Challenges of Blended E-Learning Tools in Mathematics: Students’ Perspectives University of Uyo
Bibliographic record
Abstract
An in-depth knowledge of pedagogical approaches can help improve the formulation of effective and efficient pedagogy, tools and technology to support and enhance the teaching and learning of Mathematics in higher institutions. This study investigated students’ perceptions of the challenges of blended e-learning tools in the teaching and learning of mathematics. The study is a descriptive survey design conducted with thirty undergraduate students of the University of Uyo, Nigeria. A research questionnaire of students’ perceptions on the challenges of blended e-learning tools in mathematics was used to elicit responses. The questionnaire has three sections of the perceived challenges of blended e-learning tools in mathematics; availability, accessibility and students’ ICT skills towards utilization of blended e-learning tools. Data were analyzed using SPSS at the 0.05 level of significance. The results revealed non-availability, non-accessibility and inadequate students’ ICT skills towards the utilization of blende e-learning tools for the teaching and learning mathematics. The overall results revealed that there is significant difference on students’ perceptions towards the challenges of blended e-learning tools. Based on the research findings, the institution and instructors need to identify the perceived challenges and opportunities of blended e-learning and provide practical support such as provision of Virtual Learning Environment (VLE) to diversified students learning of mathematics. The study could be used as proactive response towards the institutions’ preparedness on the development of blended e-learning approaches in terms of content design models and pedagogical approach for the teaching and learning of mathematics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".