From Active-in- Behaviour to Active-in-Thinking in Learning with Technology

1. The 11th Workshop of Knowledge Management & E-Learning From Active-in-Behaviour to Active-in-Thinking in Learning with Technology Maggie Minhong Wang Faculty of Education The University of Hong Kong June 25, 2019

3. 香港大学教育学院教授 • 知识管理与数位学习实验室 (KM&EL Lab) 主任 • 东方学者讲座教授， 华东师范大学 • 北京师范大学未来教育高精尖创新中心访问研究教授 • 哈佛大学、麻省理工学院、剑桥大学访问学者 • 《Knowledge Management & E-Learning》主编 • ESCI & Scopus indexed • 《Information & Management》副主编 • SSCI indexed

4. Areas • Technology-Enhanced Learning; • Higher Education; STEM Education; Medical Education; • Workplace Learning; Knowledge Management; • Applications of Artificial Intelligence • Focus • E-Learning Design and Analytics • Problem Solving & Inquiry Learning • Knowledge Visualization for Deeper Learning • 168 publications • 85 journal articles, 51inSSCI/SCI indexed journals • 4 books including one monograph published by Springer • 13 special issues • 10 book chapters • 56 conference papers

5. Learning Process what we think Affect / what we feel what we do Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74, 59-109.

6. Learning Process What we think e.g., memory, understanding, knowledge, problem solving, planning (mental contents and processes) What we feel e.g., interest, boredom, happiness, frustration, confidence, and anxiety (motivation, emotion) What we do e.g., reading, practice, communication interaction, collaboration, (activities) Affect /

7. 3 basic human capacities Cognition Affect/ Emotion Behavior Environment Human Being

8. Learning with technology What we think e.g., memory, understanding, knowledge, problem solving, planning (mental contents and processes) Technology What we feel e.g., interest, boredom, happiness, frustration, confidence, and anxiety (motivation, emotion) What we do e.g., reading, practice, communication interaction, collaboration, (activities) Affect /

9. Learning with technology (cont’) • More flexibility, more choices, more autonomous => student-centered, self-directed • Behavior • ubiquitous access to digital learning content • extensive communication and collaboration with peers • authentic task experience in virtual environments • Motivation and emotion • Intrinsic motivation and confidence vs. frustration and anxiety • Thinking? • developing meaningful understanding and desired performance • Any tool?

10. What we think e.g., memory, understanding, knowledge, problem solving, planning (mental contents and processes) What we feel e.g., interest, boredom, happiness, frustration, confidence, and anxiety (motivation, emotion) What we do e.g., reading, practice, communication interaction, collaboration, (activities) Affect /

11. Thinking: cognitive engagement • It incorporates thoughtfulness and willingness to exert the effort necessary to comprehend and master (complex) knowledge and (difficult) skills. • Effort • Motivation and emotion - willingness • Strategy use - strategic learning • It can range from simple memorization to the use of relevant learning strategies that promote deep understanding and expertise.

12. Cognitive engagement (cont’) • Need for learning strategies • e.g., memorization, summarizing, elaboration, seeking help • Consider: • Knowledge construction, retention, application/transfer (problem solving) • Planning, regulation • Social interaction • Deep-level vs. surface-level

13. Cognitive engagement (cont’) • Deep-level strategies • promote deep understanding and effective use of knowledge to solve complex problems • e.g., relating new ideas with existing knowledge, connection among ideas, systems thinking, knowledge construction & con-construction, knowledge transfer, monitoring comprehansion, etc. • Deep-level cognitive engagement • (Deeper learning)

14. Learning through problem-solving • Experiential learning, learning by doing • Situated learning /contextual learning • Inquiry-based learning • Discovery learning • Problem-based learning, problem solving • Project-based learning • Design-based learning • ……

15. Learning through problem-solving • Role of the learner • Student-centered learning • Self-directed, autonomous • Group/team-based learning, collaborative learning • Knowledge sharing, co-construction, group task • Role of the teacher • facilitator

16. Learning through problem-solving • Widely promoted in education, more in STEM education • Learning by working with real-world problems • ill-structured problems (Jonassen, 1997) • authentic whole tasks (Van Merriënboer & Kirschner, 2013) • Underlying theories • Situated cognition theory (Brown, Collins, & Duguid, 1989) • Situated learning theory (Lave & Wenger, 1991) • Learning and cognition occur in physical and social contexts where knowledge is created and applied • Knowledge is assumed to be better constructed through interaction with problem-oriented, socially situated environments

17. Advantages • Abstract knowledge  Real-world practice • Increase curiosity, motivation, and engagement • Develop critical thinking and problem solving skills • Apply/Integrate knowledge • Problem-solving strategies • Improve communication and collaboration skills • Improve knowledge • Knowledge consolidation • Knowledge extension • Knowledge transfer 21st Century Skills

18. Challenges • Problem-solving involves complex cognitive/thinking processes • integrating problem information with subject knowledge • formulating and justifying hypotheses • reasoning with multiple variables • These processes are often • inaccessible to learners • overlooked by instructors or experts, largely automatic or subconscious with experience • difficult for learners to capture and for teachers to facilitate • overburden learners e.g.,

