Postgraduate Students' Perceptions of Effective Supervision Practice in an Open-Distance e-Learning Environment
Bibliographic record
Abstract
The aim of this study is to explore the perceptions of postgraduate students in open distance e-learning (ODeL) institutions regarding what constitutes effective supervision practices that contribute to their academic success. This study adopted a qualitative research approach, sampling 10 postgraduate students pursuing their master’s degrees in an ODeL environment through purposive sampling. Data was collected through semi-structured interviews and analysed using thematic analysis. The findings revealed that clear communication and feedback, supportive relationships, and active engagement are key elements of effective supervision in ODeL, enhancing postgraduate students' academic success. Supervisors play a crucial role by providing both academic and emotional support through mentorship and motivation, fostering student achievement. The study recommends that supervisors hold regularly scheduled online meetings with their students, as this practice can significantly improve the effectiveness of supervision in ODeL. Additionally, the study highlights the importance of providing training for supervisors in humanising pedagogy, specifically designed for ODeL contexts. This training aims to equip supervisors with the necessary skills to effectively engage with students from diverse cultural backgrounds. It also emphasises the importance of teaching supervisors to respect students regardless of their educational background, beliefs, or cultural differences. Keywords: Postgraduate students, perceptions, effective supervision, open-distance e-learning, students L’objectif de cette étude est d’explorer les perceptions d’étudiants de cycles supérieurs inscrits dans des établissements de formation ouverte et à distance (FOAD) concernant les pratiques de supervision efficaces qui contribuent à leur réussite universitaire. Cette recherche a adopté une approche qualitative en sélectionnant, par échantillonnage raisonné, dix étudiants inscrits à un programme de maîtrise en FOAD. Les données ont été recueillies au moyen d’entretiens semi-dirigés et analysées de manière thématique. Les résultats révèlent que la communication claire et les rétroactions constructives, des relations de soutien ainsi qu’un engagement actif sont des éléments clés d’une supervision efficace en FOAD, pour favoriser la réussite des étudiants. Le rôle des superviseurs s’avère crucial, puisqu’ils offrent un soutien à la fois académique et émotionnel, grâce au mentorat et à la motivation, ce qui stimule la persévérance et la réussite des étudiants. L’étude recommande que les superviseurs tiennent des rencontres en ligne régulières avec leurs étudiants, car cette pratique peut améliorer significativement l’efficacité de la supervision en FOAD. Elle souligne également l’importance d’offrir une formation aux superviseurs en pédagogie humanisante, spécifiquement conçue pour les contextes de FOAD. Une telle formation vise à doter les superviseurs des compétences nécessaires pour interagir efficacement et respectueusement avec des étudiants issus de diverses cultures, quelles que soient leurs origines éducatives, leurs croyances ou leurs différences culturelles. Mots-clés: étudiants de cycles supérieurs, perceptions, supervision efficace, formation ouverte et à distance, réussite universitaire.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".