Cognitive Style FD/FI as a Learner Selection Criterion in Formative Evaluations A Qualitative Analysis
Bibliographic record
Abstract
Single–subject formative evaluation appears to be a cost–effective strategy for improving instructional products. However, the criterion to use for selecting an appropriate test subject who could generate optimal feedback data for improving the instructional product remains a central concern among performance technologists. This article reports the results of a qualitative study of the effectiveness of the cognitive style construct field–dependent/independent as a student selection criterion in formative evaluation. In the study, we collected formative evaluation data from two field–dependent (FD) and two field–independent (FI) test subjects while they were individually interacting with a CAI package. We focused on four different sources of data: think–aloud protocols, researcher/subject interactions, informal observations, and debriefing interviews. Our analysis of the formative evaluation data indicates that the FI individuals were better test subjects than their FD counterparts. FI subjects showed a great deal of confidence in entering the formative evaluation process. Their feedback was abundant and precise and included specific suggestions for improving the material. They not only identified their own difficulties but also speculated about difficulties other students may encounter. In contrast, the FD subjects were anxious and demonstrated less confidence in approaching the evaluation activities. Frequent probing was necessary to trigger their reactions and generate their feedback. Their feedback data were vague, and more inferences were required for translating them into revision decisions. Both FD and FI subjects could identify major discrepancies in the presentation of the material (events of learning) as well as gross misconceptions in the processing of information. Although the FD and FI feedback data differ both qualitatively and quantitatively, no conflicting observation was made.
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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 | 0.081 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".