The Use of Avatars in Gender Segregated Online Learning Within MOOCs in Saudi Arabia - A Rwaq Case Study
Bibliographic record
Abstract
<p class="3">In Saudi Arabia, gender-segregation is a known issue within higher education that often deprives female tutors from providing online learning and Massive Open Online Courses (MOOCs). As well, students may not be getting the benefit of their experience and teaching. The purpose of this study is to develop an Avatar tool to represent a female tutor in a MOOC course with the aim of alleviating the issues of a gender-segregated society in online learning. This project will undertake and analyse a case study concerning the experience of females teaching a MOOC course on “Rwaq” the first Saudi Arabian platform, which was launched in September 2013. The literature on gender-segregation and education technology is reviewed. As an example, gender-segregated in higher education and online learning in Saudi Arabia, Virtual Learning Environments (VLE), Avatar technology in higher education, and finally the adoption of an Avatar tool in MOOCs platforms in SA are examined. One of the objectives of the study is to develop a social interaction environment with learners in online learning within MOOCs. The ultimate objective of this study is to examine if this Avatar tool could alleviate issues of gender-segregation for female lecturers in online learning courses within MOOCs in higher education in Saudi Arabia.</p>
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".