Inferring Student Learning Behaviour from Website Interactions: A Usage Analysis (original) (raw)
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CHARACTERISING ON-LINE LEARNING ENVIRONMENTS
We propose a central feature of on-line learning environments as transactive communication -where participants respond to and build on to each other's contributions, developmentally toward a mutual outcome. This differs from the more didactic, or at least tutor-controlled, dialogue that takes place in traditional learning environments. In the on-line setting, therefore, communication is particularly perceived to be an essential part of the learning process. This provides a strong motive for research to determine its characteristics and functions. The need for good systems for describing and understanding the contexts of learning activity is also crucial. Who is teaching what to whom and why -and why they are there to learn -is of course critical for the understanding and from this the prediction of good pedagogical strategy. At the micro and macro levels of analysis, then, we need to review, revise, develop and implement tools for research to inform good development of practice.
Exploring student interactions in collaborative World Wide Web learning environments
1998
This paper describes a study in which a WWW learning environment was created using socio-constructivist instructional design principles. A qualitative research method was used to investigate the learning behaviours of classroom-based students in this instructional setting. In particular the study sought to investigate collaborative learner behaviours in settings where the instructional materials involved open-ended investigations and learner support by means of a printed guide.
2008
This study measures student learning, satisfaction, and interaction dynamics within Webbased online learning programs. The population of this study was students (n = 208) enrolled in multiple academic disciplines at an East Coast U.S. university. A Web-based research instrument was designed to assess students' characteristics, their perceptions of learning, satisfaction, student-to-student interactions and student-to-instructor interactions. A one-way analysis of variance (ANOVA) was conducted to see whether perceived learning, student satisfaction, student-student instructor interaction, and student-student interaction differed based on the discipline within these programs. No statistically significant differences were found. A one-way ANOVA was conducted to see whether perceived learning differed based on the technology used within these programs. No statistically significant differences were found here either. Research findings, limitations of research, and recommendations for future research are discussed.
Evaluating students pattern of interaction in online courses
2014
The primary focus in the research of online learning is interaction, which is central to an educational experience (Garrison & Cleveland-Innes, 2005). Although interaction alone does not represent participation (Garrison, Anderson & Archer, 2000), however interaction could promote participation and engage students in learning (Hrastinski, 2009). Researchers have widely agreed that in an e-learning environment, students could learn better by participating (Hrastinski, 2009). As distinguished by Moore (1989) that there are three dimensions of interaction in an e-learning environment: learner-instructor, learner-content and learner-learner. It has been suggested that participation of a student can be measured by accumulating the interactions that occurred in an e-learning forum (Davies & Graff, 2005; Lipponen et al., 2003). One of the key challenges in e-learning is to encourage participation (Bento & Schuster, 2003), which means that students need to be encouraged to interact online. ...
Building a Learning Experience: What Do Learners’ Online Interaction Data Imply?
Springer eBooks, 2019
It is still under debate whether learners' interaction data within e-learning and/or open learning environments could be considered as reflections of their learning experiences to be effective or not. Therefore, it is meaningful to explore the nature of these interactions and to make meaningful conclusions. This study aims to explore what the nature of these interactions in an e-learning environment and to describe whether there is a meaningful pattern in their interaction data. For this purpose, a course on Computer Networks and Communication was designed in an e-learning platform, where learners could receive real-time responses and monitor their process through dashboards as recommendations for their learning process. 31 metrics were gathered from database records, which yielded a common factor with six sub-factors, where the highest correlation was between learners-learning dashboards interactions and learners-learning objects. In addition, this factorial structure could be considered a holistic view of a learning experience based on the interaction within an e-learning environment. Another finding of this study indicated that learners' interaction with learning dashboards had been one and meaningful dimension of their overall learning experiences. The results of this study presents instructional design cues and pedagogical outcomes.