The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a Learning Management System (LMS). Test was attempted of a structural equation model representing the relationships among Time and Study Environment Management (TSEM), one of the sub-constructs of MSLQ, influencing a set of time-related online log variables: login frequency, login regularity, and total login time. Data were collected from 188 college students in a Korean university. Employing structural equation modeling, a hypothesized model was tested for measuring the model fit. The results presented a criterion validity of online log variables to estimate their time management.  The structural model including TSEM, online variable, and final score with a moderate fit indicated that learners’ time related online behavior mediates their psychological functions and their learning outcome. Based on the results, the final discussion includes the recommendations for further study and the meaningfulness in regard to the expantion of  Learning Analtyics for Performance and Action (LAPA) model.

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Abstract. This study analyzed students' interaction patterns in asynchronous online discussion forums by using log files left in the LMS. In taking Social Network Analysis (SNA) and Learning Analytics (LA) approach, the centrality of participants, their networking patterns, characteristics of networks among multiple topics, and changed patterns by time were reviewed within a case study. Additionally, this study found that the instructor's initiation, students' autonomy to select topics, together with the use of sample essays influenced the online discussion patterns, which is effectively illustrated by SNA results. Finally, this study discussed that not only the use of SNA as an analytics tool but also the display of the SNA outputs as a presentation tool can facilitate their smart and effective discussion activity.

This study investigates how individuals collaborativelyconstructed shared knowledge during a group activity. The dataset was collected from group activities for pre-service teachers in professional development. Participants designed body poses and action sequences that could help their students’ mathematical conceptualization. Using k-means clustering and Principal component analysis, patterns of individuals’ contributions based on their verbal and gesturalbehavior identified two groups of individuals: (1) Those who contributed to the discussion by speaking and gesturing frequently (~ 25% of the participants), and (2) those who mostly listened and focused on design ideas presented by others. Furthermore, epistemic network analysis corroborated significant differences in discourse patterns between the clusters, the results of which have significant implications for collaborative embodied learning and application for teacher education and professional development.