In an educational setting, an estimate of the difficulty of Multiple-Choice Questions (MCQs), a commonly used strategy to assess learning progress, constitutes very useful information for both teachers and students. Since human assessment is costly from multiple points of view, automatic approaches to MCQ item difficulty estimation are…

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining…

As artificial intelligence (AI) technologies are increasingly pervasive in our daily lives, the need for students to understand the working mechanisms of AI technologies has become more urgent. Data modeling is an activity that has been proposed to engage students in reasoning about the working mechanism of AI technologies. While Computational…

Automatic short answer grading (ASAG) is a challenging task that aims to predict a score for a given student response. Previous works on ASAG mainly use nonneural or neural methods. However, the former depends on handcrafted features and is limited by its inflexibility and high cost, and the latter ignores global word cooccurrence in a corpus and…

Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing…

Completion of remedial mathematics has been identified as one of the keys to college success. However, completion rates in remedial mathematics have been low and are of much debate across America. This study leverages machine learning techniques in trying to predict and understand completion rates in remedial mathematics. The purpose of this study…

Purpose: Dyslexia is characterized by its diverse causes and heterogeneous manifestations. Chinese children with dyslexia exhibit orthographic, phonological, and semantic deficits across character and radical levels when writing. However, whether character dictation can be used to distinguish children with dyslexia from their typically developing…

Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…

Advances in the integration of smart technology with interdisciplinary methods has created a new genre, approachable modeling and smart methods -- AM-Smart for short. AM-Smart platforms address a major challenge for applied and public sector analysts, educators and those trained in traditional methods: accessing the latest advances in…

We present a conceptual impact model illustrating how digital tools can facilitate the fulfillment of basic psychological needs, autonomy, competence and relatedness, which in turn may foster improvements in self-regulation and emotion regulation. The model incorporates features of digital tools such as personalized learning paths, real-time…

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…

Educational systems use models of student skill to inform decision-making processes. Defining such models manually is challenging due to the large number of relevant factors. We propose learning multidimensional representations (embeddings) from student activity data -- these are fixed-length real vectors with three desirable characteristics:…

Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study…

This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models…

In this opinion piece, we explore the idea that GenAI has the potential to fundamentally disrupt computer science education (CSE) by drawing insights from 10 pedagogical and cognitive theories and models. We highlight how GenAI improves CSE by making educational practices more effective and requires less effort and time, and all at a lower cost,…