A Review on the Use and Perceived Effects of Mobile Blogs on Learning in Higher Educational Settings
Bibliographic record
Abstract
Mobile technology is affecting the way we learn and teach in higher education. An interesting mobile tool for supporting learning and instruction is by using mobile blogs or “moblogs”. This review focuses on existing studies implementing moblogs for learning purposes in higher educational settings. A total of 16 studies were selected for the review. The constant-comparative method was used to analyze the studies. Results from the data analysis indicate that the findings fall into two overarching groups, which are: (i) usage of moblogs; and (ii) perceived effects of moblogs. Seven categories for moblog usage were identified, namely: (i) moblogs were used for context-sensitive learning; (ii) for collaboration in groups; (iii) as a tool for interaction and communication for learning; (iv) as personal learning diaries; (v) to facilitate learning at students’ own time and pace; (vi) as a tool for feedback on instruction; and (vii) for reflections in learning. Meanwhile, three categories were discovered for perceived effects of moblogs, which are: (i) perceived affective effects in terms of satisfaction and attitude; (ii) perceived social effects on students; and (iii) negative perception of moblog in terms of personal and technical factors. These categories are discussed as factors that could promote the use of moblogs for learning in higher education. Directions for future research are also discussed according to these categories as a basis for future work on moblogs for learning.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".