Kui Xie
Chairperson and Professor, Department of Counseling, Educational Psychology and Special Education | Michigan State University
East Lansing, MI, UNITED STATES
Kui Xie's research investigates areas related to motivation and engagement in digital learning.
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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 (1)
Education/Learning
Areas of Expertise (4)
Digital Learning
Educational Technology
Artificial Intelligence
Motivation
Accomplishments (1)
Chang Jiang Scholar Award, Chinese Ministry of Education (professional)
2017
Education (1)
University of Oklahoma: Ph.D., Instructional Psychology and Technology 2006
Affiliations (2)
- The Internet and Higher Education : Associate Editor
- Frontiers in Psychology : Associate Editor
Links (2)
News (1)
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.
Event Appearances (1)
"Student Engagement in Online Learning Environments"
icits2022
Journal Articles (5)
Information literacy instruction in naturalistic high school science classrooms: Instructional strategies and associations with students’ prior knowledge
Teaching and Teacher Education2024 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.
Effects and mechanisms of analytics‐assisted reflective assessment in fostering undergraduates' collective epistemic agency in computer‐supported collaborative inquiry
Journal of Computer Assisted Learning2024 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.
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 Education2024 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.
The Emergence and Escalation of Online Racial Discrimination in Digital Spaces: A Systematic Review
Review of Educational Research2024 COVID-19 required educators and students to rapidly move to online learning. Simultaneously, while navigating the pandemic in lockdown, citizens were exposed to the brutal murder of George Floyd. The increased exposure to online activity and discrimination generated a hyperawareness of the potential link between the two. Our interest was to examine that linkage as we considered the prevalence and escalation of online racial discrimination (ORD) as a student phenomenon. Filtering for adolescent and young adult students, this systematic review ultimately employed 21 articles. Our results reflect that ORD as defined, changed over time, as did the ways it manifested. Importantly, the impacts of ORD on student learning and well-being were revealed.
Does the Seat Matter? The Influence of Seating Factors and Motivational Factors on Situational Engagement and Satisfaction in the Smart Classroom
Sustainability2023 As a technology-enhanced student-centered learning environment, smart classrooms are becoming increasingly popular in higher education. It is undoubtedly important to understand how seating and motivational factors affect situational engagement and satisfaction in smart classrooms. Pre-survey, experience sampling method, and post-survey were used in this study to conduct a longitudinal survey of 113 pre-service teachers in three courses at a university in central China. Descriptive statistics, bivariate correlations, hierarchical linear modeling, and hierarchical linear regression were used to investigate the effects of seating factors and motivational factors on engagement and satisfaction in smart classrooms.
