We are showing our work in progress on visualizing learning analytics data collected from an online course on WEPS, weps.com. We are using off-the-shelf software to create both static and interactive visualizations targeting a diverse user community, from students, to instructors, to instructional designers.

1. Log Data
Visualization
O. Caprotti and M. Pauna
University of Helsinki
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
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2. From the raw data
raw log data is
hard to view
and analyze,
especially
when it
becomes
large
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
2

3. Fetch relevant information
Interesting data is
scattered, for
instance among
several DB tables.
More generally,
data will also
come from
heterogeneous
sources.
Which research
questions?
Which data?
How to graph it?
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
3

4. Visualize many dimensions
Text, color,
size, density,
shading,
shapes,
placement,
etc.
are all used to
convey
information
about a
certain aspect
of the data,
depending on
the research
question.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
4

5. Course 119 (Calculus), by student
Visualized by:
•userid (hashed),
•type of activity carried out
•name of the activity.
Extra dimensions:
•size of the circle is proportional
to the number of
actions/activity
•colors, others than gray,
denote the grade in increasing
shades.
When hovering by the mouse
on a specific action (dot), the
tooltip indicates further details
such as the final grade of the
student in the course, and the
actual grade for the activity.
Here students are not filtered
by final score but that would
be possible also.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
5

6. Course 119, top scorers
Filtering the data
wrt specific
attributes allows
e.g. to only display
students with top
final grades.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
6

7. Course 119, by resource
Clustered by
•name of the resource
•module
•students
to show how popular a
learning resource has
been and how
easy/difficult an
assignment was.
Educational designers
will be interested in
visualizing different data
from instructors or
students.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
7

8. Course 119, log paths
A log path is a subgraph in the
graph of activities of the
network of students recorded
by the logs.
If the log contains consecutive
time-stamped records for the
same userid, accessing online
resources R1 then R2, the edge
<R1,R1> appears in the activity
network.
It is labeled by the activity
name.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
8

9. Course 119, log paths around a quiz
Zooming in on a
resource to see all
log-paths that have
traversed the
resource, by any
student is an
example of
interactive data
visualization.
Resource nodes are
also colored
depending on their
degree as node in
the graph.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
9

10. Course 119, first midterm
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
10

11. Course 119, second midterm
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
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12. Forthcoming
• Visualize data to answer specific research questions
from educational specialists
• Add filtering criteria to select learning paths, e.g. of
successful students, to construct a
recommendation system
• Include additional profiling data, e.g. learning style,
motivation, and cognitive attributes, in the
visualizations
• Improve dynamic edge weight handling
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
12

13. Course 119, edges in logpaths
Edges in log
paths can be
partitioned
according to
attributes,
such as the
label.
In our
example, the
activity type.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
13

14. Course 119, statistics of log paths
Statistical
measures on
the graph
help decide
how to best
visualize
data.
SAVI Webinar
O. Caprotti and M. Pauna
November 14, 2013
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