Assessing Student Engagement in Online Programmes: Using Learning Design and Learning Analytics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the learning design framework used in the design of the Online MA in Photography at Falmouth University. It discusses the importance of evaluating the success of online learning programmes by analysing learning analytics and student feedback within the overall pedagogic context and design of the programme. Linear regression analysis was used to analyse the engagement of three cohorts of students that completed four modules of the Online MA Photography (n=33) with over 80,000 entries in the dataset. The research explored student engagement with online content that promoted low-order cognitive skills (i.e. watching videos, reading materials and listening to podcasts) as well as high-order cognitive skills (i.e. participating in online forums and webinars). The results suggest there is weak evidence of an association between average overall mark in all modules and the level of engagement with self-directed content (P = 0.0187). There is also weak evidence of an association between average overall mark in all modules and the level of engagement in collaborative activities (P < 0.0528). Three major themes emerged from the focus group 1) weekly forums and webinars, 2) self-directed learning materials and 3) learning design and support. Online learning was acceptable and convenient to postgraduate students. These findings are discussed further in the paper as potential predictors of student performance in online programmes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it