness in its implementation before being widely used in 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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".