Investigation of the Factors Affecting Open and Distance Education Learners’ Intentions to Use a Virtual Laboratory
Bibliographic record
Abstract
Laboratories, which are an integral part of education in disciplines that require hands-on training and application, can now be presented using new technologies, and application activities can be realized at a distance. In this study, virtual laboratories (VLs) are discussed, and factors affecting the students’ intention to use VLs are investigated. The study was conducted within laboratory applications of circuit analysis within an associate degree program of a distance teaching university in Turkey. In this study, which used exploratory sequential design approach, the learners’ intentions to use a VL were examined within the framework of the technology acceptance model (TAM). Content analysis was used for the analysis of qualitative data, and the partial least squares structural equation model was used for the analysis of quantitative data. As a result of the study, the developed TAM-based research model is a useful conceptual framework towards understanding and explaining the intentions of learners’ virtual laboratory usage. The results of this study will guide institutions to integrate VLs effectively into the education process and to increase and disseminate the use of VLs by learners.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".