Addictive links, Keynote talk at WWW 2014 workshop

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Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. Over the last 8 years we have explored a lesser known effect of adaptive annotation – its ability to significantly increase student engagement in working with non-mandatory educational content. In the presence of adaptive link annotation, students tend to access significantly more learning content; they stay with it longer, return to it more often and explore a wider variety of learning resources. This talk will present an overview of our exploration of the addictive links effect in many course-long studies, which we ran in several domains (C, SQL and Java programming), for several types of learning content (quizzes, problems, interactive examples). The first part of the talk will review our exploration of a more traditional knowledge-based personalization approach and the second part will focus on more recent studies of social navigation and open social student modeling

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Addictive links, Keynote talk at WWW 2014 workshop

  1. 1. Addictive Links: Engaging Students through Adaptive Navigation Support and Open Social Student Modeling Peter Brusilovsky with: Sergey Sosnovsky, Michael Yudelson, Sharon Hsiao School of Information Sciences, University of Pittsburgh
  2. 2. MOOC Massive Open Online Course
  3. 3. Completion Rate
  4. 4. MOOC Completion Rate Classic loop user modeling - adaptation in adaptive systems http://www.katyjordan.com/MOOCproject.html
  5. 5. What Else These Students Need? •  Top colleges –  Stanford, CalTech, Princeton, GATech, Penn, Duke.. •  Great faculty – top guns in their fields •  Great content •  Top online platform – Coursera •  FREE!
  6. 6. The Problem of Engagement •  Great free content and top teachers is not enough to engage students •  Peter Norvig: Motivation and Engagement are key problems for MOOCs •  The problem is not new •  A lot of great advanced content –  Works perfectly in lab studies, great gains –  Released to students to enhance learning –  No impact – students do not use it
  7. 7. The Case of QuizPACK •  QuizPACK: Quizzes for Parameterized Assessment of C Knowledge •  Each question is a pattern of a simple C program. When it is delivered to a student the special parameter is dynamically instantiated by a random value within the pre- assigned borders. •  Used mostly as a self- assessment tool in two C- programming courses
  8. 8. QuizPACK: Value and Problems •  Good news: –  activity with QuizPACK significantly correlated with student performance in classroom quizzes –  Knowledge gain rose from 1.94 to 5.37 •  But: –  Low success rate - below 40% –  The system is under-used (used less than it deserves) •  Less than 10 sessions at average •  Average Course Coverage below 40%
  9. 9. Adding Motivation •  Students need some better motivation to work with non- mandatory educational content… •  Added classroom quizzes: –  Five randomly initialized questions out of 20-30 questions assigned each week •  Good results - activity, percentage of active questions, course coverage - all increased 2-3 times! But still not as much as we want. Could we do better? •  Maybe students bump into wrong questions? Too easy? Too complicated? Discouraging… •  Let’s try something that worked in the past adaptive hypermedia that can guide students to the right content
  10. 10. User Model Collects information about individual user Provides adaptation effect Adaptive System User Modeling side Adaptation side User-Adaptive Systems Classic loop user modeling - adaptation in adaptive systems
  11. 11. Adaptive Link Annotation: InterBook   1. Concept role 2. Current concept state 3. Current section state 4. Linked sections state 4 3 2 1 √" Metadata-based mechanism
  12. 12. The Value of ANS •  Lower navigation overhead –  Access the content at the right time –  Find relevant information faster •  Better learning outcomes –  Achieve the same level of knowledge faster –  Better results with fixed time •  Encourages non-sequential navigation
  13. 13. Questions of the current quiz, served by QuizPACK List of annotated links to all quizzes available for a student in the current course Refresh and help icons QuizGuide = QuizPACK+ANS
  14. 14. Topic-Based Adaptation Concept A Concept B Concept C n  Each topic is associated with a number of educational activities to learn about this topic n  Each activity classified under 1 topic
  15. 15. QuizGuide: Adaptive Annotations •  Target-arrow abstraction: –  Number of arrows – level of knowledge for the specific topic (from 0 to 3). Individual, event-based adaptation. –  Color Intensity – learning goal (current, prerequisite for current, not-relevant, not-ready). Group, time- based adaptation. n  Topic–quiz organization:
  16. 16. QuizGuide: Success Rate n It works! n One-way ANOVA shows that mean success value for QuizGuide is significantly larger then the one for QuizPACK: F(1, 43) = 5.07 (p-value = 0.03).
