integrated problem solving environments

Topic 16: Integrated Problem Solving Environments

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Daniela di Serafino 19 ,
Elias Houstis 19 ,
Peter Sloot 19 &
Domenico Talia 19

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3149))

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European Conference on Parallel Processing

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Problem Solving Environments (PSEs) can be defined as integrated computing environments for developing, executing and analysing applications in a specific domain. They provide a set of user-friendly mechanisms and tools that allow to “compose” an application, by gluing together, using some kind of problem-oriented language, different building blocks. Such building blocks range from libraries and application codes, to tools for I/O, data visualization and analysis, and interactive steering. PSEs may also incorporate some form of knowledge, in order to assist the users in formulating, solving and analysing their problems. The main motivation for developing PSEs is that they enable to build applications without dealing with most of the details related to hardware and software architectures, to solution algorithms and their implementations, and to analysis and monitoring tools, thus allowing end-users to concentrate on the application problems to be solved. PSEs can be used for different purposes, such as modelling and simulation, design optimisation, rapid prototyping, and decision support.

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Search@Home: A Commercial Off-the-Shelf Environment for Investigating Optimization Problems

Human and Computational Approaches for Design Problem-Solving

Architecture and Design of the HeuristicLab Optimization Environment

Rapid Prototype
Software Architecture
Data Visualization
Integrate Problem
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Marco Danelutto

Computer Science Department, University of Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy

Marco Vanneschi

Information Science and Technologies Institute (ISTI) The Italian National Research Council (CNR), Area della Ricerca, Via Giuseppe Moruzzi, 1, I-56126, Pisa, Italy

Domenico Laforenza

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di Serafino, D., Houstis, E., Sloot, P., Talia, D. (2004). Topic 16: Integrated Problem Solving Environments. In: Danelutto, M., Vanneschi, M., Laforenza, D. (eds) Euro-Par 2004 Parallel Processing. Euro-Par 2004. Lecture Notes in Computer Science, vol 3149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27866-5_128

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Home > Electronic Theses and Dissertations > Dissertations > 1033

Dissertations

An integrated environment for problem solving and program development.

Fadi Pierre Deek , New Jersey Institute of Technology

Document Type

Dissertation

Date of Award

Fall 10-31-1997

Degree Name

Doctor of Philosophy in Computing Sciences - (Ph.D.)

Computer and Information Science

First Advisor

James A. McHugh

Second Advisor

Starr Roxanne Hiltz

Third Advisor

Michael G. Hinchey

Fourth Advisor

Peter A. Ng

Fifth Advisor

Wilhelm Rossak

Sixth Advisor

Murray Turoff

Seventh Advisor

Thomas J. Marlowe

Eighth Advisor

Howard S. Kimmel

Ninth Advisor

Naomi G. Rotter

A framework for an integrated problem solving and program development environment that addresses the needs of students learning programming is proposed. Several objectives have been accomplished: defining the tasks required for program development and a literature review to determine the actual difficulties involved in learning those tasks. A comprehensive Study of environments and tools developed to support the learning of problem solving and programming was then performed, covering programming environments, debugging aids, intelligent tutoring systems, and intelligent programming environments. This was followed by a careful analysis and critique of these systems, which uncovered the limitations that have prevented them from accomplishing their goals.

Next, an extensive study of problem solving methodologies developed in this century was carried out and a common model for problem solving was produced. The tasks of program development were then integrated with the common model for problem solving. Then, the cognitive activities required for problem solving and program development were identified and also integrated with the common model to form a Dual Common Model for problem Solving and Program Development. This dual common model was then used to define the functional specifications for a problem solving and program development environment which was designed, implemented, tested, and integrated into the curriculum.

The development of the new environment for learning problem solving and programming was followed by the planning of a cognitively oriented assessment method and the development of related instruments to evaluate the process and the product of problem solving. A detailed statistical experiment to study the effect of this environment on students' problem solving and program development skills, including system testing by protocol analysis, and performance evaluation of students based on research hypotheses and questions, was also designed, implemented and the result reported.

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Deek, Fadi Pierre, "An integrated environment for problem solving and program development" (1997). Dissertations . 1033. https://digitalcommons.njit.edu/dissertations/1033

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An integrated environment for problem solving and program development

The copyright law of the United States (Title 17, United States Code) governs the making of photocopies or other reproductions of copyrighted material. Under certain conditions specified in the law, libraries and archives are authorized to furnish a photocopy or other reproduction. One of these specified conditions is that the photocopy or reproduction is not to be "used for any purpose other than private study, scholarship, or research." If a, user makes a request for, or later uses, a photocopy or reproduction for purposes in excess of "fair use" that user may be liable for copyright infringement, This institution reserves the right to refuse to accept a copying order if, in its judgment, fulfillment of the order would involve violation of copyright law.

Intelligent problem-solving as integrated hierarchical reinforcement learning

Manfred Eppe ORCID: orcid.org/0000-0002-5473-3221 1 nAff4 ,
Christian Gumbsch ORCID: orcid.org/0000-0003-2741-6551 2 , 3 ,
Matthias Kerzel 1 ,
Phuong D. H. Nguyen 1 ,
Martin V. Butz ORCID: orcid.org/0000-0002-8120-8537 2 &
Stefan Wermter 1

Nature Machine Intelligence volume 4 , pages 11–20 ( 2022 ) Cite this article

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43 Citations

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Cognitive control
Computational models
Computer science
Learning algorithms
Problem solving

According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in artificial agents and robots. However, so far, the problem-solving abilities of many human and non-human animals are clearly superior to those of artificial systems. Here we propose steps to integrate biologically inspired hierarchical mechanisms to enable advanced problem-solving skills in artificial agents. We first review the literature in cognitive psychology to highlight the importance of compositional abstraction and predictive processing. Then we relate the gained insights with contemporary hierarchical reinforcement learning methods. Interestingly, our results suggest that all identified cognitive mechanisms have been implemented individually in isolated computational architectures, raising the question of why there exists no single unifying architecture that integrates them. As our final contribution, we address this question by providing an integrative perspective on the computational challenges to develop such a unifying architecture. We expect our results to guide the development of more sophisticated cognitively inspired hierarchical machine learning architectures.

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Exploring the limits of hierarchical world models in reinforcement learning

Phy-Q as a measure for physical reasoning intelligence

Hierarchical generative modelling for autonomous robots

Gruber, R. et al. New Caledonian crows use mental representations to solve metatool problems. Curr. Biol. 29 , 686–692 (2019).

Article Google Scholar

Butz, M. V. & Kutter, E. F. How the Mind Comes into Being (Oxford Univ. Press, 2017).

Perkins, D. N. & Salomon, G. in International Encyclopedia of Education (eds. Husen T. & Postelwhite T. N.) 6452–6457 (Pergamon Press, 1992).

Botvinick, M. M., Niv, Y. & Barto, A. C. Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition 113 , 262–280 (2009).

Tomov, M. S., Yagati, S., Kumar, A., Yang, W. & Gershman, S. J. Discovery of hierarchical representations for efficient planning. PLoS Comput. Biol. 16 , e1007594 (2020).

Arulkumaran, K., Deisenroth, M. P., Brundage, M. & Bharath, A. A. Deep reinforcement learning: a brief survey. IEEE Signal Process. Mag. 34 , 26–38 (2017).

Li, Y. Deep reinforcement learning: an overview. Preprint at https://arxiv.org/abs/1701.07274 (2018).

Sutton, R. S. & Barto, A. G. Reinforcement Learning : An Introduction 2nd edn (MIT Press, 2018).

Neftci, E. O. & Averbeck, B. B. Reinforcement learning in artificial and biological systems. Nat. Mach. Intell. 1 , 133–143 (2019).

Eppe, M., Nguyen, P. D. H. & Wermter, S. From semantics to execution: integrating action planning with reinforcement learning for robotic causal problem-solving. Front. Robot. AI 6 , 123 (2019).

Oh, J., Singh, S., Lee, H. & Kohli, P. Zero-shot task generalization with multi-task deep reinforcement learning. In Proc. 34th International Conference on Machine Learning ( ICML ) (eds. Precup, D. & Teh, Y. W.) 2661–2670 (PMLR, 2017).

Sohn, S., Oh, J. & Lee, H. Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies. In Proc. 32nd International Conference on Neural Information Processing Systems ( NeurIPS ) (eds Bengio S. et al.) Vol. 31, 7156–7166 (ACM, 2018).

Hegarty, M. Mechanical reasoning by mental simulation. Trends Cogn. Sci. 8 , 280–285 (2004).

Klauer, K. J. Teaching for analogical transfer as a means of improving problem-solving, thinking and learning. Instruct. Sci. 18 , 179–192 (1989).

Duncker, K. & Lees, L. S. On problem-solving. Psychol. Monographs 58, No.5 (whole No. 270), 85–101 https://doi.org/10.1037/h0093599 (1945).

Dayan, P. Goal-directed control and its antipodes. Neural Netw. 22 , 213–219 (2009).

Dolan, R. J. & Dayan, P. Goals and habits in the brain. Neuron 80 , 312–325 (2013).

O’Doherty, J. P., Cockburn, J. & Pauli, W. M. Learning, reward, and decision making. Annu. Rev. Psychol. 68 , 73–100 (2017).