19. Challenges • Inconclusive findings on learning outcomes • Problem-solving performance/expertise • Knowledge construction and transfer • Anxiety, low confidence => engagement, motivation • Learning through problem-solving is more easily advocated than accomplished • Wang, M., Kirschner, P. A., & Bridges, S. M. (2016). Computer-based learning environments for deep learning in inquiry and problem solving contexts. In Proceedings of the 12th International Conference of the Learning Sciences (ICLS). Singapore. • Wang, M., Derry, S., & Ge, X. (2017). Guest Editorial: Fostering deep learning in problem solving contexts with the support of technology. Educational Technology & Society, 20(4), 162-165.

20. Technology can’t solve all problems • Emerging learning technologies have substantially expanded the opportunities for learning through problem-solving • Virtual realities, simulations, games, web-based applications, etc • Advantages • Flexible access and interaction • Multimedia representations, immersive learning environment • Easy communications and collaboration • Computer-based learning support • … How to make the complex cognitive processes accessible to learners? => Foster deep-level cognitive engagement (deeper learning)

23. + Technology-enhanced Learning Problem Solving Foster deeper learning by making complex processes visible and accessible How to design such learning? (Learning environment, learning experience/activities, curriculum, etc.) + How to analyze such learning? (more than knowledge test)

24. Deeper learning • Deeper learning is characterized by a high level of engagement (doing + thinking) and often accompanied by intrinsic motivation, with a target to achieve a high level of understanding and performance. • Difficult to sustain <= challenges, difficulties • Complex cognitive processes in problem-solving • To make deeper learning sustainable • Help learners to manage the complex cognitive process => Improving thinking => Support doing => Sustain motivation

25. Deeper learning (cont’) • Making the complex cognitive processes visible and accessible to learners for effective thinking, action and reflection, e.g. • by externalizing the complex process of solving a problem • revealing the knowledge underlying problem-solving • connecting new ideas with prior knowledge • combining discrete pieces of knowledge into a coherent whole • making knowledge evolve by resolving conflict views Learning by doing+Learning by thinking • Wang, M., Kirschner, P. A., Spector, J. M., & Ge, X. (2018). Computer-based learning environments for deeper learning in problem-solving contexts. Computers in Human Behavior, 87, 403-405. • Wang, M., Derry, S., & Ge, X. (2017). Guest Editorial: Fostering deep learning in problem solving contexts with the support of technology. Educational Technology & Society, 20(4), 162-165.

26. Special issues • Minhong Wang and Rupert Wegerif (Guest Eds.) (2019). Special Issue on From Active-in-Behaviour to Active-in-Thinking in Learning with Technology. British Journal of Educational Technology (in press). • Minhong Wang, Paul A. Kirschner, J. Michael Spector, & Sharon Derry (Guest Eds.) (2018). Special Issue on Computer-Based Learning Environments for Deeper Learning in Problem Solving Contexts. Computers in Human Behavior, Vol.87. • Minhong Wang, Sharon Derry, & Xun Ge (Guest Eds.) (2017). Special Issue on Fostering Deep Learning in Problem Solving Contexts with Technology Support. Educational Technology & Society, Vol.20, No.4.

27. Study VisualizingThinking with Cognitive Feedback in Medical Diagnostic Problem-Solving Led by Minhong (Maggie) WangKM&EL Lab, The University of Hong Kong

28. Background • Funded by the General Research Fund from the Research Grants Council of Hong Kong • Participants were senior year medical students • Glaucoma diagnosis was chosen as the learning subject • 青光眼诊断 • Learning through practice with clinical cases • Crucial to the development of medical students’ diagnostic competence.

29. Complex Problem-Solving • Involving complex processes • Implicit – not easy to communicate for learning and reflection • Difficult – not easy to make good performance by novices • Approach • Enabling learners to capture the complex process by • Visualizing the complex process for reflection and further practice • Providing computer-generated cognitive feedback during the task process learner performance expert performance difference

30. Glaucoma Diagnosis & e-Learning System Simulated problem-solving context Reflection with cognitive feedback

31. Simulated problem-solving context • Simulated realistic clinical diagnostic problem-solving • Given incomplete information of a problem • Need to collect further information through clinical examinations • Need to make intermediate judgments based on available information • Go through the above in several rounds • Make a diagnostic conclusion

32. Computer-generated cognitive feedback • Learner performance  expert performance • Expert knowledge was collected and utilized for generation of adaptive feedback to learners throughout the task process. • Clinical examination • Intermediate judgement • Diagnostic conclusion

33. Knowledge construction in a mental map

34. Experimental study • Participants • 60 senior year medical students • Randomly assigned to the experimental and control groups • Learning tasks • Five cases for self-practice • Two cases for assessment before and after the study • Learning environment • Learners in both groups can view the expert’s summary of the case after practising the case 10 times. • Learners in the experimental group can view computer-generated performance feedback after practising the case 10 times or when the learner’s diagnostic process was 70% identical to the reference solution.

