Dataset Information

Effects of Embodied Learning and Digital Platform on the Retention of Physics Content: Centripetal Force.

ABSTRACT: Embodiment theory proposes that knowledge is grounded in sensorimotor systems, and that learning can be facilitated to the extent that lessons can be mapped to these systems. This study with 109 college-age participants addresses two overarching questions: (a) how are immediate and delayed learning gains affected by the degree to which a lesson is embodied, and (b) how do the affordances of three different educational platforms affect immediate and delayed learning? Six 50 min-long lessons on centripetal force were created. The first factor was the degree of embodiment with two levels: (1) low and (2) high. The second factor was platform with three levels: (1) a large scale "mixed reality" immersive environment containing both digital and hands-on components called SMALLab, (2) an interactive whiteboard system, and (3) a mouse-driven desktop computer. Pre-tests, post-tests, and 1-week follow-up (retention or delayed learning gains) tests were administered resulting in a 2 × 3 × 3 design. Two knowledge subtests were analyzed, one that relied on more declarative knowledge and one that relied on more generative knowledge, e.g., hand-drawing vectors. Regardless of condition, participants made significant immediate learning gains from pre-test to post-test. There were no significant main effects or interactions due to platform or embodiment on immediate learning. However, from post-test to follow-up the level of embodiment interacted significantly with time, such that participants in the high embodiment conditions performed better on the subtest devoted to generative knowledge questions. We posit that better retention of certain types of knowledge can be seen over time when more embodiment is present during the encoding phase. This sort of retention may not appear on more traditional factual/declarative tests. Educational technology designers should consider using more sensorimotor feedback and gestural congruency when designing and opportunities for instructor professional development need to be provided as well.

SUBMITTER: Johnson-Glenberg MC

PROVIDER: S-EPMC5122822 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Effects of Embodied Learning and Digital Platform on the Retention of Physics Content: Centripetal Force.

Johnson-Glenberg Mina C MC Megowan-Romanowicz Colleen C Birchfield David A DA Savio-Ramos Caroline C

Frontiers in psychology 20161125

Embodiment theory proposes that knowledge is grounded in sensorimotor systems, and that learning can be facilitated to the extent that lessons can be mapped to these systems. This study with 109 college-age participants addresses two overarching questions: (a) how are immediate and delayed learning gains affected by the degree to which a lesson is embodied, and (b) how do the affordances of three different educational platforms affect immediate and delayed learning? Six 50 min-long lessons on ce ...[more]

PMID: 27933009

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Project description:A mixed design was created using text and game-like multimedia to instruct in the content of physics. The study assessed which variables predicted learning gains after a 1-h lesson on the electric field. The three manipulated variables were: (1) level of embodiment; (2) level of active generativity; and (3) presence of story narrative. Two types of tests were administered: (1) a traditional text-based physics test answered with a keyboard; and (2) a more embodied, transfer test using the Wacom large tablet where learners could use gestures (long swipes) to create vectors and answers. The 166 participants were randomly assigned to four conditions: (1) symbols and text; (2) low embodied; (3) high embodied/active; or (4) high embodied/active with narrative. The last two conditions were active because the on-screen content could be manipulated with gross body gestures gathered via the Kinect sensor. Results demonstrated that the three groups that included embodiment learned significantly more than the symbols and text group on the traditional keyboard post-test. When knowledge was assessed with the Wacom tablet format that facilitated gestures, the two active gesture-based groups scored significantly higher. In addition, engagement scores were significantly higher for the two active embodied groups. The Wacom results suggest test sensitivity issues; the more embodied test revealed greater gains in learning for the more embodied conditions. We recommend that as more embodied learning comes to the fore, more sensitive tests that incorporate gesture be used to accurately assess learning. The predicted differences in engagement and learning for the condition with the graphically rich story narrative were not supported. We hypothesize that a narrative effect for motivation and learning may be difficult to uncover in a lab experiment where participants are primarily motivated by course credit. Several design principles for mediated and embodied science education are proposed.

Project description:Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not leverage interaction with the world and are limited to a set of objects seen during training. Humans on the other hand learn to recognize and classify different objects by actively engaging with them on first encounter. Moreover, recent theories in neuroscience suggest that cortical columns in the neocortex play an important role in this process, by building predictive models about objects in their reference frame. In this article, we present an enactive embodied agent that implements such a generative model for object interaction. For each object category, our system instantiates a deep neural network, called Cortical Column Network (CCN), that represents the object in its own reference frame by learning a generative model that predicts the expected transform in pixel space, given an action. The model parameters are optimized through the active inference paradigm, i.e., the minimization of variational free energy. When provided with a visual observation, an ensemble of CCNs each vote on their belief of observing that specific object category, yielding a potential object classification. In case the likelihood on the selected category is too low, the object is detected as an unknown category, and the agent has the ability to instantiate a novel CCN for this category. We validate our system in an simulated environment, where it needs to learn to discern multiple objects from the YCB dataset. We show that classification accuracy improves as an embodied agent can gather more evidence, and that it is able to learn about novel, previously unseen objects. Finally, we show that an agent driven through active inference can choose their actions to reach a preferred observation.

Dataset Information

Effects of Embodied Learning and Digital Platform on the Retention of Physics Content: Centripetal Force.

Publications

Effects of Embodied Learning and Digital Platform on the Retention of Physics Content: Centripetal Force.

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