Kui Xie

Chairperson and Professor, Department of Counseling, Educational Psychology and Special Education Michigan State University

  • East Lansing MI

Kui Xie's research investigates areas related to motivation and engagement in digital learning.

Contact

Michigan State University

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Biography

Dr. Kui Xie is Red Cedar Distinguished Professor and Chairperson of Department of Counseling, Educational Psychology, and Special Education at Michigan State University. Prior to MSU, he was Ted and Lois Cyphert Distinguished Professor and director of Research Laboratory for Digital Learning at Ohio State University. His research investigates areas related to motivation and engagement in digital learning, K-12 technology integration and teacher professional development, technology intervention and learning environment, learning analytics and research methods. He focuses on building translational research in partnership with K-12 schools. He has published extensively in flagship journals in crossover fields of Educational Technology, Educational Psychology, and others. His work has been highly visible to the general public appearing in The Conversation, U.S. News, NPR Radio, TIME, etc. Xie serves as the associate editor for The Internet and Higher Education and Frontiers in Psychology.

Industry Expertise

Education/Learning

Areas of Expertise

Digital Learning
Educational Technology
Artificial Intelligence
Motivation

Accomplishments

Chang Jiang Scholar Award, Chinese Ministry of Education

2017

Education

University of Oklahoma

Ph.D.

Instructional Psychology and Technology

2006

Affiliations

  • The Internet and Higher Education : Associate Editor
  • Frontiers in Psychology : Associate Editor

News

How ChatGPT Can Help Students Learn, Prevent Cheating

Voice of America  online

2023-07-04

Kui Xie and Eric Anderman are professors of educational psychology and educational technology. In their research, they have found that the main reason students cheat is their desire to do better in school. For example, some students want to get a high grade, and others want to learn all that they can about a subject.

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Event Appearances

"Student Engagement in Online Learning Environments"

icits2022  

Journal Articles

Information literacy instruction in naturalistic high school science classrooms: Instructional strategies and associations with students’ prior knowledge

Teaching and Teacher Education

2024

Students rely on information literacy (IL) to effectively assess information. However, we lack understanding about how IL instruction occurs in naturalistic science classrooms. This mixed-methods study provides descriptive accounts of teachers' IL instruction and its responsiveness to students' prior knowledge, using transcripts from 55 lessons with over 2800 minutes of classroom interactions and students' prior IL scores (n = 335). Results suggest that teachers in our sample most often provided instruction on what information is needed and how to use information to fulfill task requirements. While teachers demonstrated overall responsiveness to students’ prior knowledge, there was some mismatch among specific IL components.

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Effects and mechanisms of analytics‐assisted reflective assessment in fostering undergraduates' collective epistemic agency in computer‐supported collaborative inquiry

Journal of Computer Assisted Learning

2024

Background
Undergraduates' collective epistemic agency is critical for their productive collaborative inquiry and knowledge building (KB). However, fostering undergraduates' collective epistemic agency is challenging. Studies have demonstrated the potential of computer‐supported collaborative inquiry approaches, such as KB—the focus of this study, and reflective assessment to foster collective epistemic agency.
Objectives
This study used a quasi‐experimental design to examine the impact of reflective assessment on undergraduates' collective epistemic agency and the mechanisms of this impact.

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A personal social knowledge network (PSKN) facilitates learners’ wayfinding and its differences in behavior patterns between high and low performers in connectivist learning

International Journal of Educational Technology in Higher Education

2024

Wayfinding, which is a part of learning in connectivist learning, involves consolidating a wide variety of resources and information and building connections among them. However, learners often encounter difficulties in wayfinding, and are lost without technological support in connectivist learning. This study examined the wayfinding processes occurring within a network of learners in a personal social knowledge network (PSKN), explored differences in behavior patterns between high and low performers in PSKN. The results reveal the diversity and complexity of wayfinding in a PSKN, including finding and connecting nodes, forming cognitive maps, finding and filtering information, and creating new nodes.

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