Active Learning Barriers in Developing Mathematical Proficiency: Comparing Visual Impairment Students’ and Teachers’ Perspective
Bibliographic record
Abstract
Visual impairment (VI) students face various barriers in mathematics learning, which cause low mathematical proficiency (MP). Therefore, active learning (AL) needs to be optimized to develop the MP of VI students in inclusive classes. This research aimed to explore the barriers of AL to developing MP from the perspectives of VI students and teachers. This was qualitative research with a case study design. The subjects were nine VI students and seven mathematics teachers from an inclusive high school in Yogyakarta. They were selected using purposive sampling. Semi-structured interviews were conducted to collect data. Data analysis was done using the Bogdan and Biklen approach. The result explores AL barriers to developing MP from the perspectives of VI students and teachers almost similar. It can be categorized into three themes: 1) human side barriers, mainly barriers are students' lack of confidence in their abilities and novice teachers' lack of confidence in facilitating VI students. This indicates that both of them have low self-efficacy; 2) environmental barriers, mainly related to discrimination and limited communication skills; and 3) technology and learning media barriers, mainly related to limited learning media for VI students' hands-on activities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".