The Impact of Adaptive Complex Assessment on the HOT Skill Development of Students
Bibliographic record
Abstract
In this paper we propose a method for the adaptive complex assessment (ACA) of the higher-order thinking (HOT)skills needed by students for problem solving, and we examine the impact of the method on the development of HOTskills in a problem- based learning (PBL) environment. Complexity in the assessment is provided by initial,formative, and adaptive assessments of HOT skills; assessments of collaborative skills; and summative assessmentsof HOT skills. Adaptability in the assessment is provided through the dynamics of an instructor’s assessments basedon developing HOT skills; through a flexible choice of control tests and instructional problems for students andcollaborative groups; and through the self-formation of heterogeneous, collaborative HOT skill groups. Theassessment fosters the development of HOT and collaborative skills through a combination of personalized andcollaborative problem-based learning (PBL). The three-stage assessment process guides the development of HOTskills during subject study through PBL. The focus of the first stage is on developing the HOT skills of studentsthrough personalized PBL. The second stage is devoted to developing HOT skills and collaborative skills throughcollaborative PBL. The third stage is devoted to assessing collaborative skills and constructing summativeassessments of students. The proposed calculations for the coefficients of HOT skill development serve as aconstructive means of exploring the impact of the ACA method on the HOT skill development of 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 | 0.008 | 0.082 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".