Cognitive Styles In Student Use, Perception, And Satisfaction With Online Learning
Bibliographic record
Abstract
The current trend toward online learning significantly changes the paradigm for teaching and learning. While instructors structure and sequence how they teach in a traditional face-to-face classroom, the very nature of online learning shifts the locus of control from the instructor to the learner, as the student picks and chooses how and when they access the course, what aspects they dwell on, and for how long. As a result, a need exists to investigate how different students respond to the online interface. This study conducted at a small Canadian mid-western university was designed to investigate whether a relationship existed between students' cognitive style preferences and their comfort, use, and satisfaction with the online learning environment. The impetus driving this study was largely due to the scarcity of empirical research relating perceptual modality preferences to online learning. The majority of Internet sites do a good job of presenting information visually; however, students with a dominant visual preference represent only 3% of the population. Therefore, without looking at students' cognitive style preferences as they relate to online learning, the medium is neither being used to its fullest, nor are learners receiving the full potential of online learning. The study (n = 94) looked at two aspects of cognitive style preferences: perceptual modality and field independence/dependence. This study found that student perceptual modality preferences were significant for all three research questions (i.e., students' comfort, use, and satisfaction with WebCT). There was a negative correlation between those with auditory preferences and their comfort and use of WebCT. Conversely, there was a positive correlation between students' kinesthetic preferences and their comfort and use of WebCT. With regard to field independent/dependent students, the only relationship this study found was a negative correlation between field dependent learners and their comfort with both the downloading of files and conversion between different files types. The significance of this study lies in providing preliminary insight into how students with different cognitive preferences respond to online learning, and may provide a basis to improve the quality of online learning for students.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".