Tolman, E. C. & Honzik, C. H. Introduction and removal of reward, and maze performance in rats. Univ. California Publ. Psychol. 4 , 257–275 (1930).

Google Scholar

Butz, M. V. & Hoffmann, J. Anticipations control behavior: animal behavior in an anticipatory learning classifier system. Adaptive Behav. 10 , 75–96 (2002).

Miller, G. A., Galanter, E. & Pribram, K. H. Plans and the Structure of Behavior (Holt, Rinehart & Winston, 1960).

Botvinick, M. & Weinstein, A. Model-based hierarchical reinforcement learning and human action control. Philos. Trans. R. Soc. B Biol. Sci. 369 , 20130480 (2014).

Wiener, J. M. & Mallot, H. A. ’Fine-to-coarse’ route planning and navigation in regionalized environments. Spatial Cogn. Comput. 3 , 331–358 (2003).

Stock, A. & Stock, C. A short history of ideo-motor action. Psychol. Res. 68 , 176–188 (2004).

Hommel, B., Müsseler, J., Aschersleben, G. & Prinz, W. The theory of event coding (TEC): a framework for perception and action planning. Behav. Brain Sci. 24 , 849–878 (2001).

Hoffmann, J. in Anticipatory Behavior in Adaptive Learning Systems : Foundations , Theories and Systems (eds Butz, M. V. et al.) 44–65 (Springer, 2003).

Kunde, W., Elsner, K. & Kiesel, A. No anticipation-no action: the role of anticipation in action and perception. Cogn. Process. 8 , 71–78 (2007).

Barsalou, L. W. Grounded cognition. Annu. Rev. Psychol. 59 , 617–645 (2008).

Butz, M. V. Toward a unified sub-symbolic computational theory of cognition. Front. Psychol. 7 , 925 (2016).

Pulvermüller, F. Brain embodiment of syntax and grammar: discrete combinatorial mechanisms spelt out in neuronal circuits. Brain Lang. 112 , 167–179 (2010).

Sutton, R. S., Precup, D. & Singh, S. Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artif. Intell. 112 , 181–211 (1999).

Article MathSciNet MATH Google Scholar

Flash, T. & Hochner, B. Motor primitives in vertebrates and invertebrates. Curr. Opin. Neurobiol. 15 , 660–666 (2005).

Schaal, S. in Adaptive Motion of Animals and Machines (eds. Kimura, H. et al.) 261–280 (Springer, 2006).

Feldman, J., Dodge, E. & Bryant, J. in The Oxford Handbook of Linguistic Analysis (eds Heine, B. & Narrog, H.) 111–138 (Oxford Univ. Press, 2009).

Fodor, J. A. Language, thought and compositionality. Mind Lang. 16 , 1–15 (2001).

Frankland, S. M. & Greene, J. D. Concepts and compositionality: in search of the brain’s language of thought. Annu. Rev. Psychol. 71 , 273–303 (2020).

Hummel, J. E. Getting symbols out of a neural architecture. Connection Sci. 23 , 109–118 (2011).

Haynes, J. D., Wisniewski, D., Gorgen, K., Momennejad, I. & Reverberi, C. FMRI decoding of intentions: compositionality, hierarchy and prospective memory. In Proc. 3rd International Winter Conference on Brain-Computer Interface ( BCI ), 1-3 (IEEE, 2015).

Gärdenfors, P. The Geometry of Meaning : Semantics Based on Conceptual Spaces (MIT Press, 2014).

Book MATH Google Scholar

Lakoff, G. & Johnson, M. Philosophy in the Flesh (Basic Books, 1999).

Eppe, M. et al. A computational framework for concept blending. Artif. Intell. 256 , 105–129 (2018).

Turner, M. The Origin of Ideas (Oxford Univ. Press, 2014).

Deci, E. L. & Ryan, R. M. Self-determination theory and the facilitation of intrinsic motivation. Am. Psychol. 55 , 68–78 (2000).

Friston, K. et al. Active inference and epistemic value. Cogn. Neurosci. 6 , 187–214 (2015).

Berlyne, D. E. Curiosity and exploration. Science 153 , 25–33 (1966).

Loewenstein, G. The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116 , 75–98 (1994).

Oudeyer, P.-Y., Kaplan, F. & Hafner, V. V. Intrinsic motivation systems for autonomous mental development. In IEEE Transactions on Evolutionary Computation (eds. Coello, C. A. C. et al.) Vol. 11, 265–286 (IEEE, 2007).

Pisula, W. Play and exploration in animals—a comparative analysis. Polish Psychol. Bull. 39 , 104–107 (2008).

Jeannerod, M. Mental imagery in the motor context. Neuropsychologia 33 , 1419–1432 (1995).

Kahnemann, D. & Tversky, A. in Judgement under Uncertainty : Heuristics and Biases (eds Kahneman, D. et al.) Ch. 14, 201–208 (Cambridge Univ. Press, 1982).

Wells, G. L. & Gavanski, I. Mental simulation of causality. J. Personal. Social Psychol. 56 , 161–169 (1989).

Taylor, S. E., Pham, L. B., Rivkin, I. D. & Armor, D. A. Harnessing the imagination: mental simulation, self-regulation and coping. Am. Psychol. 53 , 429–439 (1998).

Kaplan, F. & Oudeyer, P.-Y. in Embodied Artificial Intelligence , Lecture Notes in Computer Science Vol. 3139 (eds Iida, F. et al.) 259–270 (Springer, 2004).

Schmidhuber, J. Formal theory of creativity, fun, and intrinsic motivation. IEEE Trans. Auton. Mental Dev. 2 , 230–247 (2010).

Friston, K., Mattout, J. & Kilner, J. Action understanding and active inference. Biol. Cybern. 104 , 137–160 (2011).

Oudeyer, P.-Y. Computational theories of curiosity-driven learning. In The New Science of Curiosity (ed. Goren Gordon), 43-72 (Nova Science Publishers, 2018); https://arxiv.org/abs/1802.10546

Colombo, M. & Wright, C. First principles in the life sciences: the free-energy principle, organicism and mechanism. Synthese 198 , 3463–3488 (2021).

Article MathSciNet Google Scholar

Huang, Y. & Rao, R. P. Predictive coding. WIREs Cogn. Sci. 2 , 580–593 (2011).

Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11 , 127–138 (2010).

Knill, D. C. & Pouget, A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27 , 712–719 (2004).

Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36 , 181–204 (2013).

Clark, A. Surfing Uncertainty : Prediction , Action and the Embodied Mind (Oxford Univ. Press, 2016).

Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S. & Reyonolds, J. R. Event perception: a mind/brain perspective. Psychol. Bull. 133 , 273–293 (2007).

Eysenbach, B., Ibarz, J., Gupta, A. & Levine, S. Diversity is all you need: learning skills without a reward function. In International Conference on Learning Representations (ICLR, 2019).

Frans, K., Ho, J., Chen, X., Abbeel, P. & Schulman, J. Meta learning shared hierarchies. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=SyX0IeWAW (ICLR, 2018).

Heess, N. et al. Learning and transfer of modulated locomotor controllers. Preprint at https://arxiv.org/abs/1610.05182 (2016).

Jiang, Y., Gu, S., Murphy, K. & Finn, C. Language as an abstraction for hierarchical deep reinforcement learning. In Neural Information Processing Systems ( NeurIPS ) (eds. Wallach, H. et al.) 9414–9426 (ACM, 2019).

Li, A. C., Florensa, C., Clavera, I. & Abbeel, P. Sub-policy adaptation for hierarchical reinforcement learning. In Proc. International Conference on Learning Representations https://openreview.net/forum?id=ByeWogStDS (ICLR, 2020).

Qureshi, A. H. et al. Composing task-agnostic policies with deep reinforcement learning. In Proc. International Conference on Learning Representations https://openreview.net/forum?id=H1ezFREtwH (ICLR, 2020).

Sharma, A., Gu, S., Levine, S., Kumar, V. & Hausman, K. Dynamics-aware unsupervised discovery of skills. In Proc. International Conference on Learning Representations https://openreview.net/forum?id=HJgLZR4KvH (ICLR, 2020).

Tessler, C., Givony, S., Zahavy, T., Mankowitz, D. J. & Mannor, S. A deep hierarchical approach to lifelong learning in minecraft. In Proc. 31st AAAI Conference on Artificial Intelligence 1553–1561 (AAAI, 2017).

Vezhnevets, A. et al. Strategic attentive writer for learning macro-actions. In Neural Information Processing Systems ( NIPS ) (eds. Lee, D. et al.) 3494–3502 (NIPS, 2016).

Devin, C., Gupta, A., Darrell, T., Abbeel, P. & Levine, S. Learning modular neural network policies for multi-task and multi-robot transfer. In Proc. International Conference on Robotics and Automation ( ICRA ) (eds. Okamura, A. et al.) 2169–2176 (IEEE, 2017).

Hejna, D. J., Abbeel, P. & Pinto, L. Hierarchically decoupled morphological transfer. In Proc. International Conference on Machine Learning ( ICML ) (eds. Daumé III, H. & Singh, A.) 11409–11420 (PMLR, 2020).

Hamrick, J. B. et al. On the role of planning in model-based deep reinforcement learning. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=IrM64DGB21 (ICLR, 2021).

