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1. Representation of the inferred relationships in a map‐like space

   https://doi.org/10.1002/hbm.26309  

   Li, Jinhui ; Liang, Qunjun ; Liao, Jiajun ; Zheng, Senning ; Chen, Kemeng ; Huang, Ruiwang ( April 2023 , Human Brain Mapping)

   A cognitive map is an internal representation of the external world that guides flexible behavior in a complex environment. Cognitive map theory assumes that relationships between entities can be organized using Euclidean‐based coordinates. Previous studies revealed that cognitive map theory can also be generalized to inferences about abstract spaces, such as social spaces. However, it is still unclear whether humans can construct a cognitive map by combining relational knowledge between discrete entities with multiple abstract dimensions in nonsocial spaces. Here we asked subjects to learn to navigate a novel object space defined by two feature dimensions, price and abstraction. The subjects first learned the rank relationships between objects in each feature dimension and then completed a transitive inferences task. We recorded brain activity using functional magnetic resonance imaging (fMRI) while they performed the transitive inference task. By analyzing the behavioral data, we found that the Euclidean distance between objects had a significant effect on response time (RT). The longer the one‐dimensional rank distance and two‐dimensional (2D) Euclidean distance between objects the shorter the RT. The task‐fMRI data were analyzed using both univariate analysis and representational similarity analysis. We found that the hippocampus, entorhinal cortex, and medial orbitofrontal cortex were able to represent the Euclidean distance between objects in 2D space. Our findings suggest that relationship inferences between discrete objects can be made in a 2D nonsocial space and that the neural basis of this inference is related to cognitive maps.

    

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2. How the use of concept maps changes students’ minds and brains.

   Shealy, T. ; Gero, J. ; Ignacio Jr, P. ( July 2022 , ASEE Annual Conference & Exposition)

   The research presented in this paper tested whether drawing concept maps changes how engineering students construct design problem statements and whether these differences are observable in their brains. The process of identifying and constructing problem statements is a critical step in engineering design. Concept mapping has the potential to expand the problem space that students explore through the attention given to the relationship between concepts. It helps integrate existing knowledge in new ways. Engineering students (n=66) were asked to construct a problem statement to improve mobility on campus. Half of these students were randomly chosen to first receive instructions about how to develop a concept map and were asked to draw a concept map about mobility systems on campus. The semantic similarity of concepts in the students’ problem statements, the length of their problem statements, and their neurocognition when developing their statements were measured. The results indicated that students who were asked to first draw concept maps produced a more diverse problem statement with less semantically similar words. The students who first developed concept maps also produce significantly longer problem statements. Concept mapping changed students’ neurocognition. The students who used concept mapping elicited less cognitive activation in their left prefrontal cortex (PFC) and more concentrated activation in their right PFC. The right PFC is generally associated with divergent thinking and the left PFC is generally associated with convergent and analytical thinking. These results provide new insight into how educational interventions, like concept mapping, can change students’ cognition and neurocognition. Better understanding how concept maps, and other tools, help students approach complex problems and the associated changes that occur in their brain can lay the groundwork for novel advances in engineering education that support new tools and pedagogy development for design. 

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3. Decoding individual differences in STEM learning from functional MRI data

   https://doi.org/10.1038/s41467-019-10053-y  

   Cetron, Joshua S. ; Connolly, Andrew C. ; Diamond, Solomon G. ; May, Vicki V. ; Haxby, James V. ; Kraemer, David J. M. ( May 2019 , Nature Communications)

   Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural ‘score’ to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

    

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4. How students reason about matter flows and accumulations in complex biological phenomena: An emerging learning progression for mass balance

   https://doi.org/10.1002/tea.21791  

   Scott, Emily E. ; Cerchiara, Jack ; McFarland, Jenny L. ; Wenderoth, Mary Pat ; Doherty, Jennifer H. ( July 2022 , Journal of Research in Science Teaching)

   In recent years, there has been a strong push to transform STEM education at K‐12 and collegiate levels to help students learn to think like scientists. One aspect of this transformation involves redesigning instruction and curricula around fundamental scientific ideas that serve as conceptual scaffolds students can use to build cohesive knowledge structures. In this study, we investigated how students use mass balance reasoning as a conceptual scaffold to gain a deeper understanding of how matter moves through biological systems. Our aim was to lay the groundwork for a mass balance learning progression in physiology. We drew on a general models framework from biology and a covariational reasoning framework from math education to interpret students' mass balance ideas. We used a constant comparative method to identify students' reasoning patterns from 73 interviews conducted with undergraduate biology students. We helped validate the reasoning patterns identified with >8000 written responses collected from students at multiple institutions. From our analyses, we identified two related progress variables that describe key elements of students' performances: the first describes how students identify and use matter flows in biology phenomena; the second characterizes how students use net rate‐of‐change to predict how matter accumulates in, or disperses from, a compartment. We also present a case study of how we used our emerging mass balance learning progression to inform instructional practices to support students' mass balance reasoning. Our progress variables describe one way students engage in three dimensional learning by showing how student performances associated with the practice of mathematical thinking reveal their understanding of the core concept of matter flows as governed by the crosscutting concept of matter conservation. Though our work is situated in physiology, it extends previous work in climate change education and is applicable to other scientific fields, such as physics, engineering, and geochemistry.

    

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5. Using the force: STEM knowledge and experience construct shared neural representations of engineering concepts

   https://doi.org/10.1038/s41539-020-0065-x  

   Cetron, Joshua S. ; Connolly, Andrew C. ; Diamond, Solomon G. ; May, Vicki V. ; Haxby, James V. ; Kraemer, David J. ( December 2020 , npj Science of Learning)

   null (Ed.)

   Abstract How does STEM knowledge learned in school change students’ brains? Using fMRI, we presented photographs of real-world structures to engineering students with classroom-based knowledge and hands-on lab experience, examining how their brain activity differentiated them from their “novice” peers not pursuing engineering degrees. A data-driven MVPA and machine-learning approach revealed that neural response patterns of engineering students were convergent with each other and distinct from novices’ when considering physical forces acting on the structures. Furthermore, informational network analysis demonstrated that the distinct neural response patterns of engineering students reflected relevant concept knowledge: learned categories of mechanical structures. Information about mechanical categories was predominantly represented in bilateral anterior ventral occipitotemporal regions. Importantly, mechanical categories were not explicitly referenced in the experiment, nor does visual similarity between stimuli account for mechanical category distinctions. The results demonstrate how learning abstract STEM concepts in the classroom influences neural representations of objects in the world. 

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