35. Learning Outcomes (cont’) • Problem-solving performance • Clinical examination • Intermediate judgement • Diagnostic conclusion • The experimental group outperformed the control in all aspects • A larger effect on the students’ selection of clinical examinations and intermediate judgements than on their diagnostic conclusions • Students could reach a correct diagnostic conclusion even with errors in their intermediate judgements and selection of clinical examinations • Cognitive feedback throughout the task process has the potential to address this issue

36. Learning Outcomes • Knowledge tests • No significant difference between the two groups before and after the study • Mental map • Initial information • Clinical examination • Intermediate judgment • Diagnostic conclusion • Logical reasoning • Academic emotions • Enjoyment • Confidence • Frustration • Anxiety • The experimental group outperformed in clinical examination, intermediate judgment, and logical reasoning • but not in initial information and diagnostic conclusion • Significant differences in all aspects between two groups • The experimental group perceived more enjoyment and confidence with less frustration and anxiety

37. Summary • Learning with complex real-world problems • Involves complex processes inaccessible to learners • Self-reflection only is inadequate for novices to develop expert-like performance. • Our approach • Visualisation of learners’ diagnostic processes to support self-reflection • Providing students with adaptive feedback on the gap between their performance and expert performance • Cognitive feedback on task performance can reduce learners’ anxiety and frustration while working with complex tasks • Compared with its effects on learners’ diagnostic conclusions, cognitive feedback is more effective in enhancing learners’ performance when selecting clinical examinations and making intermediate judgements • Improve learners’ understanding of the mechanism of the complex diagnostic process.

38. External representation of cognitive processes in visual forms • Facilitate the complex cognitive processes by: • Utilizing human brains’ capacity to rapidly process visual images • A diagram is sometimes worth a thousand words. (Larkin & Simon, 1987) • Meaningful representation of complex ideas, e.g., • representing information verbally and spatially • reducing ambiguous expression • grouping together relevant information • Making disciplinary norms of thinking explicit • Highlight the key elements of cognition

39. A series of studies • We found that concept map is inadequate for problem-solving or design-oriented tasks. • We demonstrated the effectiveness of integrated cognitive maps (dual mapping) for learning through problem-solving • We demonstrated the effectiveness of integrated cognitive maps (3D thinking graph) for learning through problem-solving dual mapping • We examined the effects of adding computer-generated, expert feedback in learning through problem-solving with cognitive maps. • We investigated the effects of the simple-to-complex progressive approach to learning through problem-solving with cognitive maps.

40. Related publications • Minhong Wang*, Bo Cheng, Juanjuan Chen, Neil Mercer, and Paul A. Kirschner (2017). The use of web-based collaborative concept mapping to support group learning and interaction in an online environment. The Internet and Higher Education, 34, 28–40. (SSCI IF 5.847, ranked 2/238 in Education) • Bian Wu, Minhong Wang*, Tina A. Grotzer, Jun Liu, and Janice M. Johnson (2016). Visualizing Complex Processes Using a Cognitive-Mapping Tool to Support the Learning of Clinical Reasoning. BMC Medical Education,16: 216. (SSCI & SCI) • Juanjuan Chen, Minhong Wang*, Tina A. Grotzer, and Chris Dede (2018). Using a three-dimensional thinking graph to support inquiry learning. Journal of Research in Science Teaching, 55 (9), 1239-1263. (SSCI IF 3.210, ranked 11/238 in education) • Minhong Wang*, Bei Yuan, Paul A. Kirschner, Andre W. Kushniruk, and Jun Peng (2018). Reflective learning with complex problems in a visualization-based learning environment with expert support. Computers in Human Behavior, 87, 406-415. (SSCI) • Jun Peng, Minhong Wang*, Demetrios Sampson, and Jeroen van Merrienboer (2019). Using a visualization-based and progressive learning environment as a cognitive tool for learning computer programming. Australasian Journal of Educational Technology, 35(2), 52-68. (SSCI)

41. Project members Minhong Wang Juanjuan Chen Bo Cheng Jun Peng Bian Wu Bei Yuan

42. Thank you! magwang@hku.hk

43. Making sense of models: How teachers use agent-based modeling to advance mechanistic reasoning • Scaffolding Ecosystems Science Practice by Blending Immersive Environments and Computational Modeling • Using computer-based cognitive mapping to improve students’ divergent thinking for creativity development • Fostering Deeper Learning in a Flipped Classroom: Effects of Knowledge Graphs Versus Concept Maps • Scaffolding design thinking in an online STEM preservice teacher training • Pedagogical Moves and Student Thinking in Technology-Mediated Medical Problem-Based Learning: Supporting Novice-Expert Shift • Understanding when students are active-in-thinking through modeling-in-context • Supporting collaborative learning using a diagram-based visible thinking tool based on cognitive load theory • Designing Online Tools for Collaborative Knowledge Integration • The potential of open learner models to promote active thinking by enhancing self-regulated learning in online higher education learning environments