Recent News and Announcement

Publication on Motivation and Academic Delay in Online Learning

Cheng, S., Xie, K., & Collier, J. (2023). Motivational beliefs moderate the relation between academic delay and academic achievement in online learning environments. Computers & Education, 104724. DOI: https://doi.org/10.1016/j.compedu.2023.104724 (SSCI Journal; Impact Factor: 11.18)

Abstract
Academic delay can be understood as individuals’ behavioral postponement of their work in academic settings. The purpose of this study was to examine the relations between academic delay, motivational beliefs, and academic achievement in an online science course. The sample included one hundred and sixty-eight undergraduate students. Leveraging learning analytics and using multilevel modeling, this study found that academic delay was composed of habitual and momentary components. Controlling for motivational beliefs, habitual delay was significantly more negative than momentary delay in relation to academic achievement. The relation between academic delay and academic achievement was moderated by motivational beliefs. The negative effect of momentary delay on academic achievement was dependent on academic self-efficacy, while the negative effect of habitual delay on academic achievement was dependent on emotional cost. These findings further confirm that academic delay in online learning environments is not necessarily harmful for college students. Simply focusing on academic delay without considering who is engaging in such behavior could be misleading for future intervention.

Book Chapter on Motivational Features of Digital Math Curricula

Bowman, M., & Xie, K., & Hawk, N. (2023). The Motivational Features of Digital Math Curricula: A Framework for Supporting Pre-Service Teachers. In Zimmerman, A. (eds.), Handbook of Research on Advancing Teaching and Teacher Education in the Context of a Virtual Age, IGI Global. DOI: 10.4018/978-1-6684-8407-4

Abstract
With technology increasingly prevalent in schools, it is critical that pre-service teachers learn how to select and use technological resources, such as digital curriculum, to support student motivation and learning. By presenting a framework that maps certain digital mathematics curriculum features onto specific motivational factors, this book chapter provides guidance for teacher education programs to select and adopt digital curricula that not only supports content learning but also promotes students’ motivation for learning. To build the theory, the authors conducted an exploratory case study, examining the use of a specific digital mathematics curriculum in a middle school classroom. Results demonstrate that authenticity can aid student motivation, but culturally relevant pedagogy is also needed; interactivity can support motivation and engagement, but interactions should target students’ conceptual knowledge; easy to use curriculum supports learning, but should also be easily incorporated with a learning management system.

Publication on Communities and Engagement in K-12 Virtual Learning

Hawk, N., He, J., & Xie, K. (2022). A Comprehensive Framework of Engagement in K-12 Virtual Learning: Examining Communities of Support. In Zimmerman, A. (eds.), Research, Practice, and Innovations in Teacher Education During a Virtual Age, IGI Global. DOI: 10.4018/978-1-6684-5316-2.ch014

Abstract
The members of a virtual learning community, including both personal-based and school-based, are often critical to students’ overall academic success. However, the extent of these relationships differs quite widely in a virtual learning environment as compared to a traditional, face-to-face environment. While there is evidence of the importance of these relationships on academic achievement, less synthesized are how these members act together to impact student engagement, a critical function of online learning success. In this chapter, the authors describe a comprehensive framework, guided by prior research, and how students’ support system, both in school and at home, help impact four primary dimensions of student engagement, including behavioral, cognitive, social, and affective. Finally, the chapter provides some recommendations for educator preparation programs. (more…)

Presentations at AERA 2023 Chicago

In the upcoming 2023 Annual Meeting for American Educational Research Association (AERA) in April 13-16, 2023, Chicago, our lab members will be involved in the following presentations:

Xie, K., Jiang, Z., Pan, Z., Men, Q., & He, J. (2023, April). Examining Learning Engagement through Sequential Pattern Mining. Poster session in Division C – Learning and instruction.

Shortt, M., Beard, K.S., & Xie, K. (2023, April). The Emergence and Escalation of Online Racial Discrimination in Digital Spaces: A Systematic Review of Literature. Roundtable Session in Division G – Social Context of Education.

Xie, K., Huang, L., Shortt, M., He, J., & Jiang, Z. (2023, April). The Role of Students’ Motivation on Engagement and Performance in Online Learning in Higher Education Settings: A Systematic Analytical Review. Paper session in SIG – Online Teaching and Learning

He, J., Xie, K., Jiang, Z., & Vongkulluksn, V.W. (2023, April). Examining motivation development during adolescence using a combination of variable-centered and person-centered approaches. Poster session in SIG – Motivaiton in Education.

Xie, K., Cheng, S.L., Huang, L., & Tang, H. (2023, April). Evaluating Open Educational Resources as a Teacher Digital Competency in Higher Education. Paper Session in SIG – Instructional Technology

Xie, K. (2023, April). Examining Constructivist Learning in Context and in the Moment. Symposium Session: Making Constructivist Learning Feasible: Exploring Convergence of Virtual Reality and Learning Analytics for Future Research Directions in SIG – Technology, Instruction, Cognition & Learning.

Xie Offers Keynote at 14th International Conference on Education Technology and Computers

Dr. Kui Xie served as a keynote speaker at the 14th International Conference on Education Technology and Computers hosted by University of Barcelona in Barcelona, Spain on October 28-30, 2022. In his talk, he shared his talk entitled “Examining the Multidimensionality, Situatedness, and Longitudinal Nature of Engagement in Online Learning”.

Abstract:
Student engagement has been recognized as one of the most important factors of learning and achievement in academic settings. Ample evidence in the literature has shown the critical role of engagement leading to academic success. In this talk, I will situate the engagement framework in online learning environments and discuss the specific indicators of engagement in online learning. I will elaborate three aspects of engagement that are particularly important online learning: (1) the multi-dimensionality of engagement (behavioral, cognitive, affective, and social engagement), (2) the situatedness of engagement, (3) the longitudinal nature of engagement. I will discuss methodological issues in engagement research, looking at research methods such as traditional self-reports, experience-sampling method, and learning analytics.

