Measuring Self-Regulation in Self-Paced Open and Distance Learning Environments
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
<p class="3">Previous studies have described many scales for measuring self-regulation; however, no scale has been developed specifically for self-paced open and distance learning environments. Therefore, the aim of this study is to develop a scale for determining the self-regulated learning skills of distance learners in self-paced open and distance learning courses. Participants of this study were 1279 distance learners who were part of self-paced distance learning courses in a public open and distance teaching university in Turkey. The items of the scale were prepared based on the literature review, expert opinions, and learner questionnaires. The items of the scale were reduced from 62 to 30 after expert opinions and validity and reliability analyses. For the validity of the scale, the exploratory and confirmatory factor analyses were conducted. The total variance was found to be 58.204%. The Cronbach’s alpha coefficient calculated for the reliability of the scale was found to be .937. Five factors composed of goal setting, help seeking, self-study strategies, managing physical environment, and effort regulation emerged in the 30-item scale. Thus, it was concluded that the scale has a high validity and reliability. This scale is intended to help teachers and instructional designers in developing strategies that will enable learners to either enhance their existing self-regulated learning skills or help them to acquire new skills in self-paced open and distance learning environments.</p>
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.
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 | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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