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Development of a Learning Dashboard Prototype Supporting Meta-cognition for Students

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This poster presents an initial prototype design and development of a learning analytics dashboard supporting self-regulated learning, from which the students can benefit from LA more directly. The current stage of the development focuses on providing visualizations of learning processes and behaviors extracted from operation log data of an e-book system for self-monitoring. An overview of the reading paths and time of the slide pages in a class and a detailed view of the activities and learner-created content on the selected page are provided with a comparison of the class overall states and those of the learner. This work is expected to invoke the future developments and practical experiments of an LA dashboard supporting different phases of self-regulated learning.
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Companion Proceedings 10th International Conference on Learning Analytics & Knowledge (LAK20)
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Development of a Learning Dashboard Prototype Supporting
Meta-cognition for Students
Min Lu1*, Li Chen1, Yoshiko Goda2, Atsushi Shimada1, Masanori Yamada1**
1 Kyushu University, Japan; 2 Kumamoto University, Japan
* lu@artsci.kyushu-u.ac.jp, ** mark@mark-lab.net
ABSTRACT: This poster presents an initial prototype design and development of a learning
analytics dashboard supporting self-regulated learning, from which the students can benefit
from LA more directly. The current stage of the development focuses on providing
visualizations of learning processes and behaviors extracted from operation log data of an e-
book system for self-monitoring. An overview of the reading paths and time of the slide
pages in a class and a detailed view of the activities and learner-created content on the
selected page are provided with a comparison of the class overall states and those of the
learner. This work is expected to invoke the future developments and practical experiments
of an LA dashboard supporting different phases of self-regulated learning.
Keywords: self-monitoring, dashboard, learning analytics, visualization
1 INTRODUCTION
Learning analytics (LA) with the large-scale educational log data obtained from e-learning
environments can benefit both the instructors and learners with different kinds of feedback.
Although researches of LA dashboards have become popular in recent years; however, as the most
visible results are designed for the instructors, the learners cannot benefit from LA in a direct
manner. On the other hand, monitoring the learner's own learning behaviors and processes is an
important aspect of self-regulated learning because it helps learners to be aware of their
weaknesses or deficiencies in their learning processes and regulates their learning strategies (Hofer
et al., 1998). Visualization is useful for learners to be aware of what they have been doing and what
they should do by making such information salient for them (Yen et al., 2018). Our prior research has
designed a learning analytics dashboard supporting metacognition to improve self-regulated
learning in online environments through the collection, analysis, and visualization of learning log
data (Chen et al., 2019). This poster presents an initial prototype of the dashboard, focusing on
supporting self-monitoring, which invokes future experiments, evaluations, and developments.
2 UI DESIGN OF THE LA DASHBOARD FOR STUDENTS
For self-monitoring, learners are expected to focus on the learning processes rather than the
outcome only (Zimmerman, 1998). Thus, the dashboard intends to provide the students with the
processes and behaviors visualized from learning log data in two types of views (Chen et al., 2019).
The first one is an overview of the learning activities on all the slide pages in a class. The second one
is the learning behaviors on a single page. As the point of self-evaluation is a comparison of one’s
own performance or behaviors based on certain criteria or standards (Belfiore & Hornyak, 1998),
both views provide comparisons between the class's overall situations and the user’s behaviors.
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Figure 1: Overview of reading path and time on all the slide pages in a class
Figure 2: Detailed view of the activities and learner-created content on the selected page
2.1 Overview of All the Slide Pages in a Class
A graph to visualize the slide reading path and time with the nodes on a circle stand for pages and
the links between the nodes stand for the reading path (Figure 1). The intensity of a node’s color
indicates the reading time spent on the page, and the thickness of a link shows the number of the
page transit. The accessories, which are smaller circles attached to a page node, present the
recorded learning behaviors, including highlight markers, memo annotations, on the page. The
intensity of an accessory’s color indicates the number of corresponded behaviors.
2.2 Detailed View of Each Slide Page
When the learner clicks a page node in the overview, the details of the reading time and learning
behaviors will be displayed with the learner-created content overlapped on the slide page (Figure 2).
The left view shows the average reading time of the class and all the highlight markers and memo
annotations created on the page, while the right view shows those of the learner.
3 PROTOTYPE DEVELOPMENT
We mainly developed a data processing module and a web-based visualization module to realize the
above design, mainly with the operation event logs from the e-book system of our university.
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3.1 Data Processing Module
As the accumulated operation event logs are from different courses and students, this module at
first filter the records according to slides and students’ IDs, and then extract the sequences of page-
transit related events by time after a data cleansing of events with too short intervals (<0.5s). From
such sequences, the time spent on each page and the numbers of the “from-to” links between each
pair of pages can be calculated. The overall states of the class can be obtained by summarizing the
result from all the students. The results are stored in JSON files for the visualization module.
3.2 Web-based Visualization Module
This module is developed based on D3.js, and the visual elements (e.g., the nodes and links) are
implemented as Scalable Vector Graphics (SVG), thus can be interactive to the user’s mouse
operation. This module also provides programming interfaces for setting up parameters of the
visualization, such as the size of the visual elements, colors of the nodes, accessories, links, and
intensity scales, and so on, for future developments.
4 PRELIMINARY EVALUATION AND FUTURE WORK
In the interviews with graduate students and teachers in our university, we obtained useful
comments and suggestions. For example, the links should present the direction of page transits
more clearly; the number displayed should be more meaningful to students; hyperlinks from the
graph to other useful plugins of the LMS and e-book can be helpful; and so on. With the refined
prototype, we plan to conduct formative experiments in the next step to clarify its effectiveness. In
the future, the functions of the LA dashboard to support other phases of self-regulated learning,
including knowledge monitoring, planning, and regulation, will be studied and developed.
ACKNOWLEDGMENT
This research is supported by a JST AIP Grant No. JPMJCR19U1, Japan.
REFERENCES
Chen, L., Lu, M., Goda, Y., & Yamada, M. (2019). Design of learning analytics dashboard supporting
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Belfiore, P. J., & Hornyak, R. S. (1998). Operant theory and application to self-monitoring in
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Hofer, B. K., Yu, S. L., & Pintrich, P. R. (1998). Teaching college students to be self-regulated learners.
In: D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-
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Regulated Digital Learning (SRDL). Journal of Computer Assisted Learning, 34(5), 580-589.
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Self-regulated learning: From teaching to self-reflective practice
  • P J Belfiore
  • R S Hornyak
Belfiore, P. J., & Hornyak, R. S. (1998). Operant theory and application to self-monitoring in adolescents. In: D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 184-202). New York: Guilford.