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 samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both…

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…

Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions' reputation and funding. Dropout can occur for a variety of reasons, including academic, financial,…

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational…

We constructed 4 working memory recognition models to predict behavior in the local recognition task (also called change detection), in which both content (e.g., color) and context (e.g., location) information are necessary to make correct recognition decisions. The theoretical assumptions incorporated in the models come from crossing 2 contrasts:…

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…

Replication of machine learning experiments can be a useful tool to evaluate how both "modeling" and "experimental design" contribute to experimental results; however, existing replication efforts focus almost entirely on modeling alone. In this work, we conduct a three-part replication case study of a state-of-the-art LSTM…

Guided by evolutionary-developmental models, this study tested the hypothesis that children's exposure to parental relationship instability, defined by initiation and dissolution of caregiver intimate relationships, has both costs in cognitive impairments and benefits in enhanced learning skills. Participants included 243 mothers and their…

The interaction between working memory and language processing is widely discussed in cognitive research. However, those studies often explore the relationship between language comprehension and working memory (WM). The role of WM is rarely considered in language production, despite some evidence suggesting a relationship between the two cognitive…

In prospective memory (PM) research, costs (slowed responding to the ongoing task when a PM task is present relative to when it is not) have typically been interpreted as implicating an attentionally demanding monitoring process. To inform this interpretation, Heathcote, Loft, and Remington (2015), using an accumulator model, found that PM-related…

The development of visual expertise is accompanied by enhanced visual object recognition memory within an expert domain. We aimed to understand the relationship between expertise and memory by modeling cognitive mechanisms. Participants with a measured range of birding expertise were recruited and tested on memory for birds (expert domain) and…

Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and effective exploration. Guidance in the form of targeted interventions or feedback therefore has…

The role of working memory (WM) in language acquisition has been widely reported in the developmental literature, but few studies have explored the role of sentence recall in the way WM and related linguistic abilities evolve. This study seeks to explore the organization and development of the memory architecture underlying language using a…

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…