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{{Paper
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|storemode=property
|title=LARAe: Learning analytics reflection & awareness environment
|pdfUrl=https://ceur-ws.org/Vol-1238/paper9.pdf
|volume=Vol-1238
|dblpUrl=https://dblp.org/rec/conf/ectel/CharleerSKD14
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==LARAe: Learning analytics reflection & awareness environment==
https://ceur-ws.org/Vol-1238/paper9.pdf
LARAe: Learning Analytics Reflection &
Awareness environment
Sven Charleer, Jose Luis Santos, Joris Klerkx, and Erik Duval
Dept. of Computer Science, KU Leuven
Leuven, Belgium
{Sven.Charleer,JoseLuis.Santos,Joris.Klerkx,Erik.Duval}@cs.kuleuven.be
Abstract. Exploring and managing the abundance of data that Learn-
ing Analytics generate is a challenge for both teachers and students.
This paper introduces a Learning Dashboard that provides an overview,
context and content of learner traces to help students with awareness of
feedback and progress, and assist teachers with monitoring student effort
and outcomes to intervene where needed.
Keywords: learning analytics, learning dashboards, awareness, infor-
mation visualization, effort, intervention, inquiry-based learning
1 Introduction
The purpose of Learning Analytics is understanding and optimizing learning and
the environments in which it occurs [1]. Through dashboards, Learning Analytics
can help support both teacher and students [2].
Learning Dashboards can rely on many different ways of visualizing raw
analytics data e.g. bar, star and bubble charts, interactive histograms, parallel
coordinates etc [2]. These visualization techniques can provide broad insights on
student activities [3, 4]. By adding teacher traces, our visualization also attempts
to provide awareness of feedback to improve its supportive role for both student
and teacher.
This abundance of data can be abstracted to the essentials [5, 6], but context
and content can help provide deeper insights [7]. Following the visual information-
seeking mantra of “Overview first, zoom and filter, then details-on-demand” [8],
our dashboard presents users with an abstract overview while still retaining a
sense of context and providing access to the details.
2 LARAe: Design & Implementation
LARAe visualizes traces gathered from 38 engineering students, teachers and ex-
ternal participants in an open User Interfaces course. Students worked in groups
of 3 and reported weekly through blog posts, comments and Twitter. The course
generated 419 blog posts, 1580 comments and 538 tweets.
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LARAe: Learning Analytics Reflection & Awareness environment - ARTEL14
Fig. 1. LARAe: A. Overview, B. Activities, C. Thread view
Every activity is represented by a circle (Figure 1.B) which provides direct
access to the related content (e.g. blog post, comment, tweet, retweet). Activities
are sorted chronologically, from top left to bottom right. Gradient color values
(see Figure 1.A) help recognize the age of an activity. A table (Figure 1.B)
structures the activities by student group and type. Every column represents
an activity type, every row a student group. The user can sort the data by any
activity type. Both activity age and amount help facilitate awareness of (in)active
groups. As teaching staff feedback was deemed important by both student and
teacher, a second table visualizes activities of teacher activity in a similar way.
Context plays an important role in understanding the activities e.g. a com-
ment without its surrounding discussion is difficult to assess. We propose a “fo-
cus+context” [9] solution which consists of 2 parts: highlighting related events
(Figure 1.B) and displaying the content within a thread view (Figure 1.C).
Highlighting related activities helps the user to instantly become aware of the
distribution of an activity thread across the class e.g. selecting a blog post will
highlight what groups provided most contributions. Simultaneously, the thread
view shows the content of each related activity, helping assess the quality of the
quantitative data. Visualizing discussion thread size can help students discover
interesting threads. Teachers might understand low thread size as an indication
for need of intervention. The attribute thread size is indicated by a number in
each circle (Figure 1.B).
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LARAe: Learning Analytics Reflection & Awareness environment - ARTEL14
LARAe is a web application developed using HTML5, JavaScript and D3.js1
running on a Node.js2 web service and MongoDB3 database. It supports both the
proprietary API and Tin Can API4 . It can easily be extended to support other
APIs. The dashboard is designed to run on large displays, desktop computers
and tablets. It is available at http://ariadne.cs.kuleuven.be/LARAe/.
The dashboard has also been deployed in an inquiry-based learning setting,
visualizing the learner traces gathered from the weSPOT Inquiry system5 [10].
Acknowledgment The research leading to these results has received funding
from the European Community’s Seventh Framework Programme (FP7/2007-
2013) under grant agreement No 318499 - weSPOT project.
References
1. Siemens, G., Long, P.: Penetrating the fog: Analytics in learning and education.
Volume 46., Boulder, CO, USA, EDUCAUSE (2011) 30–32
2. Verbert, K., Duval, E., Klerkx, J., Govaerts, S., Santos, J.L.: Learning analytics
dashboard applications. American Behavioral Scientist 57(10) (2013) 1500–1509
3. McAuley, J., O’Connor, A., Lewis, D.: Exploring reflection in online communities.
In: Proceedings of the 2Nd International Conference on Learning Analytics and
Knowledge. LAK ’12, New York, NY, USA, ACM (2012) 102–110
4. Donath, J.: A semantic approach to visualizing online conversations. Communi-
cations of the ACM 45(4) (April 2002) 45–49
5. Xiong, R., Donath, J.: Peoplegarden: creating data portraits for users. In: Proceed-
ings of the 12th annual ACM symposium on User interface software and technology,
ACM (1999) 37–44
6. Arnold, K.E., Pistilli, M.D.: Course signals at purdue: Using learning analytics to
increase student success. In: Proceedings of the 2nd International Conference on
Learning Analytics and Knowledge, ACM (2012) 267–270
7. Charleer, S., Klerkx, J., Odriozola, S., Luis, J., Duval, E.: Improving awareness and
reflection through collaborative, interactive visualizations of badges. In: Proceed-
ings of the 3rd Workshop on Awareness and Reflection in Technology-Enhanced
Learning. Volume 1103 of CEUR Workshop Proceedings., CEUR-WS.org (2013)
69–81
8. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information
visualizations. In: IEEE Symposium on Visual Languages, IEEE (1996) 336–343
9. Furnas, G.W.: A fisheye follow-up: Further reflections on focus + context. In:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
CHI ’06, New York, NY, USA, ACM (2006) 999–1008
10. Mikroyannidis, A., Okada, A., Scott, P., Rusman, E., Specht, M., Stefanov,
K., Boytchev, P., Protopsaltis, A., Held, P., Hetzner, S., Kikis-Papadakis, K.,
Chaimala, F.: wespot: A personal and social approach to inquiry-based learning.
Journal of Universal Computer Science 19(14) (2013) 2093–2111
1
http://d3js.org
2
http://nodejs.org/
3
http://www.mongodb.org
4
http://tincanapi.com/
5
http://portal.ou.nl/documents/7822028/f475d712-5467-40ea-968c-5aa00d951400
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