On Using Learning Analytics to Track the Activity of Interactive MOOC Videos
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J. Wachtler, M. Khalil, B. Taraghi, and M. Ebner. “On Using Learning Analytics to Track the Activity of Interactive MOOC videos”. In Proceedings of the LAK 2016 Workshop on Smart Environments and Analytics in Video-Based Learning, Edinburgh, Scotland, 2016, pp.8–17.
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On Using Learning Analytics to Track the Activity of Interactive MOOC Videos
- 1. 1 S C I E N C E P A S S I O N T E C H N O L O G Y u www.tugraz.at 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner On Using Learning Analytics to Track the Activity of Interactive MOOC Videos
- 2. 22 Graz University of Technology • Europe, Austria, Graz • http://www.tugraz.at • http://elearningblog.tugraz.at On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 3. 33 @TuMohdKhalil @mebner@behi_at@wachjose88 The Authors Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner 2016-04-26 On Using Learning Analytics to Track the Activity of Interactive MOOC Videos
- 4. 44 Content 1. Motivation 2. Related Work 3. Interactions in Learning Videos 4. Evaluation 5. Summary On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 5. 55 Motivation • interaction and communication are very important influencing factors of students' attention • attention is considered as the most crucial resource for human learning • it is from high importance to understand and to analyze it • learning analytics plays a major factor in enhancing learning environments components • reflecting and benchmarking the whole learning process On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 6. 66 Related work • other interactive video tools • Youtube • Zaption • EdTed • … • real-world pendant: audience response system • has the power to enhance students‘ attention and participation • the addition of interactivity to learning videos tries to generate similar benefits On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 7. 77 Interactions in Learning Videos • web-based information system called LIVE • registered and authenticated users • different methods of interaction • automatically asked questions and captchas • questions to the lecturer by the learners • asking text-based questions to the attendees live or at pre-defined positions • multiple-choice questions at pre-defined positions • different possibilities of analysis On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 8. 88 Video interrupted by a multiple-choice question 1. paused video is overlaid by an interaction to resume playing, it is required to respond to the interaction 2. control elements on the right side of the video invoke interactions manually ask a question to the teacher Interactions in Learning Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 9. 99 Timeline Analysis • number of users (green) and views (red) • exact values on mousehover Interactions in Learning Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 10. 1010 Watched Parts per User • marks each watched part with a bar • time of joining and leaving in relative and absolute values on mousehover Interactions in Learning Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 11. 1111 Evaluation • MOOC – Maker Day for School Children • Using Log File of LIVE • Exploratory Analysis and Visualizations • Examining effeciency and weak-points. On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 12. 1212 Evaluation – Delay of Response On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner Delay of Response Certified students took less time than the others in answering the questions.
- 13. 1313 Evaluation – Delay of Response On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner More time answering questions
- 14. 1414 Evaluation – Delay of Response On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner Non-certified holds more outliers
- 15. 1515 Evaluation – Timing of the Questions On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner Timing of the Questions We wanted to infer the relevance timing of the first question to describe the drop rate during videos. Students drop after 15% of the videos. Most students who watch 20%, keep watching the whole video
- 16. 1616 Evaluation – Timing of the Questions On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner Limited drop ratio 1st Question, should be in the high-drop rate area. (0-10%)
- 17. 1717 Evaluation – Timing of the Questions On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner Colored points depict distinct watchers count of each week video. Grey points indicates number of views. Week1-3 shows more views per user Last two weeks, views=watchers
- 18. 1818 Evaluation – Total Activity On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner Total Activity of the Videos Counting no. Of plays,stops and fully watched. Activity was high in Week1,Week3 & Week5.
- 19. 1919 Evaluation – Total Activity On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner Highly active because of the topic
- 20. 2020 Summary • an interactive video platform presenting videos of a MOOC • an evaluation of this system to examine its performance and describe the behavior of students • the main concept behind latching on the students' attention becomes attainable through evaluating the questions' content and the interactions timing On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
- 21. 2121 Thank you for your attention! • Josef Wachtler, josef.wachtler@tugraz.at • Mohammad Khalil, mohammad.khalil@tugraz.at • Behnam Taraghi, b.taraghi@tugraz.at • Martin Ebner, martin.ebner@tugraz.at Graz University of Technology Educational Technology Münzgrabenstraße 35a, 8010 Graz, Austria – Europe http://elearningblog.tugraz.at On Using Learning Analytics to Track the Activity of Interactive MOOC Videos 2016-04-26 Josef Wachtler, Mohammad Khalil, Behnam Taraghi, Martin Ebner
Hinweis der Redaktion
- This is a BoxPlot Week 4 and Week 7 shows that student took more time answering questions. Also more outliers (the dots) for the non-certified students.
- This is a BoxPlot Week 4 and Week 7 shows that student took more time answering questions. Also more outliers (the dots) for the non-certified students.
- This is a violin plot. Different than the boxplot, that this plot shows status of all the multiple choice questions directly not separated. Higher values of outliers can be seen for the non-certified.
- Vertical dashed lines show the 1st question timing effect. Week4 and week6 were late for testing purposes of the effect. This plot talks about drop ratio. Students drop in the first 15% of the video. Therefore, 1st question from LIVE should be in that period. The high drop ratio In the last 90% is because of the closing trailer of each video.
- This dot plots to see how many times a user watch a videos. First 3 weeks some users replay the video more than once. Reason: initial interest of the first online course weeks. However, last weeks students watch videos once. Also u can see in the video that number of views = number of watchers in week6 and week7. It is also obvious the number of students getting less every week.
- This plot shows activity of play, stop and fully watched. (The data has been taken from the logs files directly, not from LIVE)> this is only for your reference if some people ask there are some differences between plots and this plot. Week5 was active despite it was in the 5th week. Reason is because of the topic: 3d printing. Certified students shows much of the activity in the last 4 weeks.