  17. 17. QuizGuide: Motivation •  Adaptive navigation support increased student's activity and persistence of using the system Average activity 0 50 100 150 200 250 300 2002 2003 2004 Average num. of sessions 0 5 10 15 20 2002 2003 2004 Average course coverage 0% 10% 20% 30% 40% 50% 60% 2002 2003 2004 Active students 0% 20% 40% 60% 80% 100% 2002 2003 2004 n  Within the same class QuizGuide session were much longer than QuizPACK sessions: 24 vs. 14 question attempts at average. n  Average Knowledge Gain for the class rose from 5.1 to 6.5
  18. 18. A new value of ANS? •  The scale of the effect is too large… May be just a good luck? •  New effect after 15 years of research? •  Maybe the effect could only be discovered in full-scale classroom studies – while past studies were lab- based?
  19. 19. Round 2: Let’s Try it Again… •  Another study with the same system –  QuizGuide+QuizPACK vs. QuizPACK •  A study with another system using similar kinds of adaptive navigation support –  NavEx+WebEx vs. WebEx •  NavEx - a value-added ANS front-end for WebEx - interactive example exploration system
  20. 20. WebEx - Code Examples
  21. 21. Concept-based student modeling Example 2 Example M Example 1 Problem 1 Problem 2 Problem K Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Examples Problems Concepts
  22. 22. NavEx = WebEx + ANS
  23. 23. Does it work? •  The increase of the amount of work for the course Clicks - Overall 0 50 100 150 200 250 300 Non-adaptive Adaptive Examples Quizzes Lectures - Overall 0 2 4 6 8 10 12 Non-adaptive Adaptive Examples Quizzes Learning Objects - Overall 0 5 10 15 20 25 30 Non-adaptive Adaptive Examples Quizzes
  24. 24. Is It Really Addictive? •  Are they coming more often? Mostly, but there is no stable effect •  But when they come, they stay… like with an addictive game Clicks - Per Session 0 5 10 15 20 Non-adaptive Adaptive Examples Quizzes Learning Objects - Per Session 0 1 2 3 4 Non-adaptive Adaptive Examples Quizzes
  25. 25. Why It Is Working? •  Progress-based annotation –  Displays the progress achieved so far –  Does it work as a reward mechanism? –  Open Student Modeling •  State-based annotation –  Not useful, ready, not ready –  Access activities in the right time –  Appropriate difficulty, keep motivation
  26. 26. A Deeper Look
  27. 27. The Diversity of Work •  C-Ratio: Measures the breadth of exploration •  Goal distance: Measures the depth Self-motivated Work - C-Ratio (%) 0 0.2 0.4 0.6 Non-adaptive Adaptive Quizzes Examples Self-motivated Work - Goal Distance (LO's) 0 5 10 15 20 Non-adaptive Adaptive Quizzes Examples
  28. 28. Round 3: Trying another domain… •  Is it something relevant to C programming or to simple kind of content? •  New changes: –  SQL Programming instead of C –  Programming problems (code writing) instead of questions (code evaluation) –  Comparison of concept-based and topic-based mechanisms in the same domain and with the same kind of content
  29. 29. •  SQL-KnoT delivers online SQL problems, checks student’s answers and provides a corrective feedback •  Every problem is dynamically generated using a template and a set of databases •  All problems have been assigned to 1 of the course topics and indexed with concepts from the SQL ontology SQL Knowledge Tester
  30. 30. •  To investigate possible influence of concept-based adaptation in the present of topic-based adaptation we developed two versions of QuizGuide: Topic-based Topic-based+Concept-Based Concept-based vs Topic-based ANS
  31. 31. •  Two Database Courses (Fall 2007): §  Undergraduate (36 students) §  Graduate (38 students) •  Each course divided into two groups: §  Topic-based navigation §  Topic-based + Concept-Based Navigation •  All students had access to the same set of SQL- KnoT problems available in adaptive (QuizGuide) and in non-adaptive mode (Portal) Study Design
  32. 32. •  Total number of attempts made by all students: in adaptive mode (4081), in non-adaptive mode (1218) •  Students in general were much more willing to access the adaptive version of the system, explored more content with it and to stayed with it longer: Questions 0 25 50 75 100 Quizzes 0 5 10 15 20 25 Topics 0 1 2 3 4 5 6 Sessions 0 1 2 3 4 5 Session Length 0 5 10 15 20 25 Adaptive Non-adaptive It works again! Like magic…
  33. 33. Round 4: The Issue of Complexity •  Let’s now try it for Java… •  What is the research goal? •  Java is a more sophisticated domain than C –  OOP versus Procedural –  Higher complexity •  Will it work for complex questions? •  Will it work similarly? 0% 20% 40% 60% 80% 100% C Java language complexity Easy Moderate Hard
  34. 34. Meet QuizJET!
