Increasing Student Engagement with Personalized Emails
Bibliographic record
Abstract
Despite a large number of studies in personalizing e-learning systems, only a few sufficiently cover the impact of personalization on university emails. Either universities impose the use of university email or students are reluctant to use it. For instance, It was found that at King Abdulaziz University only 23% of students were using the university email to communicate regularly, unlike the faculty members, who showed an 80% commitment. However, this paper attempts to investigate the effect of applying different personalized systems on the university email and whether using adaptation and adaptability techniques in the university email will cover the huge gap between the university and its members. Specifically, frameworks were built to evaluate the efficiency, frequency of error occurrence, effectiveness, and user satisfaction in each experiment. We focused on testing the usability of personalized email against the existing university email and then evaluate which approach (adaptive, adaptable, or mixed-initiative) is more favorable to personalize the university email. These were conducted and evaluated using 40 subjects. Interestingly, results show that subjects with personalized emails were most efficient and satisfied as well as errors were reduced by 42%. Furthermore, significant differences were found between the three approaches (adaptive, adaptable, and mixed-initiative), and the adaptive approach was the most preferred by the respondents. A set of empirically derived guidelines was also discussed as a basis for developing a suitable university email structure.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".