Digital Transformation in Higher Education: 7 Areas for Enhancing Digital Learning


Credit: TierneyMJ / Shutterstock.com © 2022

by Florence Martin and Kui Xie

This article reflects on current practices and directions for digital transformation through a framework that supports the strategic responses and structural changes that higher education institutions could implement to enhance digital teaching and learning.


Higher education is in the era of digital transformation (Dx). Learning technologies and digital platforms are no longer an afterthought; they are critical for teaching and learning. The COVID-19 pandemic served as a catalyst for Dx, forcing colleges, universities, instructors, and students to shift online rapidly. Some instructors and students were prepared for the shift; those who were unprepared had to catch up quickly.Footnote1 This article reflects on current practices and directions for Dx through a framework that supports the strategic responses and structural changes that higher education institutions could implement to enhance digital teaching and learning. (more…)

Lab welcomes two new members

Two new members have joined our lab this Fall—Jessica Bates and Mike Frazier—who are first year PhD students in the Learning Technologies program.

Jessica has been an educator and online course developer since 2008. She currently is the K-8 Curriculum Supervisor for Virtual Virginia, a program of the Virginia Department of Education. In this role, she oversees course design and development for multiple levels and content areas. She also provides training in instructional design for online course developers. Her academic interests include instructional design and development, technology integration in teaching and learning, social emotional learning in the virtual setting, and learner’s motivation and engagement in the digital environment.

Mike joins us after teaching English, Writing, and Education Technology for eleven years in K-12 and Higher Education settings in both central Ohio and South Korea. With degrees and certifications in English, English Education, Education Technology, and Teacher Leadership, he is now interested in furthering the field through research and professional development, keeping one foot in the classroom and one foot in academia.

Xie Offers Keynote at 15th International Computer and Instructional Technologies Symposium

Dr. Kui Xie served as a keynote speaker at the 15th International Computer and Instructional Technologies Symposium hosted by Çanakkale Onsekiz Mart University in Turkey on September 7-9, 2022. In his talk, he shared his research about student engagement in online learning environments.

Abstract:
Student engagement has been recognized as one of the most important factors of learning and achievement in academic settings. Ample evidence in the literature has shown the critical role of engagement leading to academic success. In this talk, I will situate the engagement framework in online learning environments and discuss the specific indicators of engagement in online learning. I will elaborate three aspects of engagement that are particularly important online learning: (1) the multi-dimensionality of engagement (behavioral, cognitive, affective, and social engagement), (2) the situatedness of engagement, (3) the longitudinal nature of engagement. I will discuss methodological issues in engagement research, looking at research methods such as traditional self-reports, experience-sampling method, and learning analytics. In addition, I will discuss approaches to support student engagement in online learning.

Publication on Teachers’ Knowledge and Beliefs in Technology Integration

Lu, G., Liu, Q., Xie, K., Long, T., Zheng, X. (online first). Quality or quantity: How do teachers’ knowledges and beliefs ‘persuade’ their technology integration in a massive government-led training programme? The Asian-Pacific Education Researcher. DOI: https://doi.org/10.1007/s40299-022-00668-z (SSCI Journal; Impact Factor: 2.561)

Abstract
Technology integration refers to the process of innovation diffusion, from understanding technology knowledge to achieving a high level quality of technology usage. To address the gap in research regarding the critical factors that influence K-12 teachers’ practice of technology integration at the initial stage of a massive government-led training programme in rural areas, this study sought to explore the comprehensive effects of teachers’ technological pedagogical content knowledge (TPACK) and beliefs on their technology integration. Participants included 267 in-service K-12 teachers from four pilot schools in a remote rural area in north-western China. We found that: (a) T-related knowledge positively predicted the teachers’ technology integration quality and quantity, while T-unrelated knowledge negatively influenced technology integration quality and quantity; (b) the positive valence of teachers’ beliefs (PVTBs) was positively related to technology integration quality, the negative valence of teachers’ beliefs was negatively related to technology integration quantity; (c) the PVTBs impacted the quality and quantity of technology integration through T-related knowledge; and (d) gender significantly moderated the impact of positive beliefs and T-related knowledge on technology integration. This study suggests that improving K-12 teachers’ technology integration in rural areas requires prioritizing professional development programmes that improve teachers’ beliefs in the initial stage of technology diffusion.

Publication on Multilevel Latent State-Trait Models

Vongkulluksn, V.W. & Xie, K. (2022). Multilevel latent state-trait models with experiential sampling data: An illustrative case of examining situational engagement. Open Education Studies, 4(1), 252-272. https://doi.org/10.1515/edu-2022-0016 (Open Access with Free Downloads)

Abstract
Learning processes often occur at a situational level. Changes in learning context have implications on how students are motivated or are able to cognitively process information. To study such situational phenomena, Experience Sampling Method (ESM) can help assess psychological variables in the moment and in context. However, data collected via ESM is voluminous and imbalanced. Special types of statistical modeling are needed to handle this unique data structure in order to maximize its potential for scientific discovery. The purpose of this paper is to illustrate how Latent State-Trait modeling used within a multilevel framework can help model complex data as derived by ESM. A study of situational engagement is presented as an illustrative case. We describe methodological considerations which facilitated the following analyses: (1) Decomposition of trait-level and state-level engagement; (2) Group differences in variance decomposition, and (3) Predicting state component of engagement. Discussions include the relative advantages and disadvantages of ESM and multilevel Latent State-Trait modeling in facilitating situational psychological research.

Keywords:Experiential Sampling; Situational Engagement; Latent Variable Modeling; Latent State-Trait Modeling