Poster #140 - Exploration of Elementary Students' Learning Behaviors in an e-Learning Platform I-TokTok

• In Event: Poster Session 11

Abstract

The Covid-19 pandemic has heightened the global need for and use of educational technology (EdTech) (World Bank, 2021). South Korea has applied a phased approach to EdTech integration since 1996– developing educational content/platforms, establishing infrastructure, providing professional development, and distributing technologies (World Bank, 2022). One e-learning platform in Korea is I-TokTok, developed in 2018 by Gyeongnam Office of Education and Dataeum. I-TokTok is aligned closely with Korea’s national curricula and contains digital textbooks, instructional videos/activities, and built-in assessments across six core subjects. It also provides an assignment portal, content creation and message boards, and forum rooms. I-TokTok was first launched in September 2021 and is being used by approximately 1,033 schools and 184,908 students (grades 1-12) in Gyeongsangnam-do province as their main e-learning platform.

Similar e-learning platforms (e.g., Canvas, Google Classroom) have been used in the classrooms. However, very little educational research has been conducted with user learning data collected from platforms, especially with elementary students. The present study addresses this limitation by examining Big Data collected through I-TokTok. The goal was to gain a deeper understanding of elementary school students’ e-learning behavioral patterns and scenarios that are tied to learning initiatives.

Elementary students are important to examine because they are more vulnerable to learning loss than older students due to differences in cognitive abilities and their inefficiency in accessing learning independently (Patrinos and Donnelly, 2021). The user data during the Spring semester 2022 were extrapolated from 3rd-6th graders (N = 850) randomly stratified from 141,926 students.

We followed Caliper Analytics Specifications, common standards utilized for collecting learning data from digital resources (IBM Global, 2022). Exploratory factor analysis was performed on the set of 23 items. Items with poor factor loadings were deleted, with the final set containing 14 items (see Table 1). Results revealed three-factor model with factors representing Academic Engagement (accessing course material, time on task, completing assignments on time), Social Engagement (prosocial academic behavior of providing and responding to feedback), and Community Engagement (reciprocal community engagement), 𝝌2(52) = 348.67, p < 0.01, CFI = 0.9, TLI = 0.82, SMRI = 0.04.

Confirmatory factor analysis on an independent sample (50% of N) confirmed the three-factor model in EFA, 𝝌2(83) = 541.75, p < 0.01, CFI = 0.81, TLI = 0.76, SMRI = 0.11 (See Figure 1). Age/grade were introduced as a covariate. Modification indices were used to improve fit. All loadings were significant, p < 0.01. Results of analyses on the relationship between these e-learning behaviors and other user activity data and academic performance will be presented.

Understanding how students use and engage in e-learning platforms is a first step towards providing recommendations for any EdTech development and implementation. As school systems around the world prepare for the future recurrence of educational disruption as experienced during the COVID-19 pandemic, learning analytics such as the one presented can provide significant promise in understanding and optimizing the e-learning environment. Future directions using user learning data from I-TokTok will be discussed.

Authors

• Jong Tak Lee, Fox Edu

• Jae Hee Paik, San Francisco State University
  Presenting Author

• Igor Himelfar, Department of Psychometrics and Research at National Board of Chiropractic Examiners

• Kyoung Mi Yoo

• Mi Jin Kim, Fox Edu

• Rita Rodriguez, San Francisco State University

• Seung Hee Yoo, The Wright Institute