Applying and Evaluating Visualization Design Guidelines for a MOOC Dashboard to Facilitate Self-Regulated Learning Based on Learning Analytics

Abstract

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.

Supporting self-regulated learning with learning analytics interventions – a systematic literature review

“…The sizes of the samples involved in the studies varied. Some studies had less than 10 participants (Cha & Park, 2019;Inventado et al, 2013;Wise et al, 2014Wise et al, , 2016, whereas the largest population was made of 33,726 learners (Davis et al, 2017). The mean population of the reviewed studies was 1,398 participants and the median was 102 participants.…”

“…Some studies explained the early phases of their LA intervention design from a technological point of view (Frey et al, 2016;Manso-Vázquez & Llamas-Nistal, 2015;Siadaty et al, 2016b;Winne et al, 2019). Other studies examined the next phases with the perspective of user's experience on the interface of the intended LA intervention (Cha & Park, 2019;Rohloff et al, 2019;Sonnenberg & Bannert, 2016), and the rest of the studies tested the effectiveness of the LA intervention in action.…”

“…Studies focused on their LA intervention design tended to use qualitative methods, such as interviews, the think-aloud method, and focus groups, to verify the usability, usefulness, and learners' perception of an LA intervention. Interviews (Cha & Park, 2019;Corrin & de Barba, 2014;Wise et al, 2014Wise et al, , 2016 were used to discover how learners perceived the functionalities and features of LA interventions and to find approaches that learners would be willing to use in their learning processes. Sonnenberg and Bannert (2016, 2019) used the think-aloud method to gain insight into these learner perceptions and to identify how useful a design was for a learner.…”

Human-centered Approaches to Data-informed Feedback

“…However, learning analytics-based feedback tends to focus more on where learners currently are or where they are likely to be (if predictive modelling is used), but less on what to do to move towards or beyond an expected standard of a task [58]. For example, a study conducted by Cha and Park [13] shows that while dashboards may help learners monitor their learning progress and time management, learners desire prescriptive tips and recommendations to help them achieve learning goals. The lack of actional information in LA-based feedback has also been partly attributed to the disconnect with learning design.…”

Interface Design for E-Learning: Investigating Design Characteristics of Colour and Graphic Elements for Generation Z

“…Nevertheless, their participation and completion rate in e-learning is still low [10], [11], [12] even though generation Z students monopolize higher education nowadays. Although elearning is popular among the younger generation yet the dropout rate in e-learning is still high [13], [14] compared to conventional learning and is considered more of a supplement to the conventional educational approach [15].…”