Sutton, R. S. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proc. 7th International Conference on Machine Learning ( ICML ) (eds. Porter, B. W. & Mooney, R. J.) 216–224 (Morgan Kaufmann, 1990).

Nau, D. et al. SHOP2: an HTN planning system. J. Artif. Intell. Res. 20 , 379–404 (2003).

Article MATH Google Scholar

Lyu, D., Yang, F., Liu, B. & Gustafson, S. SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning. In Proc. AAAI Conference on Artificial Intelligence Vol. 33, 2970–2977 (AAAI, 2019).

Ma, A., Ouimet, M. & Cortés, J. Hierarchical reinforcement learning via dynamic subspace search for multi-agent planning. Auton. Robot. 44 , 485–503 (2020).

Bacon, P.-L., Harb, J. & Precup, D. The option-critic architecture. In Proc. 31st AAAI Conference on Artificial Intelligence 1726–1734 (AAAI, 2017).

Dietterich, T. G. State abstraction in MAXQ hierarchical reinforcement learning. In Advances in Neural Information Processing Systems ( NIPS ) (eds. Solla, S. et al.) Vol. 12, 994–1000 (NIPS, 1999).

Kulkarni, T. D., Narasimhan, K. R., Saeedi, A. & Tenenbaum, J. B. Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. In Neural Information Processing Systems ( NIPS ) (eds. Lee, D. et al.) 3675–3683 (NIPS, 2016).

Shankar, T., Pinto, L., Tulsiani, S. & Gupta, A. Discovering motor programs by recomposing demonstrations. In Proc. International Conference on Learning Representations https://openreview.net/attachment?id=rkgHY0NYwr&name=original_pdf (ICLR, 2020).

Vezhnevets, A. S., Wu, Y. T., Eckstein, M., Leblond, R. & Leibo, J. Z. Options as responses: grounding behavioural hierarchies in multi-agent reinforcement learning. In Proc. International Conference on Machine Learning ( ICML ) (eds. Daumé III, H. & Singh, A.) 9733–9742 (PMLR, 2020).

Ghazanfari, B., Afghah, F. & Taylor, M. E. Sequential association rule mining for autonomously extracting hierarchical task structures in reinforcement learning. IEEE Access 8 , 11782–11799 (2020).

Levy, A., Konidaris, G., Platt, R. & Saenko, K. Learning multi-level hierarchies with hindsight. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=ryzECoAcY7 (ICLR, 2019).

Nachum, O., Gu, S., Lee, H. & Levine, S. Data-efficient hierarchical reinforcement learning. In Proc. 32nd International Conference on Neural Information Processing Systems (NIPS) (eds. Bengio, S. et al.) 3307–3317 (NIPS, 2018).

Rafati, J. & Noelle, D. C. Learning representations in model-free hierarchical reinforcement learning. In Proc. 33rd AAAI Conference on Artificial Intelligence 10009–10010 (AAAI, 2019).

Röder, F., Eppe, M., Nguyen, P. D. H. & Wermter, S. Curious hierarchical actor-critic reinforcement learning. In Proc. International Conference on Artificial Neural Networks ( ICANN ) (eds. Farkaš, I. et al.) 408–419 (Springer, 2020).

Zhang, T., Guo, S., Tan, T., Hu, X. & Chen, F. Generating adjacency-constrained subgoals in hierarchical reinforcement learning. In Neural Information Processing Systems ( NIPS ) (eds. Larochelle, H. et al.) 21579-21590 (NIPS, 2020).

Lample, G. & Chaplot, D. S. Playing FPS games with deep reinforcement learning. In Proc. 31st AAAI Conference on Artificial Intelligence 2140–2146 (AAAI, 2017).

Vezhnevets, A. S. et al. FeUdal networks for hierarchical reinforcement learning. In Proc. 34th International Conference on Machine Learning ( ICML ) (eds. Precup, D. & Teh, Y. W.) Vol. 70, 3540–3549 (PMLR, 2017).

Wulfmeier, M. et al. Compositional Transfer in Hierarchical Reinforcement Learning. In Robotics: Science and System XVI (RSS) (eds. Toussaint M. et al.) (Robotics: Science and Systems Foundation, 2020); https://arxiv.org/abs/1906.11228

Yang, Z., Merrick, K., Jin, L. & Abbass, H. A. Hierarchical deep reinforcement learning for continuous action control. IEEE Trans. Neural Netw. Learn. Syst. 29 , 5174–5184 (2018).

Toussaint, M., Allen, K. R., Smith, K. A. & Tenenbaum, J. B. Differentiable physics and stable modes for tool-use and manipulation planning. In Proc. Robotics : Science and Systems XIV ( RSS ) (eds. Kress-Gazit, H. et al.) https://ipvs.informatik.uni-stuttgart.de/mlr/papers/18-toussaint-RSS.pdf (Robotics: Science and Systems Foundation, 2018).

Akrour, R., Veiga, F., Peters, J. & Neumann, G. Regularizing reinforcement learning with state abstraction. In Proc. IEEE / RSJ International Conference on Intelligent Robots and Systems ( IROS ) 534–539 (IEEE, 2018).

Schaul, T. & Ring, M. Better generalization with forecasts. In Proc. 23rd International Joint Conference on Artificial Intelligence ( IJCAI ) (ed. Rossi, F.) 1656–1662 (AAAI, 2013).

Colas, C., Akakzia, A., Oudeyer, P.-Y., Chetouani, M. & Sigaud, O. Language-conditioned goal generation: a new approach to language grounding for RL. Preprint at https://arxiv.org/abs/2006.07043 (2020).

Blaes, S., Pogancic, M. V., Zhu, J. J. & Martius, G. Control what you can: intrinsically motivated task-planning agent. Neural Inf. Process. Syst. 32 , 12541–12552 (2019).

Haarnoja, T., Hartikainen, K., Abbeel, P. & Levine, S. Latent space policies for hierarchical reinforcement learning. In Proc. International Conference on Machine Learning ( ICML ) (eds. Dy, J. & Krause, A.) Vol. 4, 2965–2975 (PMLR, 2018).

Rasmussen, D., Voelker, A. & Eliasmith, C. A neural model of hierarchical reinforcement learning. PLoS ONE 12 , e0180234 (2017).

Riedmiller, M. et al. Learning by playing—solving sparse reward tasks from scratch. In Proc. International Conference on Machine Learning ( ICML ) (eds. Dy, J. & Krause, A.) Vol. 10, 6910–6919 (PMLR, 2018).

Yang, F., Lyu, D., Liu, B. & Gustafson, S. PEORL: integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. In Proc. 27th International Joint Conference on Artificial Intelligence ( IJCAI ) (ed. Lang, J.) 4860–4866 (IJCAI, 2018).

Machado, M. C., Bellemare, M. G. & Bowling, M. A Laplacian framework for option discovery in reinforcement learning. In Proc. International Conference on Machine Learning (ICML) (eds. Precup, D. & Teh, Y. W.) Vol. 5, 3567–3582 (PMLR, 2017).

Pathak, D., Agrawal, P., Efros, A. A. & Darrell, T. Curiosity-driven exploration by self-supervised prediction. In Proc. 34th International Conference on Machine Learning ( ICML ) (eds. Precup, D. & Teh, Y. W.) 2778–2787 (PMLR, 2017).

Schillaci, G. et al. Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces. Adaptive Behav. 29 549–566 (2020).

Colas, C., Fournier, P., Sigaud, O., Chetouani, M. & Oudeyer, P.-Y. CURIOUS: intrinsically motivated modular multi-goal reinforcement learning. In Proc. International Conference on Machine Learning ( ICML ) (eds. Chaudhuri, K. & Salakhutdinov, R.) 1331–1340 (PMLR, 2019).

Hafez, M. B., Weber, C., Kerzel, M. & Wermter, S. Improving robot dual-system motor learning with intrinsically motivated meta-control and latent-space experience imagination. Robot. Auton. Syst. 133 , 103630 (2020).

Yamamoto, K., Onishi, T. & Tsuruoka, Y. Hierarchical reinforcement learning with abductive planning. In Proc. ICML / IJCAI / AAMAS 2018 Workshop on Planning and Learning ( PAL-18 ) (2018).

Wu, B., Gupta, J. K. & Kochenderfer, M. J. Model primitive hierarchical lifelong reinforcement learning . In Proc. International Joint Conference on Autonomous Agents and Multiagent Systems ( AAMAS ) (eds. Agmon, N. et al.) Vol. 1, 34–42 (IFAAMAS, 2019).

Li, Z., Narayan, A. & Leong, T. Y. An efficient approach to model-based hierarchical reinforcement learning. In Proc. 31st AAAI Conference on Artificial Intelligence 3583–3589 (AAAI, 2017).

Hafner, D., Lillicrap, T. & Norouzi, M. Dream to control: learning behaviors by latent imagination. In Proc. International Conference on Learning Representations https://openreview.net/pdf?id=S1lOTC4tDS (ICLR, 2020).

Deisenroth, M. P., Rasmussen, C. E. & Fox, D. Learning to control a low-cost manipulator using data-efficient reinforcement learning. In Robotics : Science and Systems VII ( RSS ) (eds. Durrant-Whyte, H. et al.) 57–64 (Robotics: Science and Systems Foundation, 2011).

