Evaluating Learner Satisfaction in a Multiplatform E-Learning System
Bibliographic record
Abstract
The main objective of this chapter is to present a comparative evaluation between two e-learning systems from the end user (learner) perspective. The evaluation instrument is based on a multiplatform e-learning systems framework and a modified version of the Questionnaire for User Interface Satisfaction (QUIS). First, the evaluation intends to compare the achievable level of overall the learner satisfaction score between a Blackboard e-learning system and a multiplatform e-learning system with three different accessing devices. Second, the evaluation intends to explore the degree of influence and identifies grouping relationships among the factors that influence learner satisfaction while engaged in a multiplatform e-learning system. Lastly, the evaluation determines the gain in the learner satisfaction score between the two systems with respect to three different accessing devices. The findings and the process of evaluation can play an important role for the designer to improve the adaptation process and to enhance the level of learner satisfaction in future multiplatform e-learning systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".