A Research Note on Accounting Students' Epistemological Beliefs, Study Strategies, and Unstructured Problem-Solving Performance
Bibliographic record
Abstract
In a previous study, Phillips (1998) observed that accounting students possess several dimensions of beliefs about the nature of knowledge, and provided evidence that one of the belief dimensions (i.e., that knowledge is uncertain) was related to a component of unstructured problem-solving performance (i.e., evaluating the relevance of case facts). Phillips (1998) also proposed that the relationship between students' beliefs and unstructured problem solving was mediated by their study strategies, but did not test this proposition. The current study replicates the belief dimensions observed by Phillips (1998) and examines the empirical relationship among students' beliefs, study strategies, GPAs, and unstructured problemsolving performance. Results indicate that one dimension of beliefs (i.e., that knowledge is complex) was associated with a dimension of study strategies (i.e., consolidating knowledge) and that these two dimensions were related to cumulative GPA and, after controlling for GPA, with a component of unstructured problem-solving performance (i.e., consolidating analyses). These findings, in conjunction with the results reported by Phillips (1998), are consistent with the theory that performance on an unstructured problem depends, in part, on the degree to which student beliefs and study strategies match the features of an “ideal” solution for the problem. This theory helps to explain how two equally knowledgeable students can differ in how they cope with unstructured problem solving, with one insisting on simple answers and the other remaining open to complex and integrative solutions.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".