Ha, D. & Schmidhuber, J. Recurrent world models facilitate policy evolution. In Proc. 32nd International Conference on Neural Information Processing Systems (NeurIPS) (eds. Bengio, S. et al.) 2455–2467 (NIPS, 2018).

Battaglia, P. W. et al. Relational inductive biases, deep learning and graph networks. Preprint at https://arxiv.org/abs/1806.01261 (2018).

Andrychowicz, M. et al. Hindsight experience replay. In Proc. Neural Information Processing Systems ( NIPS ) (eds. Guyon I. et al.) 5048–5058 (NIPS, 2017); https://papers.nips.cc/paper/7090-hindsight-experience-replay.pdf

Schwartenbeck, P. et al. Computational mechanisms of curiosity and goal-directed exploration. eLife 8 , e41703 (2019).

Haarnoja, T., Zhou, A., Abbeel, P. & Levine, S. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proc. International Conference on Machine Learning ( ICML ) (eds. Dy, J. & Krause, A.) 1861–1870 (PMLR, 2018).

Yu, A. J. & Dayan, P. Uncertainty, neuromodulation and attention. Neuron 46 , 681–692 (2005).

Baldwin, D. A. & Kosie, J. E. How does the mind render streaming experience as events? Top. Cogn. Sci. 13 , 79–105 (2021).

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Acknowledgements

We acknowledge funding from the DFG (projects IDEAS, LeCAREbot, TRR169, SPP 2134, RTG 1808 and EXC 2064/1), the Humboldt Foundation and Max Planck Research School IMPRS-IS.

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Integrated problem-based learning versus lectures: a path analysis modelling of the relationships between educational context and learning approaches

Marie-paule gustin, milena abbiati, raphael bonvin, margaret w gerbase, anne baroffio.

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CONTACT Anne Baroffio [email protected] UDREM/Medical Center, 1 rue Michel Servet, 1211 Geneva 4, Switzerland

Received 2018 Feb 13; Accepted 2018 Jun 12; Collection date 2018.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Students’ approaches to learning are central to the process of learning. Previous research has revealed that influencing students’ approaches towards deep learning is a complex process and seems much more difficult than expected, even in student-activating learning environments. There is evidence that learning approaches are impacted not only by the learning environment, but also by how students perceive it. However the nature of the links between the environment itself, the way in which it is perceived by students and students’ learning approaches is poorly understood. This study aimed at investigating the relationships between students’ perception of their educational context and learning approaches in three learning environments differing by their teaching formats (lecture or problem-based-learning PBL) and integration level of the curriculum (traditional or integrated). We tested the hypothesis that a PBL format and an integrated curriculum are associated to deeper approaches to learning and that this is mediated by student perception. The study sample was constituted of 1394 medical students trained respectively in a traditional lecture-based ( n = 295), in an integrated lecture-based ( n = 612) and in an integrated PBL-based ( n = 487) curricula. They completed a survey including the Dundee-Ready-Educational-Environment-Measure (students’ perceptions of the educational environment) and the Revised-Study-Process-Questionnaire (learning approaches). Data were analysed by path analysis. The model showed that the learning environment was related to students’ learning approaches by two paths, one direct and one mediated via students’ perception of their educational context. In the lecture-based curricula students’ used deeper approaches when it was integrated and both paths were cumulative. In the PBL-based curriculum students’ did not use deeper approaches than with lectures, due to opposite effects of both paths. This study suggested that an integrated lecture-based curriculum was as effective as a PBL curriculum in promoting students’ deep learning approaches, reinforcing the importance of integrating the curriculum before choosing the teaching format.

KEYWORDS: Educational context, integrated curriculum, learning approaches, medical education, path analysis models, problem-based learning, student perception of the educational context, undergraduate medical students

Introduction

Learning approaches are central to students’ learning. The concept of learning approaches was initially proposed by Marton and Säljö [ 1 ] and later extended [ 2 – 4 ]. These authors defined that students had different intentions when starting a learning task and used different learning processes and strategies to deal with it. Students were defined as ‘deep learners’ if they tried to understand the meaning of what they were learning, related information to prior knowledge, looked for underlying principles and critically evaluated their knowledge and also the conclusions they draw. These students were driven by an intrinsic interest. Conversely, if students were reproducing content, memorizing and rote learning in order to pass the tests, they were defined as ‘surface learners’. These students were driven by a fear of failure. Students’ use of deep approaches were considered desirable since they were impacting students’ learning outcomes [ 5 ]: several authors showed that students’ use of deep approaches predicted their academic performance, and that students who passed their exams very well had used deeper approaches than those who had done less well [ 6 – 8 ].

In 1993, J. Biggs [ 5 ] proposed a model of the learning process, which postulated that learning approaches were influenced on the one hand by the characteristics of the students and on the other hand by their learning environment or educational context (‘educational context’ will be used in this paper). Thereafter, much effort has been given on fostering deep approaches to learning in order to promote deep and meaningful learning in particular through student-centred teaching methods [ 9 ]. More recently, approaches to learning were shown not to be a stable psychological trait [ 9 ] knowing that students can adopt one approach or the other depending on the educational context in which they are learning [ 4 , 10 ]. However, fostering a deep approach has revealed quite difficult [ 11 , 12 ]. In 2010, Baeten and colleagues reviewing the studies addressing the effects of educational context on students’ approaches to learning, confirmed that ‘influencing students’ approaches towards deep learning is a complex process’ and that little is known about how factors interact and relate one to the other [ 13 ]. In addition, this review highlighted an issue raised earlier namely that it was students’ perceptions of their educational context, more than the context in itself, which influenced how a student learned [ 13 , 14 ]. Thus, in addition to contextual factors such as student interactivity, group work, or assessment type, students’ perception of workload, teaching quality, or of relevance to professional practice, were some of the factors impacting students’ deep learning. The educational context such as it is perceived by students, called ‘climate’ by Genn [ 15 ], has been described as the ‘soul and spirit of the medical school’ influencing students’ behaviour. In particular, it is related to students’ satisfaction and motivation [ 16 ], but most importantly to their achievement and academic performance [ 17 , 18 ].

The perception of the educational context can change when curricula are changed [ 19 ]. The operationalization of the curriculum generates an educational context [ 15 ] that should ideally promote an autonomous, self-motivated, independent, problem-solving learner. Since the early 1990s, a major trend in medical curricula around the world has been about integrating the teaching of basic, clinical and psychosocial sciences [ 20 ]. The principle is to construct multidisciplinary system-based blocks integrating discipline-based threads [ 21 , 22 ]. By breaking down the barriers between basic and clinical sciences, integrated curricula are thought to promote knowledge retention and development of clinical skills [ 20 ]. They also aim at providing an opportunity for students to acquire a more relevant and less fragmented knowledge. In addition, influenced by the constructivist and socio-constructivist learning theories, student-centred teaching methods have been introduced [ 23 ]. One of the most studied integrated curriculums based on socio-constructivist theory is the problem-based curriculum (PBL). PBL is a student-centred teaching method, in which students discuss cases in small groups, reactivating their previous knowledge and constructing coherent explanations of the problem, in order to prepare their self-study. After this period, students meet again and share how they understood and solved the case [ 24 ]. A major strength of PBL is that it promotes integration of knowledge and skills. Students not only acquire knowledge but also practice problem-solving skills, critical thinking, small group work and autonomous learning [ 25 , 26 ]. These constructivist learning environments are thought to influence positively students’ perception of their educational context and to foster deep learning [ 13 , 27 ].

Identifying the elements of the educational context enhancing students’ perception might give cues to improve their learning experience. However, the nature of the links between the educational context itself, the way in which it is perceived by students and students’ learning approaches is poorly understood. This study aimed at investigating in a multi-institutional natural environment the relationships between students’ perception of their educational context and learning approaches in three learning environments differing by their teaching formats (lecture or problem-based-learning PBL) and integration level of the curriculum (traditional or integrated). We used path analysis modelling to test the hypothesis that a PBL format and an integrated curriculum are associated to deeper approaches to learning and that this is mediated by student perception.

Material and methods

Participants.

A total of 2734 first-to-third-year medical students were recruited during the academic years 2012–2013 to 2015–2016 in three different French-speaking medical schools (centre 1: Lyon, France, first study year, LY1; centre 2: Lausanne, Switzerland, first study year, LA1; centre 3: Geneva, Switzerland, first, second and third study years, GE1, GE2 and GE3, respectively). From the 2734 students, 1739 (64%) answered and returned the questionnaires. The return rate was about 29% in centre 1 due to the free on-line access and was between 88 and 98% in centres 2 and 3 in which students were contacted in class and answered paper questionnaires ( Table 1 ). In centre 1, the respondents were representative of the whole group in terms of age, gender, proportion of repeaters and type of high school diploma.

Description of the population, of the administration of questionnaires and of the educational context.

a see methods for details; 2 Lyon year 1; 3 Lausanne year 1; 4 Geneva year 1; 5 Geneva year 2; 6 Geneva year 3; 7 Problem-based learning; 8 Multiple-choice questions; 9 Short-answer questions; 10 Objective structured clinical exam

Description of the educational contexts

In France and the French part of Switzerland, the undergraduate curriculum is divided into a selection year (1st year of study), two preclinical years (2nd and 3rd years), two clinical years (4th and 5th years) and one elective year (6th year). A licensing exam at the end of the sixth year ends the undergraduate medical training.

