Four-aspect classification of metrics and statistics for measuring the quality of test-based exercises¶
Research Method · All research methods
Evidence · 3 claims (3 for) · 1 study (1 design),q2· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
The article classifies the metrics and statistics usable to measure the quality of test-based exercises from student performance data according to four aspects: whether the results of the exercise are consistent (CIC via Cronbach's alpha, True Weight), whether undesired situations can be inferred (mean, median, standard deviation, skewness, kurtosis), whether the exercise meets the student's level of knowledge (Ease Index, Item Difficulty), and whether the exercise can determine the student's knowledge level (Error Ratio, Standard Error, Random Guess Score, Discrimination Index, Discrimination Efficiency, Item Discrimination Index). This classification, building on Peña (2014), structures both the tool and the evaluation questions.
Accounts¶
- Four-aspect classification of metrics and statistics for measuring the quality of test-based exercises: The article classifies the metrics and statistics usable to measure the quality of test-based exercises from student performance data according to four aspects: whether the results of the exercise are consistent (CIC via Cronbach's alpha, True Weight), whether undesired situations can be inferred (mean, median, standard deviation, skewness, kurtosis), whether the exercise meets the student's level of knowledge (Ease Index, Item Difficulty), and whether the exercise can determine the student's knowledge level (Error Ratio, Standard Error, Random Guess Score, Discrimination Index, Discrimination Efficiency, Item Discrimination Index). This classification, building on Peña (2014), structures both the tool and the evaluation questions. (Josu Arruarte et al. (2021))
Claims¶
- Boxplot visualizations enriched with correlation to final marks identified the same suspicious tests as the consistency metrics, and instructors modified two of them for the next academic year [+W]
- In a retrospective higher-education evaluation, instructors using TEA found low CIC values and unbalanced True Weights in three of the ten tests and marked them for further analysis [+W]
- Item-level visualizations of discrimination index and discrimination efficiency led instructors to identify troublesome items and replace or modify them [+W]
Related Research Methods¶
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Key Sources¶
- Josu Arruarte, Mikel Larrañaga, Ana Arruarte & Jon A. Elorriaga. (2021). Measuring the Quality of Test-based Exercises Based on the Performance of Students. International Journal of Artificial Intelligence in Education, 31, 585–602. https://doi.org/10.1007/s40593-020-00208-0
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
four-aspect-classification-of-metrics-and-statistics-for-measuring-the-quality-of-test-bas |
title |
Four-aspect classification of metrics and statistics for measuring the quality of test-based exercises |
description |
The article classifies the metrics and statistics usable to measure the quality of test-based exercises from student performance data according to four aspects: whether the results of the exercise are consistent (CIC via Cronbach's alpha, True Weight), whether undesired situations can be inferred (m |
status |
review |
generated |
by process:settle-candidates on 2026-10-28 |
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unverified — no one has confirmed this page's content |
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