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. “ 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. “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. http://gallery.carnegiefoundation.org/collections/keep/jbelcher/

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. “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. 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. 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. 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. 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. 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. 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)"

16. 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

17. 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)

18. 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”

19. 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

20. 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”

21. 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

22. 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.”

23. 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”

24. 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

25. 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

26. 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

27. 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