Semantic Web Application and Framework Development in South African Higher Education Institutions
Bibliographic record
Abstract
The evolution of the Semantic Web (SW) and its application marked a turning point in how students could benefit from a range of educational web tools and applications enabled by the SW, also referred to as Web 3.0 technology for academic purposes to meet their demands. This shift afforded students the opportunity to obtain meaningful information, collaboration and data filtering to suit their needs. It also offers freedom in how and where they choose to learn. SW tools and applications are progressively being used at several universities worldwide. However, educators’ ability to integrate the use of these tools and applications in teaching and learning appears to be a major problem in almost every development plan of education and educational reform efforts. Moreover, very few educators integrate web tools to their full potential in teaching. This paper probed the integration and use of SW tools and applications in higher education institutions (HEIs), and developed a framework for its adoption in academic processes. The objectives aimed to establish the credible features and benefits of SW tools and applications in HEIs, and how the integration supports students’ academic goals. It is anticipated to improve learning interaction and collaboration, and build a social presence and cohesion among students. The paper employed a systematic literature review, and information and communication technology theory of adoption. The developed framework ultimately suggests that SW tools and applications are beneficial and useful in positively impacting the pedagogical setting. Findings revealed that certain challenges with human factors (technophobia, beliefs), infrastructure, security concerns, ethical and legal issues were identified as a hindrance to be considered during integration. Despite the challenges, these tools and applications provide variety and a new wave of teaching and learning in South African HEIs, which is crucial for meeting the demand of the Fourth Industrial Revolution (4IR) era.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".