Mobile Learning in Mathematics: Benefits, Challenges, Strategies, and Proponents’ and Opponents’ Views
Bibliographic record
Abstract
Integrating mobile learning technologies in mathematics education in light of the pandemic, natural disasters, and recent technological developments is a significant shift in practice. This research applied the systematic review approach to assess the impact of mobile learning aids on students’ learning attitudes, motivation, and performance in mathematics. Based on a synthesis of peer-reviewed articles published from 2010 to 2024, this paper focuses on five themes: (a) student and educator experience and engagement, (b) modification in instructional practices, (c) learning and achievements, (d) opportunities, challenges, and strategies, and (e) mobile learning perceptions. The review reveals that mobile learning enhances students’ essential and higher-order thinking skills by establishing interactivity and learning through inquiry. However, other factors, such as the unequal distribution of resources and technology, still pose a challenge. To address these issues, it is suggested that future policies should focus on improving infrastructure, digital competency, and professional learning for instructors. Further, it is essential for institutions to engage faculty members in collaborative platforms to enhance their practice and have secure measures for data protection. This systematic review emphasizes the need for the researcher to conduct these evaluations over time to gauge the long-term impact of mobile learning and to look at the effects of this form of learning on different groups of students. It also concludes that future studies need to pay attention to ethical concerns and ways to provide equal opportunities in integrating technology into mathematics education for all students.
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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 | 0.081 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".