Yeonjeong Park

Educational Dashboards for Smart Learning: Review of Case Studies

An educational dashboard is a display which visualizes the results of educational data mining in a useful way. Educational data mining and visualization techniques allow teachers and students to monitor and reflect on their online teaching and learning behavior patterns. Previous literature has included such information in the dashboard to support students’ self-knowledge, self-evaluation, self-motivation, and social awareness. Further, educational dashboards are expected to support the smart learning environment, in the perspective that students receive personalized and automatically-generated information on a real-time base, by use of the log files in the Learning Management System (LMS). In this study, we reviewed ten case studies that deal with development and evaluation of such a tool, for supporting students and teachers through educational data mining techniques and visualization technologies. In the present study, a conceptual framework based on Few’s principles of dashboard design and Kirkpatrick’s fourlevel evaluation model was developed to review educational dashboards. Ultimately, this study is expected to evaluate the current state of educational dashboard development and suggest an evaluative tool to judge whether or not the dashboard function is working properly, in both a pedagogical and visual way.

Using log variables in a learning management system to evaluate learning activity using the lens of activity theory

As the advance of learning technologies and analytics tools continues, learning management systems (LMSs) have been required to fulfil the growing expectations for smart learning. However, the reality regarding the level of technology integration in higher education differs considerably from such expectations or the speed of advances in educational technologies. This research aimed to evaluate the current activation levels and usage patterns of a LMS. A large data-set was analysed, which included the online activity information from 7940 courses. Through data pre-processing, general indicators reflecting login frequencies of the virtual campus and activity-based indicators presenting the activation patterns of diverse functions provided by Moodle were derived. Activity theory was applied to interpret the results of analysis, since it has been recognised as a powerful framework to understand phenomena encompassing interactive systems. Further, time-series investigation over three consecutive semesters allowed observation of historical changes. The results revealed considerably low use of the virtual campus with only slight changes, as well as significantly different activity patterns across course attributes and colleges. Contradictions among components in the activity system are discussed, along with the implications for improving teaching and learning with LMS in higher education.

Learning from MOOCs: a qualitative case study from the learners’ perspectives

This study describes the massive open online course (MOOC) experiences of three educational technology scholars assuming the roles of learners. Adapting Carroll’s model of school learning as a theoretical framework, the study employed an autoethnography method to collect empirical data in three different MOOCs. Data analysis from regularly recorded journals revealed commonalities and differences in learner experiences. Based on the results, a refined version of Carroll’s model was produced to provide a foundation for future research and development into MOOCs.

Towards Smart Asynchronous Discussion Activity: Using Social Network Analysis to Investigate Students’ Discussion Patterns

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

Tracking Students’ Eye-Movements on Visual Dashboard Presenting Their Online Learning Behavior Patterns

This study aims to investigate students’ reactions and perceptions to the Learning Analytics Dashboard (LAD). LAD was designed and developed by researchers to present students’ online learning activity in a visualized display. An eye tracking system was incorporated to measure students’ eye-movement, including eye fixation, saccade and their sub derivatives on LAD. The results are derived from the data-mining of what the eye-tracking system generates. This study is expected to support a smart learning environment, where students can effectively monitor their online behavior patterns in real-time using their mobile devices. Students can utilize such information to change their learning patterns, and improve performance.