Learning at distance: Effects of interaction traces on academic achievement

Abstract

a b s t r a c tContemporary literature on online and distance education almost unequivocally argues for the importance of interactions in online learning settings. Nevertheless, the relationship between different types of interactions and learning outcomes is rather complex. Analyzing 204 offerings of 29 courses, over the period of six years, this study aimed at expanding the current understanding of the nature of this relationship. Specifically, with the use of trace data about interactions and utilizing the multilevel linear mixed modeling techniques, the study examined whether frequency and duration of studentestudent, studenteinstructor, studentesystem, and studentecontent interactions had an effect of learning outcomes, measured as final course grades. The findings show that the time spent on studentesystem interactions had a consistent and positive effect on the learning outcome, while the quantity of studentecontent interactions was negatively associated with the final course grades. The study also showed the importance of the educational level and the context of individual courses for the interaction types supported. Our findings further confirmed the potential of the use of trace data and learning analytics for studying learning and teaching in online settings. However, further research should account for various qualitative aspects of the interactions used while learning, different pedagogical/media features, as well as for the course design and delivery conditions in order to better explain the association between interaction types and the learning achievement. Finally, the results might imply the need for the development of the institutional and program-level strategies for learning and teaching that would promote effective pedagogical approaches to designing and guiding interactions in online and distance learning settings.

Unravelling the dynamics of instructional practice

“…Within the LAK community, substantial progress has been made both in conceptual development [10,17] as well as how to design appropriate predictive learning analytics to support students [19,26]. Nonetheless, in line with [26,44] findings from LA research in the past have been rather limited to delivering actionable feedback, while ignoring the context of which the learning data is situated.…”

Visualizations of Online Course Interactions for Social Network Learning Analytics

“…The course took place between September 2013 and January 2014, and the main reasons for this choice were the structure of the course and the high heterogeneity of students -the course is a core course in the Business Administration degree, but an elective course in the other three degrees. This is interesting because there is a positive association between studentstudent interactions in core and elective courses, larger in the case of core courses [28].…”

Mixing and Matching Learning Design and Learning Analytics

“…Substantial progress has been made both in conceptual development [5,6] as well as how to design appropriate predictive learning analytics to support students [7,8]. Nonetheless, in line with [7,9], findings from LA research in the past have been rather limited to delivering actionable feedback, while ignoring the context in which the learning data is situated.…”

“…Substantial progress has been made both in conceptual development [5,6] as well as how to design appropriate predictive learning analytics to support students [7,8]. Nonetheless, in line with [7,9], findings from LA research in the past have been rather limited to delivering actionable feedback, while ignoring the context in which the learning data is situated. Thus, there is an increasing interest to align LA with LD, as the former facilitates the transfer of tacit educational practice to an explicit rendition, while the latter provides educators with pedagogical context for interpreting and translating LA findings to direct interventions [10][11][12][13][14].…”