Applying the Rasch Model to Evaluate the Self-Directed Online Learning Scale (SDOLS) for Graduate Students
Bibliographic record
Abstract
With the rapid growth of online learning and the increased attention paid to student attrition in online programs, much research has been aimed at studying the effectiveness of online education to improve students’ online learning experience and student retention. Utilizing the online learning literature as a multi-faceted theoretical framework, the study developed and employed a new survey instrument. The Self-Directed Online Learning Scale (SDOLS) was used to examine graduate student perceptions of effectiveness of online learning environments as demonstrated by their ability to take charge of their own learning, and to identify key factors in instructional design for effective improvements. The study applied the Rasch rating scale model to evaluate and validate SDOLS through a psychometric lens to establish the reliability and validity of SDOLS. Results from Rasch analysis addressed two research questions. First, evidence was found to generally support the new instrument as being psychometrically sound but three problematic items were also identified as grounds for future improvement of SDOLS. Second, the study assessed the importance of various factors as measured by the SDOLS items in contributing to students’ ability to self-manage their own online learning. Finally, the new instrument is expected to contribute to the work of various stakeholders in online education and can serve to improve students’ online learning experience and effectiveness, increase online retention rates, and reduce online dropouts.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.003 |
| 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 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".