Effectiveness of Direct Instruction Learning Strategy Assisted by Mobile Augmented Reality and Achievement Motivation on Students Cognitive Learning Results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 it