Cognitive Mapping Decision Support for the Design of Web-Based Learning Environments
Bibliographic record
Abstract
There is much interest in the study of online learning in higher education. Student beliefs towards online learning may influence intentions and ultimately performance. Many studies have flaws in their research design and fall short in providing useful insight for decision making. In this regard, the need for developing practical e-learning implementation framework(s) is crucial. The main objective of this study is to employ the cognitive map technique to causal relationships among belief factors, and investigating various impact chains via simulations. The authors identify optimal e-learning design and implementation, while a partial least squares approach was performed to validate the proposed research model anchored in the Technology Acceptance Model. A survey was carried out and data were collected from 102 respondents. The proposed research model was tested and subsequent cognitive mapping simulations were performed. This study provides designers, instructors and decision makers an approach by which they can identify relevant factors for design, implementation and maintenance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".