Needs Assessment for Designing a Digital Learning Ecosystem to Enhance Mathematical Resilience in Pre-Service Mathematics Teachers
Bibliographic record
Abstract
This study investigates the instructional conditions, challenges, and essential needs for developing a Digital Learning Ecosystem (DLE) to enhance Mathematical Resilience (MR) among pre-service mathematics teachers in Northeastern Thailand. Grounded in the GVSS framework were Growth Mindset, Value, Struggle, and Support. This research employs a quantitative survey design with data collected from 372 pre-service teachers and 83 teacher educators across 17 teacher education programs. The findings reveal significant gaps between current and desired instructional practices, particularly in the dimensions of Struggle and Support. Key obstacles include the overuse of lecture-based instruction, limited integration of digital tools, and insufficient emotional and social support. Moreover, comparative analysis highlights perceptual differences between instructors and students, with pre-service teachers expressing greater needs across all MR dimensions. The study underscores the transformative potential of DLEs in fostering emotionally supportive, cognitively challenging, and adaptive learning environments. The findings provide critical insights for designing future instructional models that leverage digital technologies to promote mathematical resilience in teacher education.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".