The curricula at the various institutions and study years differed by their teaching formats (lecture or problem-based-learning PBL) and integration level of the curriculum (traditional or integrated), which naturally provided three different educational contexts, in which to study students’ perception of their educational context and learning approaches. We used the integration ladder framework [ 22 ] to characterize the level of integration of the curriculums. LY1was characterized by a traditional curriculum essentially based on lectures given to large classes, and based on a disciplinary approach (‘isolation to awareness’ [ 22 ]). LA1 and GE1 were characterized by a curriculum also based on lectures given to large classes, but differing from the previous situation by a teaching steered by multidisciplinary working groups in order to ensure relevance with regard to medical studies and to coordinate and integrate inside and between the modules (‘nesting’, ‘temporal coordination’, ‘sharing’, ‘correlation’ [ 22 ]). GE1 had in addition an Integration module (teaching integrative subjects such as Adaptation to effort and Inflammation, capitalizing on knowledge acquired during the previous modules) and linking cases (teaching cystic fibrosis and atherosclerosis to illustrate on-time all along the year the molecular, cellular, organ or systemic aspects taught in the modules: ‘complementary’ [ 22 ]). GE2 and GE3 were characterized by a curriculum constituted of multidisciplinary thematic teaching units taught by PBL. In each unit, students acquired knowledge from several disciplines integrated in each case, following the classical format of tutorial (case opening), self-directed study and reporting (case wrap-up and analysis of the learning process and of the group functioning) sessions (about 4 hrs tutorials and 24 hrs of self-learning per week). A few lectures and practical sessions (about 2 and 4 hrs per week, respectively), seminars of clinical skills training and community dimension (about 6 hrs per week) ran in parallel and were coordinated with problems (‘complementary’, ‘multi-disciplinary’ [ 22 ]).

Three educational contexts were defined according to the teaching format (lecture or PBL) and to the integration level of the curriculum (traditional or integrated). Thus, LY1 was defined as traditional lecture-based (educational context A), LA1 and GE1 as integrated lecture-based (educational context B), GE2 and GE3 as integrated PBL-based (educational context C) ( Table 1 ).

Data collection

Instruments.

Data used in the study derived from students’ self-reported answers to 2 instruments (supplementary tables A and B).

Learning approaches were measured with the Revised two-factor Study Process Questionnaire (R-SPQ-2F), developed by Biggs et al. [ 4 ]. The original version consisted of 20 items scored from 1 ( this item is never or only rarely true for me ) to 5 (t his item is always or almost always true for me ), with 2 major subscales: the Deep Approach (DA) and the Surface Approach (SA). The R-SPQ-2F being potentially culturally-sensitive [ 28 , 29 ], the French version presented by Gustin [ 30 ] was used after a minimal validation to ensure a reasonable application in our context. Exploratory factor analysis with two factors and geomin rotation led us to eliminate items 3 and 7 of SA. The confirmatory factor analysis model obtained with 18 items gave an acceptable fit (Root Mean Square Error of Approximation (RMSEA): 0.059; Comparative Fit Index (CFI): 0.916). Cronbach coefficients of the 18-items questionnaire were 0.78 and 0.71 for DA and new SA, respectively (see supplementary Table A for details).

Students’ perception of the educational context was measured with the Dundee Ready Educational context measure (DREEM), developed by Roff [ 31 ]. The original version consisted of 50 items scored from 0 ( strongly disagree ), to 4 ( strongly agree ) with 5 subscales. The perception of learning (12 items) related to the type of teaching (e.g., student-centred, stimulating, focused, emphasizing long-term learning, etc.). The perception of teachers (11 items) related to their attitudes, knowledge, preparation, ability to provide constructive feedback, etc. The academic self-perception (8 items) related to students’ own academic development (e.g., prepared for the profession, developing problem-solving skills, learning relevant for the future career, etc.). The perception of atmosphere (12 items) related to the general feeling of relaxed teaching activities, enjoyment outweighing stress, possibility of concentrating well, etc. The social self-perception (7 items) related to the support system for students, their social life, the interest of the matter, etc. The DREEM was considered as the most suitable instrument to measure students’ perceptions of their educational context in undergraduate medical education settings [ 32 ]. It had been used and validated in many languages and student populations, and was considered as a generic instrument to undergraduate health professions education and non-culturally sensitive [ 33 ]. The French version used for this study was a professional translation from the validated English version at the Ottawa University (courtesy of Timothy Willett). The confirmatory factor analysis model gave reasonable errors of approximation (RMSEA: 0.068; CFI: 0.81), and we decided to use it without any modification, because of the strong theoretical basis of the instrument. The respective Cronbach coefficients for the five subscales were 0.80, 0.69, 0.73, 0.78, 0.64 and 0.91 for the total scale (see supplementary Table B for details).

Administration of the survey

The survey was administered online in centre 1 and on paper in centres 2 and 3 during the second semester. In Lyon Faculty of Medicine, students received a letter from the Dean of medicine by e-mail a week before administering the questionnaires to inform them of the current study. With the agreement of the ethic comity of the Hospices Civils de Lyon , the questionnaires were proposed as free online access just after student took their mock exam. In Lausanne and Geneva Faculty of Medicine, students received an email 10 days before the survey to inform them about the research project’s main goals, the questionnaires’ content and the testing conditions (confidential, voluntary participation). All students present in the classroom on the survey day received this information once again. Students who agreed to participate signed a written consent form, as appropriate. The study was exempted from a formal review by the chair of the ethics committee of the State of Geneva (University representative).

Statistical analysis

We considered eligible for the analyses data from students who completed both questionnaires with less than 5% of missing data by dimension. As such, from the returned questionnaires, 1394 were eligible for analyses. The mean scores of DA, SA, and of the five DREEM subscales were computed for each group. Pearson’s correlations were calculated between all observed variables. The differences in scores between the three educational contexts were computed with confidence intervals (95% CI) and effect sizes (negligible if <0.30 and practically important if >0.70 [ 34 ]).

Path analysis

Path analysis is a special case of structural equation modelling which is used to examine directed relationships between a set of observed variables. We tested 2 models by path analysis. Model 1 evaluated what in students’ perception of their educational context was related to their use of deep and surface learning approaches. It tested all the relationships from each DREEM subscale to deep and surface learning approaches and from student gender and age to each DREEM subscale and learning approaches. The model assumed that the 5 DREEM subscales were inter-correlated with one another as well as DA with SA. This model 1 was tested in all participants (Model1_All) and in each of the 5 classes in turn (Model1_Clas).

Model 2 evaluated the relationships from the three educational contexts to students’ use of deep and surface learning approaches. It tested the direct and indirect (i.e., mediated via students’ perception of their educational context) relationships. The model assumed that the 5 DREEM subscales were inter-correlated with one another as well as DA with SA. This model 2 was fitted in all participants (Model2_All) and 5 times after removing each class from the whole sample in turn (sensitivity analysis, Model2_WOClas) to evaluate the robustness of the model. Only standardized path coefficients were reported.

Descriptive analyses were performed in R language (version 3.3.0) available at https://cran.r-project.org/ . Factorial and path analyses (structural equation modelling) were performed with Mplus (version 7.11) available at https://www.statmodel.com/ . The root mean square error of approximation (RMSEA) and Comparative Fit Index (CFI) were used to appreciate model fit in path analysis [ 35 , 36 ]. A RMSEA value of ≤ 0.06 and a CFI ≥ 0.90 were considered as an acceptable fit.

Descriptive statistics

Students’ perceptions of their educational context as well as their learning approaches were statistically different in the three educational contexts on all dimensions ( Table 2 ). It was better in the PBL-based integrated curriculum (C: total 136.3 ± 17.3), followed by the lecture-based integrated curriculum (B: 123.3 ± 19.8) and finally by the lecture-based traditional curriculum (A: 105.4 ± 21.3). Students used equivalent deeper and less surface approaches in both the PBL-based and lecture-based integrated curricula (33.7 ± 5.7 and 33.7 ± 6.1; 23.7 ± 5.9 and 24.7 ± 6.5 for DA and SA, respectively). In the traditional lecture-based curriculum, however, students used about as much deep (29.5 ± 5.9) than surface (27.6 ± 7.1) approaches to learning.

Descriptives of students’ perception of their educational context and of learning approaches: mean scores (SD) and change in scores of variables by educational context (95% confidence interval CI and effect sizes).

a see methods for details; 2 Lyon year 1; 3 Lausanne year 1; 4 Geneva year 1; 5 Geneva year 2; 6 Geneva year 3

Correlations between variables were low to moderate ( Table 3 ).

Correlations between variables in each educational context.

