Understanding Online Learning Based on Different Age Categories
Bibliographic record
Abstract
[This Proceedings paper was revised and published in the 2019 issue of Journal of Information Technology Education: Research, Volume 18] Aim/Purpose: To understand readiness of students for learning in online environments across different age groups. Background: Online learners today are diverse in age due to increasing adult/mature students who continue their higher education while they are working. Understanding the influence of the learners’ age on their online learning experience is limited. Methodology: A survey methodology approach was followed. A sample of one thousand nine hundred and twenty surveys were used. Correlation analysis was performed. Contribution: The study contributes by adding to the limited body of knowledge in this area and adds to the dimensions of the Online Learning Readiness Survey additional dimensions such as usefulness, tendency, anxiety, and attitudes. Findings: Older students have more confidence than younger ones in computer proficiency and learning skills. They are more motivated, show better attitudes and are less anxious. Recommendations for Practitioners: Practitioners should consider preferences that allow students to configure the learning approach to their age. These preferences should be tied to the dimensions of the online learning readiness survey (OLRS). Recommendations for Researchers: More empirical research is required using OLRS for online learning environments. OLRS factors are strong and can predict student readiness and performance. These are opportunities for artificial intelligence in the support of technology-mediated tools for learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".