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…

The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's…

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory--the component of short-term memory used for temporary…

This study is a qualitative analysis of the work of preservice special education teachers on an activity during which they had to design a game that represented the concept of working memory. The study draws from the framework of model-eliciting activities that require students to develop structural representations of concepts that can be used to…

Predicting learning and human behavior in general is a challenging endeavor. Machine learning driven predictive modeling have been an increasingly popular means to understand disparities in student performance. With more than a handful of approaches to predictive modeling, the current literature of predicting learning is plagued with issues such…

This article reports on a comprehensive synthesis of the literature on the role of working memory in second language (L2) writing. It starts with an overview and clarification of the construct and measurement of working memory, followed by an elaboration of major theoretical models informing the synthesized research. The article then presents a…

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the…

As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…

Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for learning from examples suggests that self-explanation quality mediates effects of examples on learning outcomes (Atkinson et al. in Rev Educ Res 70:181-214, 2000). We investigated…

Background: Inconsistent observations of pupillary response and blink change in response to different specific tasks raise questions regarding the relationship between eye measures, task types and working memory (WM) models. On the one hand, studies have provided mixed evidence from eye measures about tasks: pupil size has mostly been reported to…

Working memory is an essential mechanism in the cognitive learning process. However, its definitions and mechanisms remain a topic of debate. Miller-Cotto and Byrnes ("Journal of Educational Psychology," "112"(5), 1074-1084, 2020) reported a comparison of three models of working memory to determine which best accounted for data…

Working Memory (WM) is an essential concept of cognitive science since many aspects of human learning depend on it. Primarily proposed by Baddeley and Hitch (1974) and developed by Baddeley (1986), the concept of WM comprises the number of subsystems involved during the process and the considerable emphasis on its key role in cognitive tasks such…

Sensory integration takes place in the central nervous system where complex interactions such as co-ordination, attention, arousal levels, autonomic functioning, emotions, memory and higher level cognitive functions are carried out. Sensory integration gets information through the senses, puts it together with prior knowledge, information and…

Models of self-regulated learning emphasize the active and intentional role of learners and, thereby, focus mainly on conscious processes in working memory and long-term memory. Cognitive load theory supports this view on learning. As a result, both fields of research ignore the potential role of unconscious processes for learning. In this review…

Converging evidence suggests that traditional domain-general working memory (WM) training does not have reliable far-transfer effects, but produces reliable, modest near-transfer effects on structurally similar untrained tasks. Given the critical role of WM in academic development, WM training that incorporates task-specific features may maximize…