Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Abstract

Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA) can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M) and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a "virtual" student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M) a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning.

Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning

“…The third layer and final layer of the ANN in the CM act as an integrative node pulling information together from the various processing systems. This activity also consists with current understandings of executive function and working memory components related to inferencing (Eason and Ramani 2017;Lamb & Premo, 2015;.…”

Categories in conflict: Combating the application of an intuitive conception of inheritance with category construction

“…Relational categories form a key basis of knowledge organization for experts (Chi, Feltovich, & Glaser, ). In this case, we hypothesize that learners will be better equipped to identify evolutionary change and retrieve science‐based knowledge when reasoning about ecological situations and even situations that are relationally similar but lack an ecological or biological context (e.g., artificial evolutionary processes such as evolutionary algorithms, for an example see Lamb & Premo, ).…”