The Impact of Using SMS as Learning Support Tool on Students’ Learning
Bibliographic record
Abstract
This study aimed to investigate the impact of using Short Message Service (SMS) as learning support tool on students’ learning in an introductory programming course. In addition, the study examined students’ perceptions of the advantages and disadvantages of the use of SMS as a learning support tool in their class. The participants in this study were 52 students who were enrolled in two sections introductory programming course. For the purpose of the study, nonrandomized control group, pretest–posttest and qualitative interview designs were used. The control group consisted from 23 students, while the experimental one consisted from 29 students. A total number of 36 SMS messages were sent to each student, in the SMS group, over a period of 12 weeks. The messages contained different types of information, i.e. short review of programming concepts, hints to solve assignments, and triggering questions. At the end of the experiment, semi-structured interviews were conducted with ten students from the SMS group. The analysis of the collected data showed that the use of SMS as learning support tool contributed significantly in improving students’ learning. All the interviewed students believed that the use of SMS technology as learning support tool has more advantages than disadvantages. Based on the findings, this study provided some recommendations regarding the implementation of the SMS in the Jordanian higher education settings.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".