Epistemic gameplay and discovery in computational model-based inquiry activities

Wilkerson, Michelle Hoda; Shareff, Rebecca; Laina, Vasiliki; Gravel, Brian E.

doi:10.1007/s11251-017-9430-4

Cited by 21 publications

(47 citation statements)

References 38 publications

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“…My conclusion is that they have managed to lift it up together. What they show, some more explicitly than others, is that there can indeed be an integration of repeated guidance by a teacher and discovery by a student (Trninic 2018); that careful technology design and teaching can cover up what students should not (yet) see so that they can dis-cover particular insights that are not easily ''told'' (Chase and Abrahamson 2018;Levy et al 2018;Wilkerson et al 2018).…”

Section: Resultsmentioning

confidence: 98%

“…For example, Wilkerson et al (2018) convincingly argue that there should be alignment between on the one hand the epistemic games (modeling strategies) that designers and teachers invite students to engage in and on the other hand the epistemic forms (model types) that the modeling environment is designed to support. They show in their design-based research how they supported students by means of curricular activities with computer tools in playing the intended epistemic game.…”

Section: The Contributions To the Special Issuementioning

confidence: 99%

“…When doing design research, such as some authors in this special issue (Chase and Abrahamson 2018;Levy et al 2018;Wilkerson et al 2018), one loses methodological control but typically gains ecological validity (Brown 1992). When really trying to improve practice, and figuring out how the intricate interplay between guidance and discovery works, one is likely to face many aspects of the elephant I identified, notably the quality of the intervention, the detailed considerations of what to cover up and what to highlight in the design (with or without technology), the unique characteristics of students you work with, et cetera.…”

Section: Design Researchmentioning

confidence: 99%

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Discovery learning: zombie, phoenix, or elephant?

Bakker

2018

Instr Sci

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Discovery learning continues to be a topic of heated debate. It has been called a zombie, and this special issue raises the question whether it may be a phoenix arising from the ashes to which the topic was burnt. However, in this commentary I propose it is more like an elephant-a huge topic approached by many people who address different aspects. What is needed in the discussion about discovery learning and related approaches, I argue, is sublation: the kind of lifting up from the one-dimensional discussion between two extremes (minimal guidance vs. instruction) that puts an end to the everlasting tug of war by integrating justified concerns from both opposite positions. I evaluate how the different contributions to the special issue help to sublate the discussion about discovery learning to a higher level. In particular, the case study presented by Trninic illustrates how strong guidance and repetition may be needed for the discovery of something that cannot be told. I further suggest scaffolding, inferentialism, and design research as potential theoretical and methodological ways forward.

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Section: Resultsmentioning

confidence: 98%

Section: The Contributions To the Special Issuementioning

confidence: 99%

Section: Design Researchmentioning

confidence: 99%

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Discovery learning: zombie, phoenix, or elephant?

Bakker

2018

Instr Sci

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“…However, scholarship on how learners use mechanistic reasoning is sparse. Notably, Wilkerson, Shareff, Laina, and Gravel (2018) found that students who focused on entities' movements and interactions developed progressively better mechanistic, explanatory models. Dickes, Sengupta, Farris, and Basu (2016) demonstrated that applying the lens of mechanistic reasoning to student explanations of agent-based models can trace students' conceptual development of interdependence in ecology.…”

Section: Mechanistic Reasoning Frameworkmentioning

confidence: 99%

Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning

Hsiao¹,

Lee²,

Klopfer³

2019

Brit J Educational Tech

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Computer modeling promotes mechanistic reasoning when learners build and analyze models of complex systems to explore causal mechanisms and use models to generate patterns. StarLogo Nova, an agent-based modeling environment, enables novice programmers to model a system's individual components and investigate its emergent, collective behavior. Through case analysis of teachers using StarLogo Nova, we demonstrate how agent-based modeling advances thinking about mechanisms generating phenomenon. Teachers who used simulation combined with the decoding of StarLogo Nova models utilized mechanistic reasoning to make sense of how and why complex phenomenon emerged.

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“…Looking critically at discovery-based learning environments, Wilkerson et al (2018) take on the pedagogical problem of these environments potentially bearing differential effect across student cohorts. In particular, the authors analyze implicit challenges in some 5th-grade students' attempts to participate productively in modeling-based science inquiry activities that the authors developed and implemented.…”

Section: Overview Of Contributions To the Special Issuementioning

confidence: 99%

Reinventing discovery learning: a field-wide research program

Abrahamson

Kapur

2017

Instr Sci

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Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective ''video games,'' and intercorporeal master-student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts.

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Epistemic gameplay and discovery in computational model-based inquiry activities

Cited by 21 publications

References 38 publications

Discovery learning: zombie, phoenix, or elephant?

Discovery learning: zombie, phoenix, or elephant?

Making sense of models: How teachers use agent‐based modeling to advance mechanistic reasoning

Reinventing discovery learning: a field-wide research program

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