Effect of Communication Management on Distance Learners’ Cognitive Engagement in Malaysian Institutions of Higher Learning
Bibliographic record
Abstract
Rapid development of communication tools has brought about contentious issues in communication management in distance learning (DL) programs. The aim of this study is to investigate the relationships between communication management indicators, namely, communication practices, communication tools, and students' cognitive engagement in distance learning programs. A conceptual framework for communication management was developed from Moore’s Transactional Distance Learning Theory (TDLT) and other existing literature. This study was conducted using quantitative research design. A questionnaire (a survey method) was used to elicit responses from 450 randomly selected in-service teachers from three Malaysian Public universities that offer blended mode distance-learning programs. Data analysis was conducted using Analysis of Moment Structures (AMOS) software to test the structural path of communication practices, communication tools, and students' cognitive engagement. Tests of hypotheses provided evidence of measures of fit statistics. The findings provide evidences that effective communication practices and communication tools have strong positive influence on distance students’ cognitive engagement.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".