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Chenchen Ma; Jing Ouyang; Gongjun Xu – Grantee Submission, 2023
Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models that are widely used in educational and psychological measurement. A key component of CDMs is the Q-matrix characterizing the dependence structure between the items and the latent attributes. Additionally, researchers also assume in many applications certain…
Descriptors: Psychological Evaluation, Clinical Diagnosis, Item Analysis, Algorithms
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Landers, Richard N.; Auer, Elena M.; Mersy, Gabriel; Marin, Sebastian; Blaik, Jason – International Journal of Testing, 2022
Assessment trace data, such as mouse positions and their timing, offer interesting and provocative reflections of individual differences yet are currently underutilized by testing professionals. In this article, we present a 10-step procedure to maximize the probability that a trace data modeling project will be successful: (1) grounding the…
Descriptors: Artificial Intelligence, Data Collection, Psychometrics, Data Science
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Ezberci-Çevik, Ebru; Kurnaz, Mehmet Altan – Malaysian Online Journal of Educational Technology, 2022
In this study, it is aimed to reveal the models related to star subject as one of the concepts of astronomy of prospective science teachers before and after the current instruction through model analysis. This modeling situation is expressed as a Grounded Mental Model (GMM), since there will be a mental modeling that is revealed according to what…
Descriptors: Schemata (Cognition), Astronomy, Science Teachers, Preservice Teachers
Tianci Liu; Chun Wang; Gongjun Xu – Grantee Submission, 2022
Multidimensional Item Response Theory (MIRT) is widely used in educational and psychological assessment and evaluation. With the increasing size of modern assessment data, many existing estimation methods become computationally demanding and hence they are not scalable to big data, especially for the multidimensional three-parameter and…
Descriptors: Item Response Theory, Computation, Monte Carlo Methods, Algorithms
Vincent Dorie; George Perrett; Jennifer L. Hill; Benjamin Goodrich – Grantee Submission, 2022
A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…
Descriptors: Statistical Inference, Causal Models, Artificial Intelligence, Data Analysis
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Yoonjae Noh; YoonIl Yoon; Sangjin Kim – Measurement: Interdisciplinary Research and Perspectives, 2024
The default risk, one of the main risk factors for bonds, should be measured and reflected in the bond yield. Particularly, in the case of financial companies that treat bonds as a major product, failure to properly identify and filter customers' workout status adversely affects returns. This study proposes a two-stage classification algorithm for…
Descriptors: Prediction, Classification, Accuracy, Risk
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Houssam El Aouifi; Mohamed El Hajji; Youssef Es-Saady – Education and Information Technologies, 2024
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,…
Descriptors: At Risk Students, Potential Dropouts, Identification, Influences
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Nesrine Mansouri; Mourad Abed; Makram Soui – Education and Information Technologies, 2024
Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains…
Descriptors: Undergraduate Students, Decision Making, Majors (Students), Specialization
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Mingqing Wang; Kui Xie – International Journal of Web-Based Learning and Teaching Technologies, 2024
At present, the construction of online courses is an important content in the process of educational informatization. At present, there are two problems in the construction of online courses: from the teaching point of view, the design of teaching activities and teaching processes in online courses is not enough, and the teaching effect is not…
Descriptors: English Instruction, Online Courses, English Curriculum, Curriculum Development
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Tongxi Liu – Journal of Educational Computing Research, 2024
Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or…
Descriptors: Executive Function, Computation, Thinking Skills, Data Science
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Jeroen Dera – Literacy, 2024
Despite the widespread popularity of online reading challenges on platforms like Goodreads and The StoryGraph, research on this phenomenon has been mostly absent. This article addresses this gap by examining the motivations of adolescent participants in reading challenges, the outcomes of their participation and the implications for their reading…
Descriptors: Reading Programs, Computer Use, Internet, Participation
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Sohum Bhatt; Katrien Verbert; Wim Van Den Noortgate – Journal of Learning Analytics, 2024
Computational thinking (CT) is a concept of growing importance to pre-university education. Yet, CT is often assessed through results, rather than by looking at the CT process itself. Process-based assessments, or assessments that model how a student completed a task, could instead investigate the process of CT as a formative assessment. In this…
Descriptors: Learning Analytics, Student Evaluation, Computation, Thinking Skills
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Pankaj Chejara; Luis P. Prieto; Yannis Dimitriadis; Maria Jesus Rodriguez-Triana; Adolfo Ruiz-Calleja; Reet Kasepalu; Shashi Kant Shankar – Journal of Learning Analytics, 2024
Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the…
Descriptors: Learning Analytics, Attribution Theory, Acoustics, Artificial Intelligence
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Moran P. Lee; Abubakir Siedahmed; Neil T. Heffernan – Grantee Submission, 2024
Contextual multi-armed bandits have previously been used to personalize student support messages given to learners by supplying a model with relevant context about the user, problem, and available student supports. In this work, we propose using careful feature selection with relevant domain knowledge to improve the quality of student support…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Reinforcement
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Riordan Alfredo; Vanessa Echeverria; Linxuan Zhao; LuEttaMae Lawrence; Jie Xiang Fan; Lixiang Yan; Xinyu Li; Zachari Swiecki; Dragan Gaševic; Roberto Martinez-Maldonado – Journal of Learning Analytics, 2024
Despite growing interest in applying human-centred design methods to create learning analytics (LA) systems, most efforts have concentrated on initial design phases, with limited exploration of how LA tools and practices can coevolve during the actual learning and teaching activities. This paper examines how a human-centred LA dashboard can be…
Descriptors: Learning Analytics, Learning Management Systems, Artificial Intelligence, Computer Software
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