Log Data Visualization - SAVI Webinar 2013 - Caprotti Pauna

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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 …

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.

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  • Static information, and interactive information visualization.
  • Course 119, ordered byuserid (hashed), then by module (eg which type of activity was carried out), then by name of the activity. Size of the circle is proportional to the number of actions performed for this activity while the color, when different from gray, denotes the grade. Lighter shades indicate a lower score. When hovering by the mouse on a specific action (dot), the tooltip also indicates further details such as the final grade of the student in the course, and the grade for the activity. Here students are not filtered by final score but that would be possible also.
  • Here only the students that scored a final grade between 3 and 5 are visualized.
  • Here the clusters are by name of the resource, then by module (some resource names map to quizzes and to handouts), then by student. As before, the colors show the grade for the gradable activities. This grade is the best score of all attempts. This is interesting to the teacher probably whereas the quiz designer will want to know the grades of each attempt.
    The size also indicates how much it was tried to get that level score, which is a measure of difficulty and motivation.
  • Because we color the labels of actiivity, this view also gives the idea of which activities are carried out mostly.
  • There is a lot to say about dynamic visualizations, we are not yet ready to discuss it.

Transcript

  • 1. Log Data Visualization O. Caprotti and M. Pauna University of Helsinki SAVI Webinar O. Caprotti and M. Pauna November 14, 2013 1
  • 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 11
  • 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 14