scholarly journals Using the Force: STEM Knowledge and Experience Construct Shared Neural Representations of Engineering Concepts

2017 ◽  
Author(s):  
Joshua S. Cetron ◽  
Andrew C. Connolly ◽  
Solomon G. Diamond ◽  
Vicki V. May ◽  
James V. Haxby ◽  
...  
Keyword(s):  
School Change ◽  
Engineering Students ◽  
Brain Activity ◽  
Neural Response ◽  
Data Driven ◽  
Response Patterns ◽  
Neural Representations ◽  
Hands On ◽  
Machine Learning Approach ◽  
Physical Forces

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.

scholarly journals Neural alignment predicts learning outcomes in students taking an introduction to computer science course

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meir Meshulam ◽  
Liat Hasenfratz ◽  
Hanna Hillman ◽  
Yun-Fei Liu ◽  
Mai Nguyen ◽  
...  
Keyword(s):  
Computer Science ◽  
Learning Outcomes ◽  
Brain Activity ◽  
Activity Patterns ◽  
Real Life ◽  
Final Exam ◽  
Neural Representations ◽  
Real Life Setting ◽  
The Right ◽  
Mri Study

AbstractDespite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner’s neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

scholarly journals Development and delivery of a work experience week programme for mechanical engineering

2020 ◽  
pp. 030641902098104
Author(s):  
A Gonzalez-Buelga ◽  
I Renaud-Assemat ◽  
B Selwyn ◽  
J Ross ◽  
I Lazar
Keyword(s):  
Work Experience ◽  
Mechanical Engineering ◽  
Engineering Students ◽  
Impact Analysis ◽  
Legal Term ◽  
Stage 4 ◽  
Engineering Design Problem ◽  
Hands On ◽  
University Of Bristol ◽  
The University

This paper focuses on the development, delivery and preliminary impact analysis of an engineering Work Experience Week (WEW) programme for KS4 students in the School of Civil, Aerospace and Mechanical Engineering (CAME) at the University of Bristol, UK. Key stage 4, is the legal term for the two years of school education which incorporate GCSEs in England, age 15–16. The programme aims to promote the engineering profession among secondary school pupils. During the WEW, participants worked as engineering researchers: working in teams, they had to tackle a challenging engineering design problem. The experience included hands-on activities and the use of state-of-the-art rapid prototyping and advanced testing equipment. The students were supervised by a group of team leaders, a diverse group of undergraduate and postgraduate engineering students, technical staff, and academics at the School of CAME. The vision of the WEW programme is to transmit the message that everybody can be an engineer, that there are plenty of different routes into engineering that can be taken depending on pupils’ strengths and interests and that there are a vast amount of different engineering careers and challenges to be tackled by the engineers of the future. Feedback from the participants in the scheme has been overwhelmingly positive.

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