Research on construction method of learning paths and learning progressions based on cognitive diagnosis assessment

ABSTRACT

Learning path and learning progression have received extensive attention from broad disciplines. The existing research In the field of learning path is rarely applied in curriculum learning and teaching. Learning progression is usually constructed through observations, interviews but not quantitative analyses. With 726 Grade 8 students’ mathematical knowledge in TIMSS-2015 as the research object, this research adopted a newly generated assessment theory – cognitive diagnosis assessment as the research tool and exploited methods such as K-means clustering analysis to construct learning path by combing the relationships among the attributes. We obtained the students’ ability θs for each classified group through the 3PL model in the Item Response Theory (IRT) and constructed the learning progressions based on the θs and the attribute relationships. From a data-driven approach, this method has provided a new perspective as well as the data support for the construction of the learning paths and the learning progressions.

KEYWORDS:

• Learning path
• learning progression
• mathematical competency
• cognitive diagnosis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics statements

Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements. No animal studies are presented in this manuscript. No potentially identifiable human images or data is presented in this study.

Additional information

Funding

This work was support by Youth Fund for Humanities and Social Science Research of the Ministry of Education in 2021: Research on cognitive diagnosis and evaluation of mathematics core literacy(No. 21YJC880102); and Guizhou Philosophy and Social Science Planning Youth Fund (No. 19GZQN29) ;China Scholarship Council [201906140104];2020 Academic Innovation Ability Enhancement Plan for outstanding doctoral Students of East China Normal University [YBNLTS2020-003].

Notes on contributors

Xiaopeng Wu

Wu Xiaopeng is an associate professor in the Faculty of Education of Shaanxi Normal University. He was also a PhD jointly trained by East China Normal University and Purdue University. He is mainly engaged in education measurement and assessment, classroom teaching assessment, mathematics education, etc.

Rongxiu Wu

Wu Rongxiu is a postdoctoral research associate in the Neag College of Education at University of Connecticut. Her main research interests include large-scale data assessment, education measurement, latent variable modelling, etc.

Yi Zhang

Zhang Yi, a doctoral student in the School of Mathematical Sciences of East China Normal University. She mainly focuses on mathematics teacher education, mathematics curriculum and teaching, etc.

David Arthur

David Arthur is a PhD candidate in the School of Statistics at Purdue University. He mainly engages in education statistics, statistical measurement, etc.

Hua-Hua Chang

Hua-Hua Chang is the Charles R. Hicks Chair Professor in the Department of Educational Studies at Purdue University. His research interests include computerised adaptive testing, statistically testing item bias, etc.