Cognitive assessment in a computer-based coaching environment in higher education : diagnostic assessment of development of knowledge and problem-solving skill in statistics
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Bibliographic record
Abstract
Diagnostic cognitive assessment (DCA) was explored using Bayesian networks and evidence-centred design (ECD) in a statistics learning domain (ANOVA). The assessment environment simulates problem solving activities that occurred in a web-based statistics learning environment. The assessment model is composed of assessment constructs, and evidence models. Assessment constructs correspond to components of knowledge and procedural skill in a cognitive domain model and are represented as explanatory variables in the assessment model. Explanatory variables represent specific aspects of student's performance of assessment problems. Bayesian networks are used to connect the explanatory variables to the evidence variables. These links enable the network to propagate evidential information to explanatory model variables in the assessment model. The purpose of DCA is to infer cognitive components of knowledge and skill that have been mastered by a student. These inferences are realized probabilistically using the Bayesian network to estimate the likelihood that a student has mastered specific components of knowledge or skill based on observations of features of the student's performance of an assessment task. The objective of this study was to develop a Bayesian assessment model that implements DCA in a specific domain of statistics, and evaluate it in relation to its potential to achieve the objectives of DCA. This study applied a method for model development to the ANOVA score model domain to attain the objectives of the study. The results documented: (a) the process of model development in a specific domain; (b) the properties of the Bayesian assessment model; (c) the performance of the network in tracing students' progress towards mastery by using the model to successfully update the posterior probabilities; (d) the use of estimates of log odds ratios of likelihood of mastery as a measure of "progress toward mastery;" (e) the robustness of diagnostic inferences based on the network; and (f) the use of the Bayesian assessment model for diagnostic assessment with a sample of 20 students who completed the assessment tasks. The results indicated that the Bayesian assessment network provided valid diagnostic information about specific cognitive components, and was able to track development towards achieving mastery of learning goals.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it