  35. 35. Naviga&on  Area Presenta&on  Area JavaGuide
  36. 36. !! !! JavaGuide (Fall 2008) QuizJET (Spring 2008) !! parameters (n=22) (n=31) Overall User Statistics Attempts 125.50 41.71 Success Rate 58.31% 42.63% Distinct Topics 11.77 4.94 Distinct Questions 46.18 17.23 Average User Session Statistics Attempts 30.34 21.50 Distinct Topics 2.85 2.55 Distinct Questions 11.16 8.88 Magic… Here We Go Again!
  37. 37. Round 5: Social Navigation •  Concept-based and topic-based navigation support work well to increase success and motivation •  Knowledge-based approaches require some knowledge engineering – concept/topic models, prerequisites, time schedule •  In our past work we learned that social navigation – guidance extracted from the work of a community of learners – might replace knowledge-based guidance •  Social wisdom vs. knowledge engineering
  38. 38. Open Social Student Modeling •  Key ideas –  Assume simple topic-based design –  No prerequsites or concept modeling –  Show topic- and content- level knowledge progress of a student in contrast to the same progress of the class •  Main challenge –  How to design the interface to show student and class progress over topics? –  We went through several attempts
  39. 39. Parallel Introspective Views 40
  40. 40. 0 40 80 120 160 QuizJET+IV QuizJET+Portal JavaGuide Attempts Attempts Results: Progress
  41. 41. F(1,32)= 11.303, p<.01 71.35% 42.63% 58.31% 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% QuizJET+IV QuizJET+Portal JavaGuide Success Rate Success Rate Results: Success
  42. 42. Class vs. Peers •  Peer progress was important, students frequently accessed content using peer models •  The more the students compared to their peers, the higher post-quiz scores they received (r= 0.34 p=0.004) •  Parallel IV didn’t allow to recognized good peers before opening the model •  Progressor added clear peer progress
  43. 43. Progressor 44
  44. 44. The Value of Peers 205.73 113.05 80.81 125.5 0 50 100 150 200 250 Attempts Progressor QuizJET+IV QuizJET+Portal JavaGuide 68.39% 71.35% 42.63% 58.31% 0.00% 20.00% 40.00% 60.00% 80.00% Success Rate Progressor QuizJET+IV QuizJET+Portal JavaGuide
  45. 45. The Secret
  46. 46. Take-home messages •  A combination of progress-based and state- based adaptive link annotation increases the amount and the diversity of student work with non-mandatory educational content •  The effect is stable and the scale of it is quite large •  Properly organized Social Navigation might be at least as successful as the knowledge-based •  Requires a long-term classroom study to observe
  47. 47. Why It Is Important? •  Many systems demonstrated their educational effectiveness in a lab-like settings: once the students are pushed to use it - it benefits their learning •  However, once released to real classes, these systems are under-used - most of them offer additional non- mandatory learning opportunities •  “Students are only interested in points and grades” •  Convert all tools into credit-bearing activities? •  Or use alternative approaches to increase motivation
  48. 48. What we are doing now? •  Exploring new generation of open social modeling tools in wide variety if classes and domains from US to Nigeria –  Interested to be a pilot site? •  Exploring more advanced guidance and modeling approaches based on large volume of social data •  Applying open social modeling to motivate readings
  49. 49. Acknowledgements •  Joint work with –  Sergey Sosnovsky –  Michael Yudelson –  Sharon Hsiao •  Pitt “Innovation in Education” grant •  NSF Grants –  EHR 0310576 –  IIS 0426021 –  CAREER 0447083
  50. 50. Try It! •  http://adapt2.sis.pitt.edu/kt/ •  Brusilovsky, P., Sosnovsky, S., and Yudelson, M. (2009) Addictive links: The motivational value of adaptive link annotation. New Review of Hypermedia and Multimedia 15 (1), 97-118. •  Hsiao, I.-H., Sosnovsky, S., and Brusilovsky, P. (2010) Guiding students to the right questions: adaptive navigation support in an E- Learning system for Java programming. Journal of Computer Assisted Learning 26 (4), 270-283. •  Hsiao, I.-H., Bakalov, F., Brusilovsky, P., and König-Ries, B. (2013) Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia [PDF] Read About It!

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