Diffusion of Innovations Approach to the Evaluation of Learning Management System Usage in an Open Distance Learning Institution
Bibliographic record
Abstract
Academic institutions such as the University of South Africa (Unisa) are using information and communication technology (ICT) in order to conduct their daily primary operations, which are teaching and learning. Unisa is the only distance learning university in South Africa and also in Africa. Unisa currently has the highest number of students on the continent of Africa. In an attempt to bridge the gap between facilitators and students, Unisa introduced a learning management system known as myUnisa. MyUnisa is used by facilitators and students as a tool to conduct teaching and learning, and for communication. To the best of the researcher’s knowledge, factors that influence its acceptance and usage have not been studied prior to this study. The main deciders of the success of technology are the users, as is reflected in the well-established theories and models that exist to evaluate the acceptance of technology and innovation. The objective of this study was to understand the factors that contribute to the usage of myUnisa by students. An online questionnaire was used for data collection, and a quantitative analysis was conducted. Among others, the results reveal that complexity does not have a significant impact on the students' decision to use myUnisa.
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".