The Academic Procrastination in Junior High School Students’ Mathematics Learning: A Qualitative Study
Bibliographic record
Abstract
Among the main causes of low learning achievement in mathematics learning is a delayed behavior to do tasks, commonly called academic procrastination. The objectives of this research are to describe and to explain the causal factors and consequences of academic procrastination in learning mathematics for junior high school students. This research was conducted for more than five months. The research took place at six junior high schools in Madiun Regency. The research approach was a qualitative descriptive. The types of data sources were six mathematics teachers and six students, in-class mathematics learning activities, and documents. The data collection techniques were (1) questionnaire, (2) documentation, (3) passive role observation, and (3) in-depth interviews. The data were analyzed using an interactive model, which was implemented in two stages. They are at the time or during the course of data collection and after the data collection. The results of data analysis show that academic procrastination is caused by several factors. They are assumption that the tasks assigned are not important, too burdensome, and difficult to work, insufficient knowledge, too perfectionist, bad management of learning, lack of self-regulation, stress and fatigue, lack of social support, indiscipline teachers, and inconducive school culture. Academic procrastination, most importantly, leads to low learning achievement.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".