All correlations are significant at the p < 0.001 level except * ( p < 0.05)

Relationships between students’ perception of their educational context and their learning approaches

Model 1_All perfectly fitted our data (RMSEA = 0; CFI = 1; Figure 1 and Table 4 ). Students’ use of deep learning approaches was increased by their perception of good learning and academic self-perception (e.g., students increased their use of deep approaches of 0.32 SD for an increase of 1 SD in their perception of learning). Students’ use of surface approaches was decreased by their perception of good learning and marginally by their social self-perception. These effects were absent in some classes: for example, students’ perception of learning was not associated with their use of learning approaches in GE3 for DA and SA and in LY1 for SA. On the contrary, students’ social self-perception was associated to SA only in LY1. Older students used more DA and less SA, and perceived better their educational context on all dimensions. Male students had a better perception of the atmosphere, as well as of their academic and social self-perceptions. The model explained 22% and 12% of the variances of DA and SA respectively.

Path analysis model showing the relationships from students’ perception of their educational context on their learning approaches (only significant relationships are indicated).

Model 1 tested all the relationships from each DREEM subscale on deep (DA) and surface (SA) learning approaches and from gender on each DREEM subscale and on deep and surface learning approaches. The model assumed that the 5 DREEM subscales were inter-correlated with one another as well as DA with SA. Only significant relationships with their beta coefficients are represented on the figure. Full lines are positive relationships and dotted lines negative relationships.

Standardized coefficients of the paths from each dimension of students’ perception of their educational context on their learning approaches (all models 1).

Significant standardized coefficients are in bold; *** p ≤ 0.001; ** 0.001 < p ≤ 0.01; * 0.01 < p ≤ 0.05

Model 1.All was fitted in all pooled classes and models 1.Clas separately in each class

Direct and mediated relationships from each educational context to students’ learning approaches

Model 2_All perfectly fitted our data (RMSEA = 0; CFI = 1; Figure 2 and Table 5 ) hence supporting the hypothesis that students’ learning approaches were related to the educational context. In addition it showed that this relationship was constituted of 2 paths, one direct from the educational context to the learning approaches, and one indirect mediated by students’ perception of this educational context.

Path analysis model showing the relationships from the integration level and the teaching format of the curriculum on students’ perception of their educational context and on their learning approaches (only significant relationships are indicated).

Model 2 tested the relationships from the integration level and the teaching format of the curriculum on students’ use of deep (DA) and surface (SA) learning approaches, postulating a direct path and an indirect path mediated via students’ perception of their educational context. The model assumed that the 5 DREEM subscales were inter-correlated with one another as well as DA with SA. Only significant relationships between integration level, teaching format, students’ perception and students’ learning approaches are represented with their beta coefficients on the figure. Full lines are positive relationships and dotted lines negative relationships.NB: The relationships between gender, age and the other variables are not represented for a matter of clarity but can be found in Table 5 . All coefficients are adjusted for gender and age.

Standardized coefficients of the paths from the integration level of the educational context and from each dimension of students’ perception of these educational contexts on their learning approach (all models 2).

Model 2. All was fitted in all pooled classes and models 2.WoClas after removing each class from the whole sample. Grey areas indicate that the effect of partial vs. no integration could not be estimated since the only class with no-integration (i.e., LY1) was removed from the whole sample

Direct path : In the lecture-based curricula, integration increased the use of deep approaches, whereas in PBL compared to lectures (in an integrated curriculum) the use of deep approaches decreased. Both had no effect on surface approaches. As in Model 1, older students used deeper approaches. The sensitivity analysis (model 2_WoClas) confirmed these findings.

Indirect path : Integration (integrated compared to traditional curriculum) and PBL (compared to lectures) improved students’ perception of all dimensions. Integration had more effect on students’ perception of learning, of teacher and of social self-perception, whereas PBL particularly improved students’ academic and atmosphere perceptions. This improved students’ perception (learning and academic) influenced in turn students’ use of deep and surface approaches (like in Model 1). In each of the three educational contexts, older students were less positive in their perceptions than younger students (except for academic perception), and this in turn also influenced their use of deep and surface approaches.

The model explained 25% and 12% of the variances of DA and SA, respectively.

Table 6 summarized the direct and indirect paths from the educational context to the deep and surface learning approaches. Compared to a traditional lecture-based curriculum, an integrated lecture-based curriculum increased deep (0.26) and decreased surface (−0.18) approaches, cumulating the effects of the direct and indirect paths. The PBL-based integrated curriculum had no additional effect (−0.03) on students’ use of deep learning approaches, compared to an integrated lecture-based curriculum: it combined two opposite effects, a direct negative (−0.18) and an indirect positive (0.15) effects. In addition, it had a small negative effect (−0.08) on students’ use of surface approaches, through the indirect path. Age, also combining opposite direct and indirect effects, had finally no effect on students’ use of surface approaches and a small positive effect (0.07) on students’ use of deep approaches. Gender had no significant effect on these relationships.

Summary of the direct and indirect paths from the integration level of the curriculum, from the teaching format and from gender to the deep and surface learning approaches.

Standardized coefficients (range of the sensitivity analysis); § not significant at 5% level

This study aimed at investigating by path analysis the relationships between students’ perception of their educational context and their learning approaches in three learning environments differing by their teaching formats (lecture or PBL) and integration level of the curriculum (traditional or integrated). We found in this specific setting that an integrated lecture-based curriculum seemed as effective in stimulating students’ use of deep learning approaches as a PBL-based curriculum. In addition, the analysis suggested that the educational context is related to learning approaches by two paths, one direct and one mediated by student perception, providing some new insights into the complex interplay between the educational context and students’ learning approaches. Indeed our analysis suggested that both paths had cumulative effects in the case of the integrated lecture-based curriculum (compared to the traditional), whereas they had opposite effects in the case of the PBL-based curriculum (compared to the integrated lecture based).

Findings of this study show that, compared to a traditional curriculum, an integrated curriculum, even if based on lecturing, improved students’ perception of their educational context on all dimensions (learning, teacher, general atmosphere, academic and social self-perceptions). Furthermore, when taught by PBL it increased even more the positive way students perceived their educational context. This is different from a previous study in which lecture-taught students’ perceptions were positive, but perception of activating methods was very variable with extremely positive and negative opinions [ 37 ]. An analysis of the differences between the three curricula highlighted the following points. In the lecture-based traditional curriculum, teaching was not coordinated, teaching methods were focused on transmission and promoted rote learning, and a severe selection pressured students and induced a climate of fear of failure. In the lecture-based integrated curriculum, modules were constructed by multidisciplinary teams, learning objectives were coordinated around different disciplines, red threads were emphasized inside and throughout the modules (e.g., by the linking cases), and lectures were contextualized. We pondered that this context, although similarly competitive, allowed students to give meaning to what was taught, to form relationships among concepts and to connect their learning to future needs [ 13 , 21 , 22 ]. In the PBL-based curriculum, perception was even better because of the teaching organized around realistic clinical cases and students being active and cooperative learners. This could improve their perception of learning, of feeling confident in their academic success, and of being prepared to the exercise of their profession [ 13 , 25 , 27 , 38 ]. This could also enhance their social self-perception through repeated practice of group work [ 39 ].

Our path analysis models were consistent with published research, showing that students tended to adopt one approach or the other depending on the context in which they were learning [ 4 , 5 , 10 , 15 ] and that not only the context itself but also how it is perceived by students was influencing their approaches to learning [ 13 , 14 ]. With regards to the model proposed by J. Biggs [ 5 ], we found that the way students perceived their teachers seemed to have little or no influence on their learning approaches. By contrast, their perception of the type of teaching (e.g., student-centred, stimulating, focused, emphasizing long-term learning) as well as of their own academic development (e.g., prepared for the profession, developing problem-solving skills, learning relevant for the future career) did impact the way they learned, stimulating the use of deep and decreasing the use of surface approaches.

Like previous studies, we observed that a traditional not integrated subject-based curriculum increased surface approaches [ 40 , 41 ]. In addition, the conditions of strong selection encountered by first-year students could be experienced as a ‘survival course’ and induce students to adopt surface strategies suited to the learning situation [ 42 ]. Interestingly, however, we noted that in similarly competitive contexts, the Swiss first-year students perceived their context as better and adopted less surface approaches than the French first-year students. We hypothesize that a potentially different social climate among the three institutions, aided by an integrated curriculum including clear objectives, defined assessment criteria and relevant teaching content, allowed to optimize and align the curriculum, thus eventually favouring deep learning [ 43 – 45 ]. When it came to compare the lecture-based to the PBL-based integrated curricula, the model suggested that the PBL format on one hand improved students’ perception of their educational context which in turn stimulated them to use deeper and less surface learning approaches, but on the other hand directly decreased students’ use of deep approaches, with no effect on surface approaches. The finding that PBL might have two opposite effects on learning approaches could provide cues to understand some of the controversial findings about the effect of PBL on learning approaches. Indeed in a recent review, Dolmans and colleagues [ 46 ] found that PBL had an overall small positive effect on the deep approach and no effect on the surface approach. However, looking at individual studies effect sizes, it was striking to see that they ranged from −0.53 to 0.93 for the deep approach, and from −0.50 to 0.50 for the surface approach. The density of the curriculum, unclear goals, perception of cognitive load, insufficiently aligned assessment and workload were some of the factors that have been shown to counter-influence the desired outcome of PBL and other constructivist learning environments on stimulation of deep approach [ 12 , 44 , 47 – 49 ]. In this line, a previous study suggested that the practice of PBL may evolve along a PBL-based curriculum and move away from the original model [ 50 ]. A previous qualitative analysis of our students’ comments revealed that the perceived workload, the insufficiently aligned assessment and the absence of a counterbalanced role of teachers were the factors driving the learners away from the deep learning planned with PBL (unpublished results). In addition, the complexity and time-consuming learning activities required in PBL sessions [ 48 ] and the demands of the deep learning approach over the surface approach could induce students to revert over the years to a more expedient, superficial and hence less demanding approach to learning [ 9 , 11 , 12 ].

