A Theoretical and Evidence-Based Conceptual Design of MetaDash: An Intelligent Teacher Dashboard to Support Teachers' Decision Making and Students’ Self-Regulated Learning

Abstract

Teachers’ ability to self-regulate their own learning is closely related to their competency to enhance self-regulated learning (SRL) in their students. Accordingly, there is emerging research for the design of teacher dashboards that empower instructors by providing access to quantifiable evidence of student performance and SRL processes. Typically, they capture evidence of student learning and performance to be visualized through activity traces (e.g., bar charts showing correct and incorrect response rates, etc.) and SRL data (e.g., eye-tracking on content, log files capturing feature selection, etc.) in order to provide teachers with monitoring and instructional tools. Critics of the current research on dashboards used in conjunction with advanced learning technologies (ALTs) such as simulations, intelligent tutoring systems, and serious games, argue that the state of the field is immature and has 1) focused only on exploratory or proof-of-concept projects, 2) investigated data visualizations of performance metrics or simplistic learning behaviors, and 3) neglected most theoretical aspects of SRL including teachers’ general lack of understanding their’s students’ SRL. Additionally, the work is mostly anecdotal, lacks methodological rigor, and does not collect critical process data (e.g. frequency, duration, timing, or fluctuations of cognitive, affective, metacognitive, and motivational (CAMM) SRL processes) during learning with ALTs used in the classroom. No known research in the areas of learning analytics, teacher dashboards, or teachers’ perceptions of students’ SRL and CAMM engagement has systematically and simultaneously examined the deployment, temporal unfolding, regulation, and impact of all these key processes during complex learning. In this manuscript, we 1) review the current state of ALTs designed using SRL theoretical frameworks and the current state of teacher dashboard design and research, 2) report the important design features and elements within intelligent dashboards that provide teachers with real-time data visualizations of their students’ SRL processes and engagement while using ALTs in classrooms, as revealed from the analysis of surveys and focus groups with teachers, and 3) propose a conceptual system design for integrating reinforcement learning into a teacher dashboard to help guide the utilization of multimodal data collected on students’ and teachers’ CAMM SRL processes during complex learning.

Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques

“…This is a significant element for reflection, since these differences may be due to various factors related to the students' own characteristics, such as digital competences (García-Peñalvo, 2021), cognitive, metacognitive, affective strategies, etc. (Bártolo-Ribeiro et al, 2020;Cloude et al, 2019;Wiedbusch et al, 2021;Yilmaz et al, 2020;Yoon et al, 2021), learning style and their response to Self-regulated learning (Valadas et al, 2017) or teacher characteristics, also related to digital competences and teaching style. These aspects will be addressed in future studies.…”

“…Furthermore, LMSs allow for continuous monitoring and adaptation to the student's learning pace, which increases the use of metacognitive and motivational strategies during the learning process (Cloude et al, 2019). All of which will facilitate the generalisation of metacognitive skills to achieve learning objectives (Wiedbusch et al, 2021). Therefore, in this environment, collaborative student work in small groups can be implemented, which will facilitate the work dynamics within the PBL methodology (Shanmuganeethi et al, 2020).…”

Closing the loop – The human role in artificial intelligence for education

“…One way is the use of teacher dashboards, which provide teachers with elaborated information about students’ learning processes. Further, teacher dashboards can automatically suggest support measures for specific learners (see Wiedbusch et al, 2021 ).…”

Game-Based Learning Analytics for Supporting Adolescents’ Reflection

“…However, considerable research is still required. Implementing game-learning analytics in the classroom for instructional decision making will also require additional support and technological resources, such as a dashboard to illustrate data visualizations for sense making that are currently unavailable to most teachers (Perez-Colado et al, 2017;Cloude, Dever, Wiedbusch, & Azevedo, 2020;Wiedbusch et al, 2021;Roll & Winne, 2015).…”

“…When is the ideal time to prompt learners to reflect, and does that time vary based on the learning goal and environment? Researchers should also consider the role of adolescents' motivation, cognitive load, or level of knowledge of problem solving, since game-learning analytics data could help pinpoint when learners are not engaging in reflection (e.g., if the quality of solution-based reflection and motivation is low) to inform instructional decision making (Winne et al, 2019;Winne, 2017;Cloude et al, 2020;Wiedbusch et al, 2021).…”