Computer-Mediated Assessment of Higher-Order Thinking Development
Bibliographic record
Abstract
Solving complicated problems in a contemporary knowledge-based society requires higher-order thinking (HOT). The most productive way to encourage development of HOT in students is through use of the Problem-based Learning (PBL) model. This model organizes learning by solving corresponding problems relative to study courses. Students are directed to develop HOT skills needed for problem-solving that are essential for their future professional activities. However, in order to promote effective HOT skills development of students in PBL learning environment, enhancement of the model is required. The model enhancement can be attained through specific computer-mediated assessment of learning of students. In this paper we introduce an innovative approach to complex, adaptive, and computer-mediated assessment of HOT skills development of individual students. Complexity of assessment is expressed by forming the combined assessments of HOT skills of different types. Adaptation of assessment to the process of HOT skills development is expressed by changes in an instructor‘s fixed assessments by crossing from one phase of PBL to another. Assessment adaptation is provided for individual students as for a study group. Computer-mediation of assessment is provided by a Computer Assessment Tool which promotes students’ HOT skills development and facilitates the assessment process for an instructor. The proposed coefficient of success of HOT skills development serves as an effective tool of analysis of this process.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".