The present study highlighted the complexity and difficulty to influence students’ approaches towards deep learning [ 13 ]. Our models suggested that about 25% of the variance of students’ deep approaches might be explained by the impact of their educational context and their perception. The other factors belong to the personal and institutional fields. The institutional culture can influence both the educational context itself and its perception by students [ 51 ]. However, the impact of institutional factors is about four times smaller than that of individual factors on the perceptions of the educational context [ 52 ]. Yet, perceptions being defined through the learner’s lens, this makes the educational context fairly complex to be defined and understood [ 52 ]. One of the most influencing factors seems to be students’ initial learning approach [ 53 , 54 ], that could be driven by their epistemological beliefs [ 55 ]. It impacts how students approach learning, whatever the characteristics of the course are, and seem to account for about 30% of the variance in students’ subsequent use of learning approaches [ 12 ]. Moreover, the stronger the initial learning approaches, the less they may subsequently change according to the educational contexts (Gijbels 2008), since they influence the appreciation and use of elements of the educational context, reducing the impact of instructional measures [ 54 ]. For example, deep learners preferring educational contexts favouring understanding were more positive in their perception, seemed more suited to PBL than surface learners [ 56 ] and did not change their approach between a conventional and a PBL curriculum [ 12 , 54 ]. Conversely, surface learners adapted their learning approaches in action learning (interactive lectures and group work) and utilized deeper strategies than in a conventional curriculum [ 54 ]. This suggests that future studies should include personal characteristics of students, in particular their initial learning profile that might influence their individual evolution of learning approaches during their study years.

Strengths of this study are first its multi-institutional and international design. Having similar findings at three medical schools from two countries and three different study years, could provide the representativeness to support the generalizability of the findings concerning the impact of students’ perception of the educational context on learning approaches. Second, the use of path analysis to test and confirm our hypotheses allowed for the disentangling between direct and mediated effects of the educational context on learning approaches.

Limitations of this study concern first the use of translated scales for DREEM and R-SPQ-2F, which could impact some psychometric properties of the instruments. Second, the use of self-administered questionnaires which have been claimed inaccurate and potentially limiting the interpretation of the results; yet, self-perception may still influence how students learn. Third, the lower return rate from the class LY1 could limit the representability of the lecture-based not integrated group. However we have showed evidence that its socio-demography was representative of the whole population, and have no reason to think that the learning approaches adopted by these students would be different. In addition, the sensitivity analysis ensured that it did not much influence the results. A fourth limitation concerns all the factors that might influence learning approaches and were not considered in this study, such as student and teacher factors [ 13 ], and institution-specific influences [ 57 ].

In conclusion, our study suggests that a carefully integrated lecture-based curriculum was as effective as a PBL-based curriculum in stimulating students to adopt deep approaches to learning. This does not mean that PBL must be challenged, since there is sufficient evidence to prove that PBL is a very effective teaching format [ 23 , 58 , 59 ]. However, the way PBL is practiced should be questioned, since its effectiveness highly depends on its implementation [ 60 ]. Thus, curriculum design could benefit of putting its forces first in multidisciplinary teams developing a student-centred, integrated, multidisciplinary program, before choosing the teaching format itself, in order to create favourable conditions for deep learning [ 61 ].

Funding Statement

This work was supported by the University of Geneva (CH) Faculty of Medicine; Swiss Federal Office of Public Health [Grant number REF-13.005595, 14.001567, 15.029074];

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary Material

Supplemental data for this article can be accessed here .

