Attention Profiling Algorithm for Video-based Lectures

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Presentation at HCII 2014, Crete, June 2014

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Attention Profiling Algorithm for Video-based Lectures

  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 www.tugraz.at Attention Profiling Algorithm for Video-based Lectures Josef Wachtler, Martin Ebner and Behnam Taraghi ZID - Social Learning - TU Graz HCII 2014
  2. 2. 2 Attention Profiling Algorithm for Video-based Lectures Content 1. Motivation 2. Implementation of the Algorithm – Operating-Context – Recording Joined Timespans – Calculating the Attention-Level 3. Evaluation 4. Conclusion Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  3. 3. 3 Attention Profiling Algorithm for Video-based Lectures Graz, University of Technology Europe, Austria, Graz http://www.tugraz.at Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  4. 4. 4 Motivation Students’ Attention students are confronted with a growing quantity of information they can handle and process only a limited number of these information at the same time selective attention is the most crucial resource for human learning so it is from high importance to control and analyze it Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  5. 5. 5 Motivation Interaction and Communication should be used in many different forms as well as in all possible directions avoid that learners become tired or annoyed increase the attention and the contribution feedback for teachers: Is it possible for the learners to follow the content? Is the speed appropriate? ... Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  6. 6. 6 Implementation of the Algorithm Overview the attention profiling algorithm is divided in two parts: a detailed recording of the joined timespans of each single user the calculation of an attention-level based on the reaction-times to the interactions Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  7. 7. 7 Operating-Context Web-Application on-demand video or live-broadcasting implements the attention profiling algorithm different methods of interaction: automatically asked questions and captchas asking questions to the lecturer asking text-based questions to the attendees multiple-choice questions at pre-defined positions Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  8. 8. 8 Operating-Context Interactions during a Video Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  9. 9. 9 Recording Joined Timespans Functionalities for each attendee it is possible to say at which time he/she watched which part of the video calculating statistical values the shortest or the longest joined timespan the average length of the joined timespans ... Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  10. 10. 10 Recording Joined Timespans Models the JoinedUser-model connects an user to an event the History-model represents a joined timespan with both, absolute and relative timestamps Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  11. 11. 11 Calculating the Attention-Level Functionalities calculation of an attention-level which is based on the reaction-times of the attendees to the interactions 1. logging the reaction-times 2. calculating the attention-level maxim: if the attendee reacts slower the attention-level decreases result ranges from 0% to 100% Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  12. 12. 12 Calculating the Attention-Level Logging the Reaction-Times the Interaction-model with its concrete sub-class models for each possible receiver of an interaction connects an interaction to an user the CallHistory-model logs every occurrence of an interaction in absolute and relative timestamps the difference between the real start time and the response time is equal to the reaction-time Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  13. 13. 13 Calculating the Attention-Level Logging the Reaction-Times Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  14. 14. 14 Calculating the Attention-Level Overview the calculation is split in three rounds: 1. calculation of an attention-level based on the reaction-times for every call of an interaction (I) 2. grouping them to attention-levels (AL) of each interaction-methods (IM) 3. generalizing to an attention-level of a joined timespan Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  15. 15. 15 Calculating the Attention-Level Round 1 the calculation has two parameters 1. SUCCESS UNTIL states the time until an attention-level of 100% could be reached 2. FAILED AFTER indicates after which reaction-time an attention-level of 0% will be assumed Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  16. 16. 16 Calculating the Attention-Level Round 1 f(tij) represents the attention-level of the j-th interaction of the i-th interaction-method tij is the corresponding reaction-time f(tij ) =    100 if tij ≤ SUCCESS UNTIL 0 if tij > FAILED AFTER g(tij ) else (1) Where g(tij) is g(tij ) = 100 − tij − SUCCESS UNTIL FAILED AFTER − SUCCESS UNTIL ∗ 100 (2) Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  17. 17. 17 Calculating the Attention-Level Round 2 ai calculates the attention-level of the i-th interaction-method by forming the mean mi is the number of its interactions ai = mi j=0 f(tij) mi (3) Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  18. 18. 18 Calculating the Attention-Level Round 3 takes the attention-level of each interaction-method (ai) and again forms the mean over them n is the number of interaction-methods attention = n i=0 ai n (4) Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  19. 19. 19 Evaluation Overview three goals 1. gaining suitable parameters to force the algorithm to deliver realistic values 2. comparing the results of the algorithm with the feedback of the attendees to implement adoptions 3. evaluating the effects of the adoptions Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  20. 20. 20 Evaluation Gaining Suitable Parameters live-broadcasting of the lecture Societal Aspects of Information Technology analyzing recorded reaction-times of the interactions the average reaction-time is calculated to place the parameters around this point Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  21. 21. 21 Evaluation Compare Results with Feedback live-broadcasting of the lecture Introduction to Structured Programming complete number of attendees vs. active ones active: watched ≥ 75% and attention-level ≥ 50% Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  22. 22. 22 Evaluation Results Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  23. 23. 23 Evaluation Feedback attendees felt uncomfortable with their attention-level - they assumed a much higher one impossible to answer faster because the live-stream does not stop if an interaction occurs the number of interactions should not be very high Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  24. 24. 24 Evaluation Adoptions the video pauses if an interaction occurs lecturers asked to pause his/her presentation at the occurrence of an interaction at a live broadcasting the number of interactions is lowered to a maximum of three interactions in a period of ten minutes Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  25. 25. 25 Evaluation Testing Adoptions 8 videos of the lecture Learning in the Net: From possible and feasible things complete number of attendees vs. active ones to test the adoptions active: watched ≥ 75% and attention-level ≥ 50% Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  26. 26. 26 Evaluation Results Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  27. 27. 27 Evaluation Discussion the parameters for the calculation of the attention-level are highly sensitive the accuracy depends on many different factors (e.g. difficulty of the questions, the content of the video, ...) the timespan between the interactions should not be to small the two parts of the attention profiling algorithm are only powerful in combination Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  28. 28. 28 Conclusion Conclusion attention is the most crucial resource in human learning attention profiling algorithm with to parts recording of the joined timespans calculation of an attention-level delivers realistic values after some adoptions Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014
  29. 29. 29 Thank you ... ... for your attention! Questions? Josef Wachtler, josef.wachtler@tugraz.at Martin Ebner, martin.ebner@tugraz.at ZID – “Social Learning” Graz, University of Technology M¨unzgrabenstraße 35A, A-8010 Graz http://elearningblog.tugraz.at Josef Wachtler, Martin Ebner and Behnam Taraghi, ZID - Social Learning - TU Graz HCII 2014

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