The Design and Evaluation of the Persuasiveness of e-Learning Interfaces
Bibliographic record
Abstract
This study addresses the general goal of designing more engaging e-learning applications through persuasive technology. The authors present and discuss two potential approaches to the design persuasive e-learning applications that differ in terms of comprehensiveness and ease of application. The more straightforward approach based on Fogg is considered for designers who may not have the time or background to invest large efforts to analyze and understand how the principles of persuasive technology can be deployed. The Oinas-Kukkonen and Harjumaa (2009) approach is presented as a different approach that does require such investment. The design approaches are complemented with a persuasive assessment grid that can be used as an inspection instrument, akin to usability inspections as found in the field of human-computer interaction. The intent is that this instrument can complement the design process by giving early feedback on issues to address. The authors report an experiment where the inspection instrument is applied to an existing e-learning application. The actual data on how students used it provides feedback on how effective the persuasive grid is for detecting issues. The results show that the application scores low on most criteria, and the usage patterns generally confirm this assessment. However, the authors also find that some students were persuaded to engage more thoroughly to use the system and conclude that large individual differences affects the factors of influence and should lead the designers of e-learning application to consider different means in the design of persuasive technology.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".