Assessment in the Use of Excel Competency for Problem Solving Using the Approach of Expert and Novice Theory
Bibliographic record
Abstract
The assessment of Competency-Based Learning (CBL) generally lacks a foundation to guide the construction of instruments that accords the nature and goals of this educational model. The measurement instruments normally used in CBL only provide a numerical score with limited information about the levels of competencies reached. This research aims to outline an assessment model that gives room to infer the individual's level of achieved competencies. The study is grounded in the theory of experts and novices and employed a mixed methodology in order not only to discover the measurement of the levels of competency from a numerical perspective but also to qualitatively understand how the students achieve a certain level of expertise in a concrete disciplinary area. The focus of this research study was on problem solving using Excel. Five professors participated in criteria selection, problem design, and the assessment process. We concluded that CBL assessment can be implemented in a more integral way when supported by theoretical frameworks that permit instructors to assess students' achievements and give more effective feedback related to their strengths and weaknesses.
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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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".