Special Session 8

Learning Analysis and Multi-Dimensional Data-Driven Educational Evaluation

Description
With the rapid development of generative artificial intelligence and multi-modal data processing technologies, educational evaluation is undergoing a profound paradigm shift from "result-oriented" to "process insight". This forum aims to explore how learning analysis technologies can be utilized in the teaching process at all grade levels to collect multi-modal learning data, and to build a comprehensive evaluation system that can comprehensively depict students' digital profiles and accurately diagnose cognitive and non-cognitive abilities.
The forum will focus on two core topics: The first is "technology empowerment", which explores how to utilize technologies such as natural language processing, computer vision, and brain science to analyze learning patterns in a human-machine collaborative environment, and to capture details of classroom interactions, changes in cognitive states, and emotional experiences, etc.; The second is "evaluation transformation", which focuses on shifting from traditional knowledge assessment to "three-dimensional evaluation" of core competencies, learning qualities, and metacognition, and exploring how data can drive teaching improvement and personalized intervention, ultimately achieving the evaluation goal of "for better learning". We expect to build a new educational evaluation ecosystem that is both technologically forward-looking and rich in humanistic care through interdisciplinary thought exchanges.

Session organizers
Assoc. Prof. Xinyi Wu, Dalian University, China
Assoc. Prof. Chao Wan, Shenyang University, China

The topics of interest include, but are not limited to:
▪ Multimodal Learning Analysis and Evaluation Insights
Focus on how to integrate multi-source data such as students' behavioral logs, eye-tracking, voice emotions, and brain neural signals from various teaching scenarios, breaking through the limitations of a single data source, and deeply understanding classroom participation, cognitive load, and collaborative learning patterns, providing a more comprehensive evidence chain for comprehensive evaluation.
▪ Student Comprehensive Quality Evaluation Enabled by Generative AI
Explore the application of large language models like ChatGPT in accompanying data collection, non-cognitive ability modeling, and automatic generation of student growth portraits, as well as how to use AI to achieve objective interpretation of evaluation results and traceability analysis of growth factors.
▪ Learner Trait Mining and Personalized Learning Support
Research on data-driven learner emotion analysis, knowledge hiding behavior, and measurement models of learning quality, aiming to accurately identify individual differences, and thereby design adaptive learning paths and precise intervention strategies.
▪ Learning Interaction Analysis in a Human-Machine Collaborative Environment
Analyze the interaction patterns and discourse dynamics between teachers, students, and between students and machines in classrooms with the intervention of generative AI and intelligent partners. Utilize methods such as natural language processing (NLP) and cognitive network analysis (ENA) to reveal how intelligent technologies promote or influence the occurrence of deep learning among students.
▪ Data-Driven Teaching Decision-Making and Evaluation Result Application
Focus on how to convert the data generated by learning analysis into understandable and actionable teaching insights for teachers. Explore the "diagnosis-feedback-optimization" closed-loop mechanism based on data and how to promote teaching management changes in regions and schools through data dashboards and inspection models.
▪ Design and Methodological Innovation of Process-Based Evaluation Tools
Collect research achievements on learning analysis algorithm models, intelligent assessment tools (such as automatic encoding, problem-solving strategy identification), and the application exploration of interdisciplinary research paradigms (such as integrating learning science and computer science) in the evaluation field.
▪ Educational Evaluation Ethics and Teachers' Digital Literacy
Explore how to balance data collection privacy and evaluation benefits in the new data-driven evaluation paradigm, ensuring the fairness and interpretability of algorithms. At the same time, focus on the data literacy and evaluation capabilities that teachers should possess in the digital era, and explore the integration of "technical coldness" and "educational warmth".

Submission method
Submit your Full Paper (no less than 5 pages with two colums) or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 8 (Learning Analysis and Multi-Dimensional Data-Driven Educational Evaluation)
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Introduction of session organizers

Assoc. Prof. Xinyi Wu
Dalian University, China

Wu Xinyi, Associate Professor, School of Education, Dalian University. She holds a doctoral degree in Educational Technology and was selected for the provincial "High-level Young Talent Program". She is a master's supervisor. She is a young member of the Assessment Committee of the China Educational Technology Association and has been dedicated to technologies such as multimodal learning analysis, brain-computer interface empowerment in education, virtual space learning, and graph neural networks. She has conducted research on topics such as desktop virtual reality learning spaces, multimodal intelligent agents empowering education, and innovative science education in primary and secondary schools.
In the past three years, the applicant has published 6 papers as the first author in important academic journals in the fields of education or computer science, including "Modern Educational Technology", "Education and Information Technologies", and "Journal of Eye Movement Research". She has led several projects funded by provincial social science youth funds, educational reforms, and educational associations. She has also participated in several projects funded by the Ministry of Education, general humanities, and the National Natural Science Foundation. She has published two related textbooks. She has received provincial first-class courses and honors such as being an outstanding instructor in the National College Design Competition. She has been deeply involved in technology-enabled education for multiple school levels and has won several awards in education informatization competitions. She serves as a reviewer for several SSCI journals such as "Research in Higher Education" and "humanities and social sciences communications".

 

Assoc. Prof. Chao Wan
Shenyang University, China

Wan Chao, the head of the Modern Educational Technology Department at the Normal College of Shenyang University, is an associate professor with a doctorate in educational technology. He is also a graduate supervisor and has been selected as a high-level talent in Liaoning Province. He has been responsible for the "14th Five-Year Plan" educational science research projects in Liaoning Province and the youth fund projects of the Liaoning Society for Social Sciences. He has been awarded a first-class course in Liaoning Province and has compiled textbooks such as "Curriculum Theory for Kindergartens" and "Early Childhood Education". He has received the "14th Five-Year Plan" national planning textbook award for vocational education and won the provincial second prize in the National Teacher Teaching Innovation Competition. He has published over ten papers in CSSCI journals such as "Open Education Research" and "Northeastern University Journal (Social Sciences Edition)", and two papers in the first-tier SSCI category. He has guided graduate students to win the second prize in the National Tianjibing Education Master Skills Competition, two second prizes in the National Micro-lesson Competition, two first prizes in the Provincial Education Master Skills Competition, and five second prizes. He has also guided undergraduate students to win the third prize in the National Computer Design Competition. He serves as a reviewer for several CSSCI journals and educational-related SSCI journals.