Student satisfaction with a web-based dissertation course: Findings from an international distance learning master's programme in public health
Bibliographic record
Abstract
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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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".