Abstract
By analyzing the test data of 2718 secondary school students in Guangzhou China on 15 listening items from Guangzhou English Achievement Examination (2015) through G-DINA model, the study explored the relationships among the listening comprehension skills. Based on the test specifications and listening skill taxonomies in existence, 5 experts in language skills and language testing conducted item content analysis independently for the 15 listening items, defined 5 listening attributes, and constructed the Q-matrix. After analyzing latent classes and their posterior probabilities, the study discovered the relationship among the listening skills. According to the listening skill relationship, the study provides insights on the sequence of listening skill training. The efficiency of training may be improved when closely related listening skills are instructed and practiced at the same time. The study also demonstrates that the compensatory and saturated G-DINA model caters to the characteristics of listening comprehension skills and can be applied to tests involving highly interactive and hierarchical skills.
Highlights
Introduction to Cognitive Diagnostic ResearchCognitive diagnostic assessment (CDA) is designed to measure specific knowledge structures and processing skills in students so as to provide information about their cognitive strengths and weaknesses (Leighton & Gierl, 2007)
At a certain significant level, if the Max z-Scores based on ρ and l are larger than the critical values (CV) based on ρ and l respectively, the Cognitive diagnosis models (CDMs) adopted in the analysis will be rejected
This study adopted G-DINA model to explore the relationships among listening comprehension skills
Summary
Cognitive diagnostic assessment (CDA) is designed to measure specific knowledge structures and processing skills in students so as to provide information about their cognitive strengths and weaknesses (Leighton & Gierl, 2007). CDMs may differ in terms of model saturation, interattribute relationships, estimation methods, estimation software, and its versatility in dealing with polytomously scored items. These differences can have significant impact on the estimation of examinee skill mastery status and their interpretation (Lee & Sawaki, 2009). Model saturation determines whether a CDM allows for all possible item parameters, including interactions of attributes. The attribute structure is compensatory in that strength in one attribute may compensate for weakness in another, mastery of all attributes involved in an item is not necessarily required for a test taker to answer the elt.ccsenet.org. The development and applications of those CDMs have always been accompanied with tests on mathematics, medical science, and psychology
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