Augmented Reality and MS-Kinect in the Learning of Basic Mathematics: KARMLS Case
Bibliographic record
Abstract
By its nature, the learning of certain complex contents has always been a focus of attention and a challenge in the study of mathematics. This fact acquires greater importance if it is about the learning of children, because the psycho-cognitive skills of this type of user, especially when they attend the first levels of Basic General Education are not yet mature. As a result, children are unable to assimilate correctly and easily certain content of an abstract nature during the early stages of mathematics learning. This study presents the results of the application of a computer system called “Kinect based Augmented Reality Math Learning System - KARMLS”, whose design and development uses the Augmented Reality technology and the motion sensor implemented in MS-Kinect camera. The developed application covers elementary math topics corresponding to the Basic General Education curriculum of the Republic of Ecuador. The study used an experimental quantitative approach, involving 29 third-grade children (13 girls and 16 boys), who attend to 2 Basic General Education schools in Riobamba city, Ecuador. The results that allowed to evaluate the prototype proposed in the study were obtained by means of a pretest and a posttest, which were contrasted with the students’ t-test for paired samples. Through the analysis of data obtained and the discussion, it is concluded that the applied computer system had a positive effect for the learning when used as a supplementary tool in the classroom and that it was more effective in children who previously had low performance than with those of high performance. Also, the children were motivated and with positive attitudes regarding the use of the analyzed software.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".