Wanli Xing

Learning analytics in CSCL with a focus on assessment: an exploratory study of activity theory-informed cluster analysis

In this paper we propose an automated strategy to assess participation in a multi-mode math discourse environment called Virtual Math Teams with Geogrebra (VMTwG). A holistic participation clustering algorithm is applied through the lens of activity theory. Our activity theory-informed algorithm is a step toward accelerating heuristic approaches to assessing collaborative work in synchronous technology mediated environments like VMTwG. Our Exploratory findings provide an example of a novel, time-efficient, valid, and reliable participatory learning assessment tool for teachers in computer mediated learning environments. Scaling online learning with a combination of computation and theory is the overall goal of the work this paper is situated within.

Building models explaining student participation behavior in asynchronous online discussion

Previous studies have invested much effort in understanding how participation in asynchronous online discussion affects student learning, and what factors influence student participation behavior. Results of these studies have been inconclusive and these investigations are often conducted from isolated perspectives. Relying on social cognitive theory, this study proposes two dynamic student participation models in online dialogue and particularly highlights understudied factors - collective efficacy, social ability, reading behavior, the time dimension of participation - to examine the mediation and causal relationship among those factors and their influence on learning. The models are tested utilizing data collected from a large US university. Specifically, while the predictive constructs are operationalized through the survey instruments, the outcome measures are modeled using electronic trace data and actual evaluation information. Data is analyzed using the Partial Least Squares modeling method. Results demonstrate the intertwined relationship among constructs and a different influencing mechanism for each construct on participation behavior and learning. By comparing these two built models, the time dimension of participation is shown to be more influential in predicting student learning than posting and reading actions. The paper concludes with a discussion of the implications of this study. Shows two social cognitive theory informed online student participation models.Applies collective efficacy, social ability, and temporal dimensions to online learning.Computational model shows the relationship between participation behavior and learning.The time dimension of model predicts student learning.

Twitter Archeology of learning analytics and knowledge conferences

The goal of the present study was to uncover new insights about the learning analytics community by analyzing Twitter archives from the past four Learning Analytics and Knowledge (LAK) conferences. Through descriptive analysis, interaction network analysis, hashtag analysis, and topic modeling, we found: extended coverage of the community over the years; increasing interactions among its members regardless of peripheral and in-persistent participation; increasingly dense, connected and balanced social networks; and more and more diverse research topics. Detailed inspection of semantic topics uncovered insights complementary to the analysis of LAK publications in previous research.

Autistic youth in 3D game-based collaborative virtual learning: Associating avatar interaction patterns with embodied social presence: Autistic youth in 3D game-based learning

Abstract: This study examines interaction patterns in a series of game activities for learning social skills by autistic youth in a 3D game‐based collaborative virtual learning environment (CVLE). Researchers studying collaborative learning have indicated the importance of social interactions and social presence. However, few studies have examined the relationship of avatar social interactions with embodied social presence in a 3D game‐based CVLE. Specifically, we examined avatar‐mediated verbal and nonverbal interactions by autistic youth in iSocial, a game‐based 3D CVLE for developing social competencies. How are avatar‐mediated verbal and nonverbal social interactions related to the extent of embodied social presence? Building on prior studies on embodied social presence (ESP) theory and the verbal and nonverbal social interaction framework, this paper aims to explore the link between the combination of verbal (appropriate and inappropriate) and nonverbal (avatar proximity, orientation, joint attention and gesture) interaction patterns and experienced ESP level. We report on the results of 3 cohorts of a total of 11 youth aged from 11 to 14 who were diagnosed with Autism Spectrum Disorder learning in 13 game activities in iSocial. The video data of the participants’ screen recordings were analyzed and coded based on the ESP theory and verbal and nonverbal social interaction framework. Through cluster analysis, the results identify distinct patterns of verbal and nonverbal interaction that are associated with different levels of embodied social presence. The findings of this study (1) shed light on the link between social interactions and embodied social presence and (2) provide a deeper understanding of how the unique spatial and visual characteristics of 3D CVLE and the design of game activities in 3D CVLE may transform collaborative learning, especially for autistic youth. [ABSTRACT FROM AUTHOR]

Corrigendum to “Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization” [Computers in Human Behavior 58 (2016) 119–129]

Corrigendum to “Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization” [Computers in Human Behavior 58 (2016) 119e129] Wanli Xing a, , Xin Chen , Jared Stein , Michael Marcinkowski d a Department of Educational Psychology and Leadership, Texas Tech University, Lubbock, TX 79409, USA b School of Engineering Education, Purdue University, West Lafayette, IN 47906, USA c Instructure Incorporation, 6330 S 3000 E #700, Salt Lake City, UT 84121, USA d College of Information Sciences and Technology, Penn State University, State College, PA 16801, USA