Fostering Deep Learning in Problem-Solving Contexts through Effective Design of Learning Environments with Technology Support Proposal Co-Chairs Minhong Wang, The University of Hong Kong, Hong Kong Sharon Derry, University of North Carolina at Chapel Hill, USA Xun Ge, University of Oklahoma, USA
Discussant
J. Michael Spector, University of North Texas, USA
Abstract Learning through problem-solving has been widely adopted to provide ample opportunity for deep learning. However, learning in problem contexts often involves complex processes. Many students tend to engage in surface experience rather than deep learning by in-depth understanding of practical experience, relating new ideas with prior knowledge or experience, converging knowledge by resolving conflicts, and combining discrete pieces of knowledge into a coherent whole. The participants of this session will share and discuss their recent studies on the design of problem-oriented learning environments that make deep learning accessible from different perspectives. The focus will be on challenges of deep learning in problem-solving contexts, effective design of learning environments that address the challenges, and meaningful analysis of learning in such environments. Summary Objectives Learning through inquiry and problem-solving has been widely promoted to foster deep learning by grounding abstract knowledge in real-world situations. Given complex processes involved in learning with real-world problems and authentic tasks, many students tend to engage in surface experience rather than deep learning. Deep learning has constantly emphasized intrinsic motivation and in-depth understanding by relating ideas, using evidence, examining the logic, and looking for pattern and principles [1, 2]. Deep learning in problem-solving contexts requires more attentions to in-depth reflection on practical experience, externalizing tacit aspects of complex tasks, relating new ideas with prior knowledge or experience, converging knowledge by resolving conflicts, and combining discrete pieces of knowledge into a coherent whole [1, 2]. To address these challenging issues, most members in this proposal have been working together through a related workshop at ICLS 2016. The AERA session aims to provide a forum for exchanging ideas with more researchers interested in our themes, i.e., understanding the challenges of deep learning in problem-solving contexts, effective design of learning environments that address the challenges, and meaningful analysis of learning in such environments. Overview The first contribution discusses deep learning through experimentation in an immersive virtual ecosystem. The second studies the challenges in implementing design-based science activities that foster deep learning. The third investigates how mobile devices afford deep learning in outdoor problem-solving activities. The fourth explores interview framing and embodiment that help elicit deep learning in a school robotics course. The fifth examines how reflective learning with clinical problems can be fostered through process visualization and adaptive feedback. The sixth discusses self-regulated learning in an undergraduate PBL curriculum in health science. The seventh examines how deep learning is exhibited in students’ processing of feedback. The eighth explores the coconstruction of deep inquiry by students in an elementary science classroom. Finally, the ninth contribution studies the impact of conflict resolution on knowledge convergence in problem-solving contexts.
Significance It is important to provide all students with ample opportunity to engage in deep learning that enable them to thrive [3]. While traditional learning environments have been increasingly extended to support deep learning by linking abstract knowledge with realworld problems, it is critical to bolster the understanding of multiple challenges of deep learning in problem-solving contexts, and how such challenges can be resolved by effective design of the learning environments. The proposed session will constitute an important step in this much-needed direction.
Structure The session will start from a brief introduction of the theme with relevant theory and principles (5 minutes). In the second part, each poster will be given 3 minutes for presentation, followed by 30-minute direct discussion between attendees and presenters (60 minutes). The third part will be discussing questions and sharing views from multiple perspectives (20 minutes). The last part will be a summary to draw together a set of approaches or strategies on how deep learning with real-world problems and authentic tasks can be effectively designed, implemented, and analyzed (5 minutes). References
[1]
Chin, C., & Brown, D. E. (2000). Learning in science: A comparison of deep and
surface approaches. Journal of Research in
Science Teaching, 37(2), 109–138.
[2]
Entwistle, N. (2000). Promoting deep
learning through teaching and assessment: Conceptual frameworks and educational
contexts. Paper presented at the first annual conference of the Teaching and
Learning Research Programme. Leicester Retrieved from
http://www.etl.tla.ed.ac.uk/publications.html.
[3] Noguera,
P., Darling-Hammond, L., & Friedlaender, D. (2015). Equal Opportunity for Deeper
Learning. Students at the Center: Deeper
Learning Research Series. Boston, MA: Jobs for the Future.
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