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ACM ITICSE 2014 - Talk on Motivational Active Learning


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ACM ITICSE 2014 - Talk on Motivational Active Learning

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ACM ITICSE 2014 - Talk on Motivational Active Learning

  1. 1. M O T I VAT I O N A L A C T I V E L E A R N I N G ( M A L ) J O H A N N A P I R K E R , M A R I A R I F F N A L L E R - S C H I E F E R , C H R I S T I A N G Ü T L J P I R K E R @ M I T. E D U G R A Z U N I V E R S I T Y O F T E C H N O L O G Y
  2. 2. “ I H E A R A N D I F O R G E T. I S E E A N D I R E M E M B E R . I D O A N D I U N D E R S TA N D . ”
  3. 3. “Despite great lecturers, attendance at MIT's freshman physics course dropped to 40% by the end of the term, with a 10% failure rate.” H T T P : / / W E B . M I T. E D U / E D T E C H / C A S E S T U D I E S / T E A L . H T M L
  4. 4.
  5. 5. T E C H N O L O G Y- E N A B L E D A C T I V E L E A R N I N G • Collaborative learning (groups from 3-9) • Desktop experiments linked to laptops • Media-rich visualisations & simulations • Personal response systems to stimulate interaction between instructor and students
  6. 6. “The failure rate, a major trigger for the project, has decreased substantially while the learning gains as measured by standard assessment instruments have almost doubled.” B E L C H E R , D O R I - H T T P : / / W E B . M I T. E D U / E D T E C H / C A S E S T U D I E S / P D F / T E A L 1 . P D F
  7. 7. Instead of frustrating and stressing students with failure rates at exams, we want to reward them for putting extra effort to account for mistakes and not punish them for failing one single exam.
  8. 8. T H E C O U R S E • Information Search & Retrieval • 1/3 Math, 1/3 Programming, 1/3 Theory • Master Students • Small classes (limited to 28 students)
  9. 9. O B J E C T I V E S • Design a course combining (1) theoretical background & concepts, (2) algorithmic understanding, and (3) analytical understanding of mathematical models • Engaging students by interactivities & motivational activities • Increase the students’ activities and motivation for hands-on exercises
  10. 10. C O M B I N E I N T E R A C T I V E E N G A G E M E N T W I T H G A M I F I C AT I O N • Collaborative Learning: Small subgroups (2 - 4) • Constant interactions between instructor & students with concept questions, small quizzes, and discussion questions • Immediate feedback on their task performance • Motivational feedback: Badges for special activities and leaderboard information • Errors are allowed: Revision of assignments & quizzes • Adaptive class design: Learning progress during and after class allows in-time adaption the individual learning speed of the class.
  11. 11. M O T I VAT I O N A L A C T I V E L E A R N I N G G R O U P L O C AT I O N Lecture Block C L A S S Recap Quiz 1 C L A S S Concept Questions 1 C L A S S Concept Quiz 1 C L A S S Discussion Question 2 C L A S S Research Question 2 C L A S S Programming Assignments 2 - 4 C L A S S Calculation Assignments 2 - 4 C L A S S Reflection Quiz 1 H O M E Reflection Forum / Report 4 H O M E
  12. 12. 0" 20" 40" 60" 80" 100" 120" 140" 160" 180" Minutes" Timline"of"lecture"3" Lecture" 3.1."Recap"Quiz" 3.2"Concept"Ques?on" 3.3."Short"Calcula?on:"Bayes"Theorem" "3.4."Short"Calcula?on:"Ranking"1" 3.5."Short"Calcula?on:"Ranking"2" 3.6."Discussion:"Steps"of"Ranking" 3.7."Concept"Ques?on"Quiz" "3.11."Discussion:"Quality"of"Retrieval"Models" 3.12."Concept"Ques?on" 3.13."Calcula?on"Retrieval"Performance(Part1/2)" 3.14."Concept"Ques?on"Quiz:"Precision"/"Recall" 3.15."Calcula?on"Retrieval"Performance"(Part"2/2)"
  13. 13. T H E S T U D Y - G O A L S • Evaluate the students’ understanding of the course content • Analyze the students’ engagement and motivation • Analyze the students’ attitude towards the new model and the used e-learning environment
  14. 14. T H E S T U D Y - M AT E R I A L S & M E T H O D S • Qualitative & Quantitative Methods & Field Observation • Learning progress: Pre & Post-Quizzes • Active Participation: Field observation • Reflection of content types, motivation types (AMS), rating of gamification aspects)
  15. 15. E X P E R I E N C I N G C O O P E R AT I V E L E A R N I N G “The group assignments during classes were the best concept. It was good to use the concept just learned to remember it better, but also eventual misunderstandings could be discussed”
  16. 16. Arith. Mean Std Dev I Prefer Activities In Teams. 3.38 1.32 I Prefer Activities In Groups Of 2 Over Activities In Groups Of 4 4.1 1.3 I Would Have Liked More Activities In A Team Of Four Than In A Team Of Two 2.33 1.62 I Would Have Liked More Single Activities In This Course 2.57 1.29 The Topics Were Easier To Understand In Groups Of 2 3.9 1.48 The Topics Were Easier To Understand In Groups Of 4 2.95 1.28 The Topics Were Easier To Understand Alone 2.29 1.19 I Prefer To Be Graded / Get Points Individually 3.05 1.32 I Prefer To Get Feedback Individually 3.24 1.18 I Learned More In Group Assignments Than In Individual Assignments 3.38 1.12
  17. 17. E X P E R I E N C I N G M O T I VAT I O N “[I liked] the chance to improve already graded work. It was also a motivating thing to immediately see received points”; “[I liked] 2nd chances“; “[I like that it is] hard to fail this course and hard to get lost and procrastinate”
  18. 18. Arith. Mean Std Dev I Liked Getting Points Rather Than Grades For Exercises. 3.95 1.12 I Was Motivated To Do The Bonus Assignments 3.57 1.25 I Liked Earning Badges 2.52 1.21 Earning Badges Was Not Important To Me 4.38 1.24 I Used The Grading Book To View My Points 4.67 0.58 I Used The Grading Book To View My Ranking 3.67 1.35 I Was Interested In The Ranking Information 3.33 1.43 Seeing My Own Ranking Motivated Me To Conduct Further Assignments 2.81 1.6
  19. 19. E X P E R I E N C I N G I N T E R A C T I V I T Y “I liked the interactivity of the course. It was not like in other assignment-based courses, where exercises must be done at home and then presented. There was time for researching or calculations, and then the results were discussed.”; “I liked the interactive learning. The structure of the course, some parts lecture, immediately followed by exercises, was nice.”
  20. 20. D E S I G N E D F O R A D A P TA B I L I T Y “It was hard to follow all the stuff showed in the lecture, but the lecturer obviously read the feedback after each block and slowed down a little bit at the end which was much better”
  21. 21. A S S E S S I N G T H E L E A R N I N G P R O G R E S S 28 Students 25/28 Best Grade 1 Dropout 2 Second best grade
  22. 22. S O M E TA K E A WAY S • Collaborative > Competition • Attract different learning styles. Competition as “extra” for the target student group, also badges only work for some students • Use groups of 3 • Design for adaptability
  23. 23. F U T U R E W O R K • Lots of data left to evaluate • Updated course • Smaller groups • Better designed badges • More automatic assessment
  24. 24. T H A N K Y O U F O R Y O U R AT T E N T I O N . J O H A N N A P I R K E R , J P I R K E R @ M I T. E D U , W W W. J P I R K E R . C O M , @ J O E Y P R I N K