Developing online tutors and mentors in Sri Lanka through a community building model: Predictors of satisfaction
Bibliographic record
Abstract
This paper discusses the results of a tutor mentor development program that utilized a community building model to train online tutors and mentors in higher education institutions and professional organizations in Sri Lanka. Based on WisCom; an instructional design model for developing online wisdom communities, this tutor mentor development program which utilized a blended format of face-to-face and online activities in MOODLE, attempted to build a learning community between trainees, both academics and professionals who represented diverse disciplines and organizations. A regression model examined predictors of learner satisfaction, using four independent variables: Community Building, Interaction, Course Design, and Learner Support. Interaction emerged as a strong predictor of Learner Satisfaction explaining 50.2% of the variance in Learner Satisfaction. This finding shows the importance of designing interactive learning activities to support learning online, and contradicts the general belief that Sri Lankan participants would be less likely to interact online because they come from a traditional education system that encourages passivity and reception of ideas from a more learned teacher. Qualitative analysis showed evidence of several types of learning online as a result of collaborative group interaction, as well as issues that contributed to non-participation. Factors that motivated participants to stay engaged in learning could be classified into three categories: (1) general enjoyment, interest and motivation; (2) collaborative learning and community building; and (3) knowledge building. These results suggest that the online learning design based on WisCom led to learner satisfaction and supported interaction and collaborative learning in the Sri Lankan socio-cultural context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".