Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules: (i) a…

Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…

From about 7 months of age onward, infants start to reliably fixate the goal of an observed action, such as a grasp, before the action is complete. The available research has identified a variety of factors that influence such goal-anticipatory gaze shifts, including the experience with the shown action events and familiarity with the observed…

How does the act of explaining influence learning? Prior work has studied effects of explaining through a predominantly proximal lens, measuring short-term outcomes or manipulations within lab settings. Here, we ask whether the benefits of explaining extend to academic performance over time. Specifically, does the quality and frequency of student…

According to the theory of predictive processing, understanding in the present involves non-consciously representing the immediate future, based on probabilistic inference shaped by learning from the past. This paper suggests links between this neuroscientific theory and the psychoanalytic concept of reverie -- an empathic, containing attentional…

Existing approaches in the literature on cognitive control in conflict tasks almost exclusively target the outcome of control (by comparing mean congruency effects) and not the processes that shape control. These approaches are limited in addressing a current theoretical issue--what contribution does learning make to adjustments in cognitive…

Research on what neural mechanisms facilitate word reading development in non-alphabetic scripts is relatively rare. The present study was among the first to adopt a multivariate pattern classification analysis to decode electroencephalographic signals recorded for primary school children (N = 236) while performing a Chinese character decision…

Children's everyday activities enable them to learn some science even before entering preschool education and children bring these ideas with them when entering education settings. Some of these ideas, or else mental representations, may not be compatible with what is generally accepted by the scientific community. This paper presents the results…

The Iowa Gambling Task (IGT) is widely used to study decision-making within healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the IGT in an effort to address this problem, but currently no…

Skill acquisition theorists conceptualize second language (L2) learning as the acquisition of a set of perceptual, cognitive, and motor skills. The dominant view in skill acquisition theory is to regard L2 skill acquisition as a three-stage process "from initial representation of knowledge through initial changes in behavior to eventual…

This dissertation presents various machine learning applications for predicting different cognitive states of students while they are using a vocabulary tutoring system, DSCoVAR. We conduct four studies, each of which includes a comprehensive analysis of behavioral and linguistic data and provides data-driven evidence for designing personalized…

A complete understanding of the fundamental mechanisms of learning and memory continues to elude neuroscientists. Although many important discoveries have been made, the question of how memories are encoded and maintained at the molecular level remains. So far, this issue has been framed within the context of one of the most dominant concepts in…

Comprehensive understanding of complex biological systems necessitates the use of computational models because they facilitate visualisation and interrogation of system dynamics and data-driven analysis. Computational model-based (CMB) activities have demonstrated effectiveness in improving students' understanding and their ability to use and…

The cognitive conflict hypothesis asserts that information that directly contradicts a prior conception is 1 of the prerequisites for conceptual change and other forms of nonmonotonic learning. There have been numerous attempts to support this hypothesis by adding a conflict intervention to learning scenarios with weak outcomes. Outcomes have been…

Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling…