=Paper=
{{Paper
|id=Vol-1518/paper6
|storemode=property
|title=Exploring Inquiry-Based Learning Analytics through Interactive Surfaces
|pdfUrl=https://ceur-ws.org/Vol-1518/paper6.pdf
|volume=Vol-1518
|dblpUrl=https://dblp.org/rec/conf/lak/CharleerKD15
}}
==Exploring Inquiry-Based Learning Analytics through Interactive Surfaces==
https://ceur-ws.org/Vol-1518/paper6.pdf
Exploring Inquiry-Based Learning Analytics through
Interactive Surfaces
Sven Charleer, Joris Klerkx, and Erik Duval
Dept. of Computer Science
KU Leuven
Celestijnenlaan 200A
3001 Leuven, Belgium
sven.charleer@kuleuven.be, joris.klerkx@kuleuven.be, erik.duval@kuleuven.be
ABSTRACT
Learning Analytics is about collecting traces that learners
leave behind and using those traces to improve learning.
Dashboard applications can visualize these traces to present
learners and teachers with useful information. The work in
this paper is based on traces from an inquiry-based learning
(IBL) environment, where learners create hypotheses, dis-
cuss findings and collect data in the field using mobile de-
vices. We present a work-in-progress that enables teachers
and learners to gather around an interactive tabletop to ex- Figure 1: Students gathering around an interac-
plore the abundance of learning traces an IBL environment tive tabletop, exploring learner traces of a Human-
generates, and help collaboratively make sense of them, so Computer Interaction course.
as to facilitate insights.
Categories and Subject Descriptors
H.5.2 [Information interfaces and presentation]: User learners whose activities can be tracked in detail. Reflect-
Interfaces; H.5.n [Information interfaces and presenta- ing on those traces can help learners to understand what is
tion]: Miscellaneous the optimal setting and context in which they learn best.
Teachers can, among other things, use the same traces to
find out where learners struggle with what content or activ-
General Terms ity. Dashboards help present this abundance of data in a
Design, Human Factors, Experimentation way that supports both teachers and learners [14].
Keywords Teachers show interest in using dashboards collaboratively
interactive surfaces, learning analytics, learning dashboards, with learners to discuss their activities, progress and re-
collaboration, reflection, awareness, information visualiza- sults [3]. Interactive tabletops can facilitate and capture
tion, sense-making, inquiry-based learning collaboration activities in the classroom [8]. In previous
work [2] we explored this platform to visualize learning an-
1. INTRODUCTION alytics data (see Figure 1), using the affordances (e.g. large
Similar to the Quantified Self 1 movement, which focuses on display size, multi-user interaction) of interactive tabletops
collecting user traces and using the data for self-improvement, to create a collaborative sense-making environment [6].
Learning Analytics can help to understand and optimize
(human) learning and the environments in which it occurs [12]. This paper describes our work-in-progress on an interactive
However, capturing learner traces can generate an abun- tabletop visualization for learner traces that are generated
dance of data, especially in the context of Massive Open by students in an inquiry-based learning (IBL) environment.
Online Courses (MOOCs) that involve tens to thousands of Section 2 briefly present the learning environment and the
data it generates. Section 3 discusses development details,
1 section 4 explains the design of the tabletop visualization.
http://quantifiedself.com
We discuss our findings and future work in section 5
2. IBL LEARNING TRACES
Contrary to a traditional passive role in a classroom, in
Inquiry-Based Learning (IBL), learners assume an active
role as explorer and scientist with a focus on learning “how
to learn”. Teachers try to stimulate learners to pose ques-
tions and create hypotheses regarding a specific topic, per-
form independent investigations, gather data to confirm and
Figure 2: weSPOT Inquiry Environment, presenting
6 phases and 2 active widgets in phase 5 (Interpre-
tation).
Figure 4: A. The overview of all activities. B. The
list of students participating in the inquiry (with
student filter options). C. The content behind se-
lected activities. D. Phase filter options.
Following a user-centered rapid prototyping approach, we
started from paper prototypes to gather initial feedback on
early ideas, gradually developed more functional digital pro-
Figure 3: A web-based dashboard for teachers and totypes which have been deployed and evaluated with learn-
students providing access to learning analytics data ers regarding usability.
per inquiry.
Web technologies (HTML, CSS3 and JavaScript) facilitate
development of quick prototypes and allows us to deploy
on most school infrastructures. Interaction is supported
discuss their findings and generate conclusions. 6 phases through both native browser mouse/touch events and the
of learning activities are often discerned in an IBL process npTUIClient plug-in 3 , allowing the application to run on in-
model: problem identification, operationalization, data col- teractive tabletops, interactive white-boards, tablets, phones
lection, data analysis, interpretation and communication [9]. and desktop computers. Our interactive tabletop setup cur-
As each learner can follow his own route through the IBL rently facilitates up to 5 users.
process, it is obvious that the sequence and length of these
phases differ among students. Individual and collaborative A centralized filter system using Crossfilter 4 and a modular
reflection is furthermore vital in every phase. Indeed,“even and event-based architecture facilitates easy creation of new
at the very beginning when students need to develop a ques- widgets. D3.js 5 and Processing.js 6 help visualize the data.
tion or a hypothesis, they need to reflect upon the question, A Node.js 7 back-end generates the web pages while fetching
and evaluate it before they decide to proceed. They also need the learning traces from the weSPOT environment.
