Student satisfaction with a web-based dissertation course: Findings from an international distance learning master's programme in public health
Notice bibliographique
Résumé
Introduction Online distance learning (e-learning) is now an established method for providing higher education, in the UK and across the world. The focus has largely been on developing the technology, and less attention has been given to developing evidence-informed course provision. Thus the effectiveness of this teaching approach, and its acceptability to students, is, at times, uncertain. Many higher education courses require students to submit a dissertation. Traditional face-to-face courses will include meetings between the student and an allocated supervisor, to support the dissertation component of the course. Research into the supervisory relationship and student satisfaction has focused on doctoral students. Little is known about the experiences of students studying for a master’s degree. The aim of the current study was to measure student satisfaction with the dissertation course as part of a fully online distance learning master’s programme in public health. Methods All students submitting a dissertation as part of their master’s programme in Public Health were sent an electronic survey to complete, in September 2012. The 34 item questionnaire used a four point Likert scale for students to rate levels of satisfaction across key components of the course, including preparatory materials, study skills, and support, and with the amount and content of supervision. Open ended/free text questions were used to determine factors associated with levels of satisfaction and to gain student feedback on the course overall. The constant comparative method was used to identify key themes from the free-text responses. Results Of the 45 students submitting a dissertation, 82% (37) responded to the survey. The majority of students, 85% (28) were satisfied or very satisfied with the dissertation course overall. Levels of satisfaction remained high for many of the components examined. Differences were observed for part time and full time students, and for the type of dissertation, but these were not significant. Similarly, non significant findings were observed for associations between satisfaction and the estimated number of contacts initiated with their supervisor, and for the time spent working on their dissertation. The constant comparative analysis identified key themes and feedback included ‘self development’, ‘peer support’, and ‘writing skills’. Conclusions Generally high levels of satisfaction were received from students studying a dissertation course as part of a fully online distance learning programme in public health. Areas for further improvement were identified and the results act as a benchmark for future quality enhancement. These findings suggest that appropriate information, study skills, and supervisory support can be provided in an online distance learning programme, for students taking a master’s level dissertation course.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».