Brain activity links performance in science reasoning with conceptual approach

Abstract

Understanding how students learn is crucial for helping them succeed. We examined brain function in 107 undergraduate students during a task known to be challenging for many students—physics problem solving—to characterize the underlying neural mechanisms and determine how these support comprehension and proficiency. Further, we applied module analysis to response distributions, defining groups of students who answered by using similar physics conceptions, and probed for brain differences linked with different conceptual approaches. We found that integrated executive, attentional, visual motion, and default mode brain systems cooperate to achieve sequential and sustained physics-related cognition. While accuracy alone did not predict brain function, dissociable brain patterns were observed when students solved problems by using different physics conceptions, and increased success was linked to conceptual coherence. Our analyses demonstrate that episodic associations and control processes operate in tandem to support physics reasoning, offering potential insight to support student learning.

An Understanding of (Mis)Understanders: Exploring the Underlying Mechanisms of Concept Learning Using Functional Magnetic Resonance Imaging

“…Instruction regarding conceptual understanding of scientific concepts should aim to disclose students' prior knowledge and assumptions about the concept of interest. Additionally, explicit attention to metacognitive components of learning may also facilitate the putative control processes that are involved in concept learning (Allaire-Duquette et al, 2019;Bartley et al, 2019).…”

Simulating a Computational Biological Model, Rather Than Reading, Elicits Changes in Brain Activity during Biological Reasoning

“…Functional magnetic resonance imaging (fMRI) is a particularly powerful tool for evaluating changes in the spatial distribution of neural activity in response to instruction. Characterization of the neural networks involved in scientific reasoning is a relatively new endeavor, although studies from the last 25 years have established the ability of fMRI to provide insight into the neural bases of scientific reasoning and the effects of different instructional formats on these neural mechanisms (e.g., Masson et al, 2014;Kontra et al, 2015;Mason and Just, 2015;Bartley et al, 2019;Schwettmann et al, 2019). For example, Kontra et al (2015) used fMRI to examine the impact of different instructional methods in physics.…”

“…Because these areas have been linked to working memory and higher-level reasoning, the authors suggested that MBI might support students' use of mental simulation and prediction generation. Moreover, students used different neural networks during sequential phases of reasoning, drawing initially on brain regions associated with higher-level working memory and proceeding to regions linked to visual information processing and memory retrieval (Bartley et al, 2019). Notably, this study involved pre-and postsemester MRI scans, as opposed to evaluating the effects of MBI relative to other forms of instruction.…”

“…A meta-analysis of several reasoning and problem-solving studies indicated that these tasks generally activate a network encompassing the dorsolateral PFC, anterior cingulate and anterior insular regions, and posterior parietal regions (Bartley et al, 2018). In a study of undergraduate students' brain activity in response to conceptual reasoning in physics, students showed activity bilaterally in the dorsolateral and lateral orbitofrontal PFC, although several other regions, including the posterior parietal, retrosplenial, posterior cingulate, and lateral occipito-temporal regions, also were implicated (Bartley et al, 2019). Complex analogical reasoning tasks that involve the integration of multiple sources of information elicit activity in the most rostro-lateral regions of the PFC (Green et al, 2006;Krawczyk et al, 2011;Watson and Chatterjee, 2012).…”

“…Although the students in our Simulate group did not show the hypothesized pattern of greater activity in lateral prefrontal regions, they may have been drawing on different mental models from the Read group to reason through their responses, reflected in their different neural response patterns. Both Bartley et al (2019) and Lee (2012) found that students used an array of posterior brain regions, including posterior parietal, lingual, and parahippocampal areas, when reasoning about physical and biological systems, suggesting that educational strategies designed to support these reasoning processes may affect neural networks extending beyond lateral prefrontal areas. Given the role of Brodmann area 2 in somatosensory processing (Grefkes et al, 2001; also see Supplemental Material, which includes a meta-analytic functional decoding analysis of our fMRI results), it is possible that students in the Simulate group were re-instantiating the more interactive sensory process of manipulating models when re-exposed to those models in the scanner.…”