The Effect of Mangala, the Intelligence Game Taught by Distance Education, on the Mathematical Motivations and Problem-solving Skill Levels of 6th-Grade Students
Bibliographic record
Abstract
This study examines the effect of "Mangala" on the mathematical motivation and problem-solving skill levels (PSoSL) of 6th-grade students. A single-group pre-test-post-test quasi-experimental design based on the quantitative research approach was used in the study conducted through distance education. The sample consisted of 14 6th-grade students. The data collection tools used in the study, which continued for three weeks in the 2020-2021 academic year, were the Mathematical Motivation Scale (MMS) and Performance Tasks (PT). The data obtained from this study were analyzed using the SPSS 22.0 package program. Since the sample size was small and the data did not show normal distribution, data analysis was carried out using the Wilcoxon Signed Rank Test, one of the non-parametric statistical tests. Data analysis showed a statistically significant difference between the pre and post-experiment MMS scores of the students. The effect of "Mangala" on students' PSoSL was evaluated through performance tasks. The Progressive Scoring Scale (PSS) taken from Baki (2014) was used to evaluate performance tasks. Researchers redefined each criterion in the scoring key and set four levels: "very good," "good," "unsatisfactory," and "empty." The analysis of the problems in the performance tasks showed that the students usually gave "very good" and "good" answers. However, some students had difficulties finding solution strategies and writing a similar problem; thus, they left blank answers.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".