[1]. Marton F, Säljö R.. On qualitative differences in learning: I-Outcome and process British. J Educ Psychol. 1976;46(1):4–12. [ Google Scholar ]
[2]. Trigwell K, Prosser M. Development and Use of the Approaches to Teaching Inventory [journal article]. Educ Psychol Rev. 2004;16(4):409–424. [ Google Scholar ]
[3]. Biggs JB. The role of metalearning in study process. Br J Educ Psychol. 1985;55(3):185–212. [ Google Scholar ]
[4]. Biggs JB, Kember D, Leung DYP. The revised two factor study process questionnaire: R-SPQ-2F. Br Educ Psychol. 2001;71:133–149. [ DOI ] [ PubMed ] [ Google Scholar ]
[5]. Biggs JB. From Theory to Practice: A Cognitive Systems Approach. Higher Educ Res Dev. 1993January;12(1):73–85. . [ Google Scholar ]
[6]. Feeley AM, Biggerstaff DL. exam success at undergraduate and graduate-entry medical schools: is learning style or learning approach more important? a critical review exploring links between academic success, learning styles, and learning approaches among school-leaver entry (“traditional”) and graduate-entry (“nontraditional”) medical students. Teach Learn Med. 2015Jul-Sep;27(3):237–244. . PubMed PMID: 26158325; eng [ DOI ] [ PubMed ] [ Google Scholar ]
[7]. May W, Chung E-K, Elliott D, et al. The relationship between medical students’ learning approaches and performance on a summative high-stakes clinical performance examination. Med Teach. 2012;34(4):e236–e241. [ DOI ] [ PubMed ] [ Google Scholar ]
[8]. Salamonson Y, Weaver R, Chang S, et al. Learning approaches as predictors of academic performance in first year health and science students. Nurse Education Today. 2013July;33(7):729–733. . [ DOI ] [ PubMed ] [ Google Scholar ]
[9]. Struyven K, Dochy F, Janssens S, et al. On the dynamics of students’ approaches to learning: the effects of the teaching/learning environment. Learn Instruction. 2006;16(4):279–294. [ Google Scholar ]
[10]. Entwistle NJ, Peterson ER. Conceptions of learning and knowledge in higher education: relationships with study behaviour and influences of learning environments. Int J Educ Res. 2004;41(6):407–428. [ Google Scholar ]
[11]. Marton F, Säljö R. Approaches to learning In: DJHaNJEE, Marton F, editor. The experience of learning. Implications for teaching and studying in higher education. Edinburgh: Scottish Academic Press; 1997. [ Google Scholar ]
[12]. Gijbels D, Segers M, Struyf E. Constructivist learning environments and the (im)possibility to change students’ perceptions of assessment demands and approaches to learning. Instr Sci. 2008;36(5):431–443. [ Google Scholar ]
[13]. Baeten M, Kyndt E, Struyven K, et al. Using student-centred learning environments to stimulate deep approaches to learning: factors encouraging or discouraging their effectiveness. Educ Res Rev. 2010;5(3):243–260. [ Google Scholar ]
[14]. Entwistle NJ. Approaches to learning and perceptions of the learning environment: introduction to the special issue. High Educ. 1991;22(3):201–204. [ Google Scholar ]
[15]. Genn JM. AMEE medical education guide no. 23 (part 1): curriculum, environment, climate, quality and change in medical education-a unifying perspective. Med Teach. 2001aJuly;23(4):337–344. . PubMed PMID: 12098379; eng [ DOI ] [ PubMed ] [ Google Scholar ]
[16]. Müller FH, Louw J. Learning environment, motivation and interest: perspectives on self-determination theory. South Afr J Psychol. 2004June1;34(2):169–190. . [ Google Scholar ]
[17]. Wayne SJ, Fortner SA, Kitzes JA, et al. Cause or effect? The relationship between student perception of the medical school learning environment and academic performance on USMLE Step 1. Med Teach. 2013;35(5):376–380. .PubMed PMID: 23444883 [ DOI ] [ PubMed ] [ Google Scholar ]
[18]. Fraser BJ. Science learning environments: assessment, effects and determinants In: BJFKGT , editor. International handbook of science education. Great Britain: Kluwer; 1998. p. 527–561. [ Google Scholar ]
[19]. Genn JM. AMEE medical education guide no. 23 (part 2): curriculum, environment, climate, quality and change in medical education - a unifying perspective. Med Teach. 2001b;23(5):445–454. . PubMed PMID: 12098364; eng [ DOI ] [ PubMed ] [ Google Scholar ]
[20]. Brauer DG, Ferguson KJ. The integrated curriculum in medical education: AMEE guide no. 96. Med Teach. 2015;37(4):312–322. . PubMed PMID: 25319403 [ DOI ] [ PubMed ] [ Google Scholar ]
[21]. Wilkerson L, Stevens CM, Krasne S. No content without context: integrating basic, clinical, and social sciences in a pre-clerkship curriculum. Med Teach. 2009;31(9):812–821. [ DOI ] [ PubMed ] [ Google Scholar ]
[22]. Harden RM. The integration ladder: a tool for curriculum planning and evaluation. Med Educ. 2000July;34(7):551–557. PubMed PMID: 10886638; eng [ DOI ] [ PubMed ] [ Google Scholar ]
[23]. Dochy F, Segers M, Van Den Bossche P, et al. Effects of problem-based learning: a meta-analysis. Learn Instruction. 2003;13(5):533–568. [ Google Scholar ]
[24]. Norman GR, Schmidt HG. The psychological basis of problem-based learning: a review of the evidence. Acad Med. 1992September;67(9):557–565. PubMed PMID: 1520409; Eng [ DOI ] [ PubMed ] [ Google Scholar ]
[25]. Schmidt HG, Rotgans JI, Yew EHJ. The process of problem-based learning: what works and why. Med Educ. 2011;45(8):792–806. [ DOI ] [ PubMed ] [ Google Scholar ]
[26]. Hmelo-Silver CE. Problem-based learning: what and how do students learn? [journal article]. Educ Psychol Rev. 2004;16(3):235–266. [ Google Scholar ]
[27]. Dart BC, Burnett PC, Purdie N, et al. Students’ conceptions of learning, the classroom environment, and approaches to learning. J Educ Res. 2000;93(4):262–270. [ Google Scholar ]
[28]. Stes A, De Maeyer S, Van Petegem P. Examining the cross-cultural sensitivity of the revised two-factor study process questionnaire (r-spq-2f) and validation of a Dutch version. PLoS One. 2013;8(1):e54099 . PubMed PMID: 23342085; PubMed Central PMCID: PMC3546932. eng [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
[29]. Immekus JC, Imbrie PK, Test A. Cross-validation of the revised two-factor study process questionnaire factor structure among western university students. Educ Psychol Meas. 2010June1;70(3):495–510. . [ Google Scholar ]
[30]. Gustin M-P, Vinciguerra C, Isaac S, et al. Approches d’apprentissage et réussite en première année commune des études de santé (PACES) en France. Pédagogie Médicale. 2016;17(1):23–43. [ Google Scholar ]
[31]. Roff S, McAleer S, Harden RM, et al. Development and validation of the Dundee Ready Education Environment Measure (DREEM). Med Teach. 1997;19(4):295–299. [ Google Scholar ]
[32]. Soemantri D, Herrera C, Riquelme A. Measuring the educational environment in health professions studies: A systematic review. Med Teach. 2010;32:947–952. [ DOI ] [ PubMed ] [ Google Scholar ]
[33]. Roff S. The Dundee Ready Educational Environment Measure (DREEM)–a generic instrument for measuring students’ perceptions of undergraduate health professions curricula. Med Teach. 2005June;27(4):322–325. . PubMed PMID: 16024414; Eng [ DOI ] [ PubMed ] [ Google Scholar ]
[34]. Hojat M, LaNoue M. Exploration and confirmation of the latent variable structure of the Jefferson scale of empathy. Int J Med Educ. 2014;5:73–81. . PubMed PMID: 25341215; PubMed Central PMCID: PMCPmc4207175. eng [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
[35]. Maccallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling. Psychol Methods. 1996;1(2): 130–149. citeulike-article-id:1538286. [ Google Scholar ]
[36]. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus alternative. Structural Equation Model. 1999;6 DOI: 10.1080/10705519909540118 [ DOI ] [ Google Scholar ]
[37]. Struyven K, Dochy F, Janssens S. Students’ likes and dislikes regarding student-activating and lecture-based educational settings: consequences for students’ perceptions of the learning environment, student learning and performance. Eur J Psychol Educ. 2008September1;23(3):295–317. . English. [ Google Scholar ]
[38]. The Dundee RS. Ready Educational Environment Measure (DREEM)–a generic instrument for measuring students’ perceptions of undergraduate health professions curricula. Med Teach. 2005;27 DOI: 10.1080/01421590500151054 [ DOI ] [ PubMed ] [ Google Scholar ]
[39]. McLean M, Van Wyk JM, Peters-Futre EM, et al. The small group in problem-based learning: more than a cognitive ‘learning’ experience for first-year medical students in a diverse population. Med Teach. 2006;28(4):e94–e103. [ DOI ] [ PubMed ] [ Google Scholar ]
[40]. Coles CR. Differences between conventional and problem-based curricula in their students’ approaches to studying. Med Educ. 1985;19(4):308–309. [ DOI ] [ PubMed ] [ Google Scholar ]
[41]. Hilliard RI. How do medical students learn: medical student learning styles and factors that affect these learning styles. Teach Learn Med. 1995January1;7(4):201–210. . [ Google Scholar ]
[42]. Zeegers P. Approaches to learning in science: a longitudinal study. Br J Educ Psychol. 2001March;71(Pt 1):115–132. PubMed PMID: 11307704; eng [ DOI ] [ PubMed ] [ Google Scholar ]
[43]. Karagiannopoulou E, Christodoulides P. The impact of Greek University students’ perceptions of their learning environment on approaches to studying and academic outcomes. Int J Educ Res. 2005;43(6):329–350. [ Google Scholar ]
[44]. Schumacher DJ, Englander R, Carraccio C. Developing the master learner: applying learning theory to the learner, the teacher, and the learning environment. Acad Med. 2013;88(11):1635–1645. [ DOI ] [ PubMed ] [ Google Scholar ]
[45]. Trigwell K, Prosser M. improving the quality of student learning: the influence of learning context and student approaches to learning on learning outcomes. High Educ. 1991;22(3):251–266. [ Google Scholar ]
[46]. Dolmans DH, Loyens SM, Marcq H, et al. Deep and surface learning in problem-based learning: a review of the literature. Adv Health Sci Educ Theory Pract. 2016November;13(21):1087–1112. . PubMed PMID: 26563722; Eng. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
[47]. Nijhuis JFH, Segers MSR, Gijselaers WH. Influence of redesigning a learning environment on student perceptions and learning strategies [journal article]. Learn Environments Res. 2005;8(1):67–93. [ Google Scholar ]
[48]. Groves M. Problem-based learning and learning approach: is there a relationship? Adv Health Sci Educ. 2005;10(4):315–326. [ DOI ] [ PubMed ] [ Google Scholar ]
[49]. Papinczak T, Young L, Groves M, et al. Effects of a metacognitive intervention on students’ approaches to learning and self-efficacy in a first year medical course. Adv Health Sci Educ Theory Pract. 2008May;13(2):213–232. PubMed PMID: 17120079; eng. [ DOI ] [ PubMed ] [ Google Scholar ]
[50]. Baroffio A, Vu N, Gerbase M. Evolutionary trends of problem-based learning practices throughout a two-year preclinical program: a comparison of students’ and teachers’ perceptions. Adv Health Sci Educ. 2013October1;18(4):673–685. . English. [ DOI ] [ PubMed ] [ Google Scholar ]
[51]. Skochelak SE, Stansfield RB, Dunham L, et al. Medical student perceptions of the learning environment at the end of the first year: a 28-medical school collaborative. Acad Med. 2016March8;91(9):1257–1262. PubMed PMID: 26959222; Eng. [ DOI ] [ PubMed ] [ Google Scholar ]
[52]. Gruppen LD, Stansfield RB. Individual and institutional components of the medical school educational environment. Acad Med. 2016;91(11):S53–S57. . PubMed PMID: 00001888-201611001-00019 [ DOI ] [ PubMed ] [ Google Scholar ]
[53]. Wilson K, Fowler J. Assessing the impact of learning environments on students’ approaches to learning: comparing conventional and action learning designs. Assess Eval Higher Educ. 2005February;30(1):87–101. . [ Google Scholar ]
[54]. Vermetten YJ, Vermunt JD, Lodewijks HG. Powerful learning environments? How university students differ in their response to instructional measures. Learn Instruction. 2002June;12(3):263–284. . [ Google Scholar ]
[55]. Ozkal K, Tekkaya C, Cakiroglu J, et al. A conceptual model of relationships among constructivist learning environment perceptions, epistemological beliefs, and learning approaches. Learn Individ Differ. 2009January;19(1):71–79. . [ Google Scholar ]
[56]. Papinczak T. Are deep strategic learners better suited to PBL? A preliminary study. Adv Health Sci Educ. 2009;14(3):337–353. [ DOI ] [ PubMed ] [ Google Scholar ]
[57]. Cashin WE, Downey RG. Disciplinary differences in what is taught and in students’ perceptions of what they learn and of how they are taught. New Dir Teaching Learn. 1995;1995(64):81–92. [ Google Scholar ]
[58]. Schmidt HG, Van Der Molen HT, Te Winkel WWR, et al. Constructivist, problem-based learning does work: a meta-analysis of curricular comparisons involving a single medical school. Educ Psychol. 2009October28;44(4):227–249. . [ Google Scholar ]
[59]. Strobel J, Van Barneveld A. When is PBL more effective? a meta-synthesis of meta-analyses comparing pbl to conventional classrooms. Interdiscip J Problem-Based Learn. 2009;3(1):44–58. [ Google Scholar ]
[60]. Dolmans DH, Wolfhagen IH, Van Der Vleuten CP, et al. Solving problems with group work in problem-based learning: hold on to the philosophy. Med Educ. 2001September;35(9):884–889. PubMed PMID: 11555227; eng [ DOI ] [ PubMed ] [ Google Scholar ]
[61]. Baeten M, Struyven K, Dochy F. Student-centred teaching methods: can they optimise students’ approaches to learning in professional higher education? Stud Educ Eval. 2013March;39(1):14–22. . [ Google Scholar ]

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