to reflect while deciding what kind of data they need to col-
lect, how to proceed to data analysis, and how to communi-
cate their results” [10]. 4. DESIGN
Flexible visual analysis tools must provide appropriate con-
In the weSPOT Inquiry Environment 2 , a teacher can set trols for specifying the data and views of interest [5]. Filter-
up an inquiry regarding a specific research topic. For each ing out unrelated information to focus on relevant items is
phase, learners can use a set of widgets (see Figure 2) to the key control in our learning dashboards due to the abun-
e.g. create hypotheses, ask questions, rate and comment dance of traces learners leave behind. Previous work [3] has
on activities, generate mind-maps, etc. By taking pictures, shown that there is also a need for context and content to
recording videos, entering text and data from measurements complement the visualized data. We therefore follow the
through a mobile application, students collect data in the visual information-seeking mantra of “Overview first, zoom
field to support their hypotheses. All activities in the learn- and filter, then details-on-demand” [11]: our tabletop visu-
ing environment are logged and stored in a data store and alization presents users with a coordinated set of widgets
exposed as learning traces through REST services. Teach- which contain: (i) a complete overview of all activities (Fig-
ers and students can access the learning analytics data of a ure 4.A), (ii) data filters (Figure 4.B/D) and (iii) the content
specific inquiry through a web-based dashboard integrated view (Figure 4.C).
in weSPOT Inquiry Environment 4, and the tabletop appli-
3
cation. https://github.com/fajran/npTuioClient
4
http://square.github.io/crossfilter/
5
http://d3js.org
3. ITERATIVE DEVELOPMENT 6
http://processingjs.org
2 7
http://inquiry.wespot.net/ http://nodejs.org
Figure 5: Time-lines per activity thread. The high-
lighted thread consist of a hypothesis creation fol-
lowed by 2 edits, a user rating and 2 comments.
Figure 7: A prototype with 5 filter “drop zones”.
Dropping a filter value into the blue (top-left) drop
zone highlights data points matching the filter result
by coloring the top-left part of the glyph.
4.2 Filtering the Data
Using the filter widgets, users can focus on activities by
drilling down on one or more phases (see Figure 4.D), or one
or more learners (see Figure 4.B). When multiple learners
are selected (e.g. a group that works together), the path of
Figure 6: A. The blue path indicates the steps taken each learner can be individually highlighted (see Figure 6.B),
by a student. In this case, the student learned some- in order to provide an overview of work distribution. This
thing which he then rated. This then lead to the cre- can help teachers to find struggling learners in a group. It
ation of a new hypothesis. B. Visualization limited can also help learners to become aware of uneven work dis-
to a group of 2 students. Individual paths are high- tribution and help to redivide the work. The path can also
lighted. The student indicated by the yellow line has shed light on the methodology a learner uses to reach a cer-
been more active with both commenting and rating tain result (e.g. Figure 4.A).
activities. The student has also been more active in
phase 6 (purple). The interface of Figure 4 is limited to one person driving
the navigation and only supports global filters. To fully
use the affordances of the tabletop and create a collabora-
tive sense-making environment, the application must sup-
4.1 Visualizing IBL Traces port both individual as well as group work [4]. Figure 7
The visualization displays a time-line per activity thread (see shows an early prototype that presents 5 participants with
Figure 5). For instance, the creation of a hypothesis by a individual filtering tools. Global filters result in more tightly
learner is followed by every comment on, rating on, and edit coupled collaboration [13], but can disturb individual work.
of the hypothesis. Squares represent create and edit events, One participant’s filter activity could remove data from the
while circles represent comment events. Stars represent a visualization another participant is working with. To allow
rating activity, triangles are data collection events. Activ- participants to simultaneously filter the data presented on
ities within a single thread are connected by a horizontal the tabletop, we use the multivariate attributes of a glyph-
line. This enables teachers and learners to see the evolution based visualization [1]. The filter result of each participant is
of an activity thread, the comments that may have impacted highlighted in the color corresponding to the user interface.
edits of e.g. the original hypothesis, and the rating trend.
Activities in other activity threads can enrich the context
5. CONCLUSION AND FUTURE WORK
Our interactive visualization will be deployed in multiple
of a specific thread. A discussion in one thread might in-
secondary school pilots 8 across Europe, both on interac-
fluence the creation of a new hypothesis, or an edit of an
tive tabletop devices and interactive white-boards. Ques-
existing one. Therefore, every activity is positioned relative
tionnaires regarding usefulness for both teachers and stu-
in time to all other activities displayed, allowing the users
dents will help evaluate our design choices, while interaction
to backtrack through time across multiple threads at once
logging and video recordings of collaboration sessions can
(see Figure 6.A).
provide insights in whether the application is useful as a
sense-making environment.
IBL phases (see Section 2) in which an activity occurs are in-
dicated by different background colors, matching the colors
Our application lets users retrace individual steps taken by
used of the web dashboard (see Figure 4). The visualiza-
(groups of) learner(s), i.e. they can collaboratively (i) re-
tion can be panned and zoomed using standard multi-touch
8
interactions. http://portal.ou.nl/web/wespot/pilots
flect on the rationale of a learner’s decisions and actions, lenses for geospatial exploration. In Proceedings of the
(ii) (re-)examine past explanations and conclusions, and (iii) Ninth ACM International Conference on Interactive
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Collaborative Learning, CSCL 2013, pages 119–128,
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The research leading to these results has received funding personal open tools for inquiry based learning:
from the European Community’s Seventh Framework Pro- Pedagogic and diagnostic frameworks. The
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