Embodying scientific concepts in the physical space of the classroom

Malcolm, Peter; Moher, Tom; Bhatt, Darshan; Uphoff, Brian; Lopez-Silva, Brenda

doi:10.1145/1463689.1463761

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Previous work with Embedded Phenomena simulations has shown that they can support development of domain understandings and authentic scientific practice, facilitate positive attitudes towards science and conducting experiments, and increase student agency in finding things out through experimentation rather than from a teacher (Malcolm, Moher, Bhatt, Uphoff, & López Silva, 2008; Moher, 2008; Moher et al, 2008; Novellis & Moher, 2011). The current research extends prior findings by showing that Embedded Phenomena activities may differentially improve learning and attenuate the effects of individual differences in spatial skills on learning in science, relative to non-embedded activities.…”

Section: Discussionmentioning

confidence: 99%

Leveling the playing field: Grounding learning with embedded simulations in geoscience

2016

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Although desktop simulations can be useful in representing scientific phenomena during inquiry activities, they do not allow students to embody or contextualize the spatial aspects of those phenomena. One learning technology that does attempt to combine embodiment and grounded experience to support learning in science is Embedded Phenomena. The objective of this research was to investigate the effectiveness of a classroom-based Embedded Phenomena activity for learning in geoscience, and to investigate whether individual differences in spatial skills had an impact on the effectiveness. The simulated scientific phenomenon was earthquakes, and 44 fifth grade (10-year old) students learned from a unit containing both content instruction and simulations. In the embedded condition, 15 earthquake events were simulated within the classroom space and students enacted the computation of epicenters with strings and their bodies. Students in the non-embedded condition received the same content instruction and did the same activities, but the epicenter computations were done with maps instead of with students’ bodies. Students in the embedded condition showed greater learning gains overall. Further, the Embedded Phenomena activity attenuated the effect of individual differences in spatial skills on learning in science such that low spatial individuals performed as well as high spatial individuals in the embedded condition.

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Section: Discussionmentioning

confidence: 99%

Leveling the playing field: Grounding learning with embedded simulations in geoscience

2016

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“…The output is the classroom: the output is no longer a simple display but a range of ways to change the physical learning space. This is illustrated by projects such as WallCology (Malcolm et al 2008) and smart classrooms (Slotta et al 2013). In other words, while AIED initially aimed at modelling the contents and the learner, a challenge for the future of AIED is to model educational spaces, i.e.…”

Section: Trend 1: More Physicalmentioning

confidence: 99%

The Evolution of Research on Digital Education

Dillenbourg

2016

Int J Artif Intell Educ

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How does AI&EdAIED today compare to 25 years ago? This paper addresses this evolution by identifying six trends. The trends are ongoing and will influence learning technologies going forward. First, the physicality of interactions and the physical space of the learner became genuine components of digital education. The frontier between the digital and the physical has faded out. Similarly, the opposition between individual and social views on cognition has been subsumed by integrated learning scenarios, which means that AIED pays more attention today to social interactions than it did at its outset. Another trend is the processing of learners' behavioural particles, which do not carry very many semantics when considered individually, but are predictive of knowledge states when large data sets are processed with machine learning methods. The development of probabilistic models and the integration of crowdsourcing methods has produced another trend: the design of learning environments has become less deterministic than before. The notion of learning environment evolved from a rather closed box to an open ecosystem in which multiple components are distributed over multiple platforms and where multiple stakeholders interact. Among these stakeholders, it is important to notice that teachers play a more important role than before: they interact not only at the design phase (authoring) but also in the runtime phase (orchestration). These trends are not specific to AIED; they depict the evolution of learning technologies as a whole.

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“…To this end, educational researchers have responded by creating learning environments that allow learners to work together as they are immersed in complex problems. The increasing use of mixed-reality experiences (e.g., Moher, 2008;Malcolm et al, 2008;Dunleavy et al, 2009;Peppler et al, 2010;Tscholl et al, 2013;Slotta et al, 2013;Enyedy et al, 2015;Planey & Lindgren, 2018;Mallavarapu et al, 2019) situates learners in a collaborative experience in the real world with face-to-face interactions.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Utility of Social-Network-Derived Collaborative Opportunity Temperature Readings for Informing Design and Research of Large-Group Immersive Learning Environments

2022

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Large-group (n > 8) co-located collaboration has not been adequately studied because it demands different conceptual framings than those used to study small-group collaboration, while also posing pragmatic constraints on data collection. Working within these pragmatic constraints, we use video data to devise an indicator of the current possibilities for learner collaboration during large-group co-located interactions. We borrow conceptualizations from proxemics and social network analysis to construct collaborative opportunity networks, allowing us to define the concept of collaborative opportunity temperature (COT) readings: a “snapshot” of the current configuration of the different social subgroup structures within a large group, indicating emergent opportunities for collaboration (via talk or shared action) due to proximity. Using a case study of two groups of people (n = 11, n = 12) who interacted with a multi-user museum exhibit, we outline the processes of deriving COT. We show how to quickly detect differences in subgroup configurations that may result from educational interventions and how COT can triangulate with and complement other forms of data (audio transcripts and activity logs) during lengthier analyses. We also outline how COT readings can be used to supply formative feedback on social engagement to learners and be adapted to other learning environments.

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Embodying scientific concepts in the physical space of the classroom

Cited by 7 publications

References 12 publications

Leveling the playing field: Grounding learning with embedded simulations in geoscience

Leveling the playing field: Grounding learning with embedded simulations in geoscience

The Evolution of Research on Digital Education

Exploring the Utility of Social-Network-Derived Collaborative Opportunity Temperature Readings for Informing Design and Research of Large-Group Immersive Learning Environments

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