The document discusses innovative pedagogies in problem-based learning (PBL) with a focus on learning analytics, exploring how formal and informal learning can enhance employability. It includes various studies on collaborative learning, emotional analytics, and adaptive teaching methods, emphasizing the importance of understanding learning processes and environments. Furthermore, it highlights the role of learning dispositions, motivation, and data-driven insights in improving educational experiences.

1. PBL, Transfer and New Media
@DrBartRienties
Reader in Learning Analytics
11th of December 2015
Dublin
FACiLiTATE Employability and
Transfer - E/PBL research and
practice Seminar

2. Innovative Pedagogy 2015 report out!!!
http://www.open.ac.uk/blogs/innovating/
Crossover learning
Connecting formal and informal learning
Learning through argumentation
Developing skills of scientific argumentation
Incidental learning
Harnessing unplanned or unintentional learning
Context-based learning
How context shapes and is shaped by the process of learning
Computational thinking
Solving problems using techniques from computing
Learning by doing science with remote labs
Guided experiments on authentic scientific equipment
Embodied learning
Making mind and body work together to support learning
Adaptive teaching
Adapting computer-based teaching to the learner’s knowledge and action
Analytics of emotions
Responding to the emotional states of students
Stealth assessment
Unobtrusive assessment of learning processes

4. (Social) Learning Analytics
“LA is the measurement, collection, analysis and reporting of data about learners
and their contexts, for purposes of understanding and optimising learning and the
environments in which it occurs” (LAK 2011)
Social LA “focuses on how learners build knowledge together in their cultural
and social settings” (Ferguson & Buckingham Shum, 2012)

6. A) Role of informal learning in PBL Medical
programme
C) How do students choose
collaboration PBL tools?B Learning analytics with
120+ variables in PBL Math course

7. Informal learning
• Medical programme
• 1st year students
• Face-to-Face
• Measurements after 4 months studying
• Problem-Based Learning
• N=302

8. Hommes, J., Rienties, B., de Grave, W., Bos, G., Schuwirth, L., Scherpbier, A. (2012). Visualising the invisible: a network approach to reveal the informal
Formal and Informal Interaction between learners and teachers crucial for learning
processes and outcomes (DeCuyper et al., 2010; Giesbers, Rienties, et al., 2013;
Hommes, Rienties et al, 2012; Rienties et al., 2012, 2013a, 2013b)

9. Hommes, J., Rienties, B., de Grave, W., Bos, G., Schuwirth, L., Scherpbier, A. (2012). Visualising the invisible: a network approach to reveal the informal
social side of student learning. Advances in Health Sciences Education. 17(5), 743-757. Impact factor: 2.089.
Not significant
Not significant

10. Rienties, B., Hernandez Nanclares, N., Hommes, J., & Veermans, K. (2014). Understanding emerging knowledge spillovers in small-group learning settings; a networked learning perspective. In
V. Hodgson, M. De Laat, D. McConnell & T. Ryberg (Eds.), The Design, Experience and Practice of Networked Learning (Vol. 7, 127-148) Springer: Dordrecht.
Learn more inside assigned group
Learn more outside group
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80% occurs outside formal classroom

11. Hommes, J., Arah, O. A., de Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2014). Medical students perceive better group learning processes when large classes are
made to seem small. PLOS One, 9(4), e93328. doi: 10.1371/journal.pone.0093328

12. Hommes, J., Arah, O. A., de Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2014). Medical students perceive better group learning processes when large classes are
made to seem small. PLOS One, 9(4), e93328. doi: 10.1371/journal.pone.0093328

13. Hommes, J., Arah, O. A., de Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2014). Medical students perceive better group learning processes when large classes are
made to seem small. PLOS One, 9(4), e93328. doi: 10.1371/journal.pone.0093328

14. Introduction math/stats
• Business
• 1st year students
• Blended
• 0-12 weeks after start studying
• Adaptive learning/Problem-Based
Learning
• N=990

16. Diagnostic
EntryTests
Week 0 Week 1 Week 2 Week 3 Week 4 Week 6Week 5
Quiz 1 Quiz 2 Quiz 3
Final
Exam
• Math-
Exam
• Stats-
Exam
--------------------------------------------- BlackBoard LMS behaviour -----------------------------------------
Week 7
Mastery scores
MyMathlab
Mastery scores
Practice time #
Attempts
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
MyMathlab
Practice time #
Attempts
Mastery scores
MyStatlab
Mastery scores
Practice time #
Attempts
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
Practice time
# Attempts
Mastery scores
MyStatlab
Practice time
# Attempts
Demogra-
phic data
QMTotal
Week 8
Learning Styles,
Motivation,
Engagement
Learning
Emotions
-Learning dispositions ------------------ ------------------------------------------------------------------
Tempelaar, D., Rienties, B., Giesbers., B. (2015). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in
Human Behaviour. 47, 157-167. Impact factor: 2.067.

18. LMS prediction Not great 

19. E-tutorials prediction Substantial improvement!

20. Entry test and quizes Even better!

21. All elements combined:

22. Using track data we can follow:
-who is struggling?
-where?
-when?
-why?

24. Who is struggling in week 3?
What can be done about this?
• (Personalised) feedback
• (Personalised) examples
• Peer support
• Emotional/learning support

25. Online acculturation/introduction
course Economics
• Economics/acculturation
• (Nearly) 1st year international students
• Distance Education
• -6 – 0 weeks before starting @uni
• Problem-Based Learning
• N=110

26. + e-book system

27. Dynamic interaction of sychronous and
asychronous learning
Giesbers, B., Rienties, B., Tempelaar, D.T., & Gijselaers, W. H. (2014). A dynamic analysis of the interplay between asynchronous and synchronous
communication in online learning: The impact of motivation. Journal of Computer Assisted Learning, 30(1), 30-50. Impact factor: 1.632.

28. Intrinsic Motivation ↑ initial asynchronous contributions 
↑ in asynchronous and synchronous contributions
Giesbers, B., Rienties, B., Tempelaar, D.T., & Gijselaers, W. H. (2014). A dynamic analysis of the interplay between asynchronous and synchronous
communication in online learning: The impact of motivation. Journal of Computer Assisted Learning, 30(1), 30-50. Impact factor: 1.632.

29. Is data from Virtual Learning Environment systems (e.g., Blackboard, Moodle)
useful for learning (analytics) and PBL? What else should we focus on to
improve our understandings of social interaction?
• “Raw” VLE data does not seem very
useful
• (entry)quizzes/formative learning
outcomes in combination with learning
dispositions provide good early-
warning systems

30. Implications for EURO CALLHow can we make learning more
personalised, adaptive and meaningful,
and what are the implications for PBL?
• What about informal learning?
• Individual differences? Learning
dispositions?
• Emotions?
• Ethics?

31. PBL, Transfer and New Media
@DrBartRienties
Reader in Learning Analytics
11th of December 2015
Dublin
FACiLiTATE Employability and
Transfer - E/PBL research and
practice Seminar