Effectiveness of Direct Instruction Learning Strategy Assisted by Mobile Augmented Reality and Achievement Motivation on Students Cognitive Learning Results
Bibliographic record
Abstract
The study aims are to determine whether there is a difference in the average learning outcomes between students who are subject to Direct instruction model aided by mobile augmented reality and Direct instruction model supported by non mobile augmented reality. The presence or absence of significant differences in cognitive learning outcomes between groups of students with high achievement motivation, moderate achievement motivation, and low achievement motivation group. There is no interaction between learning strategies and achievement motivation toward cognitive learning outcomes.Population in this research is all student of semester 1 academic year 2016/2017 Sample is taken by using sampling cluster random sampling technique in mathematics education study of Universitas PGRI Semarang. Methods of data collection in this study are obtained by using interview methods, test methods, and method documentation. The results showed that: (a) There were significant differences in cognitive learning outcomes between groups of students treated with direct instructional strategies with MAR and group of students who were treated with direct instruction learning strategies with non-MAR. (B) There is a significant difference of cognitive learning outcomes between groups of students with high achievement motivation, moderate achievement motivation and low achievement motivation group. (C) There is an interaction between learning strategies and achievement motivation toward cognitive learning outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".