• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2799-2823
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• 2019

With the help of learning analytics, MOOCs have wider potential to succeed in learning through promoting self-regulated learning (SRL). The current study aims to apply and validate visualization design guidelines for a MOOC dashboard to enhance such SRL capabilities based on learning analytics. To achieve the research objective, a MOOC dashboard prototype, LM-Dashboard, was designed and developed, reflecting the visualization design guidelines to promote SRL. Then, both expert and learner participants evaluated LM-Dashboard through iterations to validate the visualization design guidelines and perceived SRL effectiveness. The results of expert and learner evaluations indicated that most of the visualization design guidelines on LM-Dashboard were valid and some perceived SRL aspects such as monitoring a student's learning progress and assessing their achievements with time management were beneficial. However, some features on LM-Dashboard should be improved to enhance SRL aspects related to achieving their learning goals with persistence. The findings suggest that it is necessary to offer appropriate feedback or tips as well as to visualize learner behaviors and activities in an intuitive and efficient way for the successful cycle of SRL. Consequently, this study contributes to establishing a basis for the visual design of a MOOC dashboard for optimizing each learner's SRL.

• The Journal of Korean Association of Computer Education
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• v.21 no.3
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• pp.35-50
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• 2018

Online dashboard is designed to support learners' self-regulation of their learning process and activities to promote learner-centered learning. Given the dashboard usually provides information within a limited space, it is important to define which information should be presented in order to meet the various needs of online learners. We analyzed relevant literature, existing dashboards, and learners' dashboard experiences, and identified a list of information that should be provided by the dashboard. As a result, four categories including learning preparation, learning participation, interaction, and learning outcomes, and eleven sub-categories of dashboard information were extracted. The results suggest implications for the design of online dashboard for learner-centered learning.

• Educational Technology International
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• v.20 no.2
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• pp.223-255
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• 2019

The purpose of this study is to draw implications for designing online tools to support monitoring in collaborative learning. For this purpose, eighteen research papers that explored learner dashboards and group awareness tools were analyzed. The driving questions for this analysis related to the information and outcomes that must be monitored, whose performance they represent, and who monitors the extent of learning. The analytical frameworks used for this study included the following: three modes of co-regulation in terms of who regulates whose learning (self-regulation in collaborative learning, other regulation, and socially shared regulation) and four categories of dashboard information to determine which information is monitored (information about preparation, participation, interaction, and achievements). As a result, five design implications for learner dashboards that support monitoring were posited: a) Monitoring tools for collaborative learning should support multiple targets: the individual learner, peers, and the entire group; b) When supporting personal monitoring, information about the individual and peers should be displayed simultaneously to allow direct comparison; c) Information on collaborative learning achievements should be provided in terms of the content of knowledge acquired rather than test scores; d) In addition to information related to interaction between learners, the interaction between learners and learning materials can also be provided; and e) Presentation of the same information to individuals or groups should be variable.

• Educational Technology International
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• v.18 no.2
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• pp.73-99
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• 2017

The learning analytics dashboard (LAD) is a supporting tool for teaching and learning in its personalized, automatic, and visual aspects. While several studies have focused on the effect of using dashboard on learning achievement, there is a research gap concerning the impacts of learners' characteristics on it. Accordingly, this study attempted to verify the differences in learning achievement depending on learning motivation level (high vs. low) and dashboard intervention (use vs. non-use). The final participants were 231 university students enrolled in a basic statistics course. As a research design, a 2 × 2 factorial design was employed. The results showed that learning achievement varied with dashboard intervention and the interaction effect was significant between learning motivation and dashboard intervention. The results imply that the impact of LAD may vary depending on learner characteristics. Consequently, this study suggests that the dashboard interventions should be offered after careful consideration of individual students' differences, particularly their learning motivation.

• The Journal of Korean Association of Computer Education
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• v.20 no.6
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• pp.1-15
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• 2017

The purpose of this study is to design a LMS(Learning Management System) dashboard for instructor feedback support based on learning analytics and to apply a LMS dashboard incorporating such taxonomy which allows an instructor to give a student personalized feedback according to the class content and a student's traits. In the dashboard design phase, usable instructional data were selected from LMS based on feedback taxonomy in terms of learning analytics. Two validity tests were conducted with 8 instructional technologists over 8 years of experience, and were revised accordingly. The final dashboard screen has three parts: A comprehensive analysis screen to provide appropriate feedback based on instructor feedback taxonomy analysis, a summary screen for learner analysis, and a recommended feedback guide screen. Detailed analysis information are provided through other dashboards that are displayed in eight screens: login analysis, learning information confirmation analysis, teaching materials learning analysis, assignment/tests, and posts analysis. All of these dashboards were represented by analysis information and data based on learner analytics through visualization methods including graphs and tables. The implications of educational utilization of the dashboard for instructor feedback support based on learning analytics and the future researches were suggested based on these results.