Assessing Students’ Learning Attitude and Academic Performance Through m-Learning During the COVID-19 Pandemic
Bibliographic record
Abstract
This study aimed to assess college of education students’ learning attitude and academic performance in using m-learning during the COVID-19 pandemic. The study employed a pre-test and post-test experimental research design with 50 students from the College of Education, Ikere Ekiti, Nigeria. Two research instruments were used to collect data from the participants on two occasions. The first instrument was a students’ attitude questionnaire that measured the attitude of the participants towards learning. The second instrument was the students’ academic performance test that measured the students’ scores. The differences between pre- and post-tests were measured through independent t-test. Demographic data are presented in a bar chart and show that the majority of the students own mobile devices that were suitable for learning; that the majority of the students used mobile devices for learning; and that all the respondents in the experimental group possessed mobile devices with the Zoom app. The pre-test findings revealed no significant differences in the attitude and performance of students towards m-learning and traditional learning (p>0.005) while the post-test findings showed significant differences in the attitude and performance of students towards m-learning and traditional learning (p<0.005). These findings suggest that m-learning should be integrated into the school curriculum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".