This study is intended to provide an example of computational modeling (CM) experiment using machine learning algorithms. Specific outcomes modeled in this study are the predicted influences associated with the Science Writing Heuristic (SWH) and associated with the completion of question items for the Cornell Critical Thinking Test. The Student Task and Cognition Model in this study uses cognitive data from a large-scale randomized control study. Results of the computational model experiment provide for the possibility to increase student success via targeted cognitive retraining of specific cognitive attributes via the SWH. This study also illustrates that computational modeling using machine learning algorithms (MLA) is a significant resource for testing educational interventions, informs specific hypotheses, and assists in the design and development of future research designs in science education research.
The primary barrier to understanding visual and abstract information in STEM fields is representational competence the ability to generate, transform, analyze and explain representations. The relationship is known between the foundational visual literacy and the domain specific science literacy, however how science literacy is a function of science learning is still not well understood despite investigation across many fields. To support the improvement of students’ representational competence and promote learning in science, identification of visualization skills is necessary. This project details the development of an artificial neural network (ANN) capable of measuring and modeling visual science literacy (VSL) via neurological measurements using functional near infrared spectrometry (fNIRS). The developed model has the capacity to classify levels of scientific visual literacy allowing educators and curriculum designers the ability to create more targeted and immersive classroom resources such as virtual reality, to enhance the fundamental visual tools in science.
Verbal communication to relay information between students and the teacher, i.e., talk, lies at the heart of all science classrooms. This study investigated and began to characterize the neurological basis for the talk between science teachers and students in terms of speaker-listener coupling in a naturalistic setting. Speaker-listener coupling is the time-locked moment in which speaker vocalizations result in activity in the listeners brain. This activity is highly predictive and tightly ties to listener understanding. The design for this study was an observational stimulus-response study using neuroimaging data obtained from talk sessions between a teacher and a student. Results were obtained using a functional near-infrared spectrometer and an artificial neural network model. Examination of the data suggested that speaker-listener coupling occurs between a student and a teacher during successfully understood verbal communications. This study promotes further research into the exploration of how individual interactions between persons (speakers and listeners) via talk are perceived and influence individual cognition. Study outcomes suggest coupled brains create new knowledge, integrate practices and content, and verbal and nonverbal communication systems which are constrained at two levels the environmental level and the speaker listener level. The simplicity of brain-to-brain coupling as a reference system may simplify the understanding of behaviors seen during the learning of science in the classroom.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.