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Lu, Yu; Wang, Deliang; Chen, Penghe; Meng, Qinggang; Yu, Shengquan – International Journal of Artificial Intelligence in Education, 2023
As a prominent aspect of modeling learners in the education domain, knowledge tracing attempts to model learner's cognitive process, and it has been studied for nearly 30 years. Driven by the rapid advancements in deep learning techniques, deep neural networks have been recently adopted for knowledge tracing and have exhibited unique advantages…
Descriptors: Learning Processes, Artificial Intelligence, Intelligent Tutoring Systems, Data Analysis
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Delianidi, Marina; Diamantaras, Konstantinos – Journal of Educational Data Mining, 2023
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…
Descriptors: Academic Achievement, Prediction, Cognitive Measurement, Bayesian Statistics
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Limin Wang; Qing Zhou; Le Ji; Huiying Zhang; Ye Li – Journal of Baltic Science Education, 2025
Evaluating students' cognitive understanding of core concepts and identifying learning path aligned with their cognitive developmental patterns are critical for enhancing conceptual mastery in chemistry. This study employed the Rule Space Model to investigate students' cognitive characteristics and learning path in comprehending the conceptual…
Descriptors: Secondary School Students, Foreign Countries, Chemistry, Scientific Concepts
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Zhan, Peida; Liu, Yaohui; Yu, Zhaohui; Pan, Yanfang – Applied Measurement in Education, 2023
Many educational and psychological studies have shown that the development of students is generally step-by-step (i.e. ordinal development) to a specific level. This study proposed a novel longitudinal learning diagnosis model with polytomous attributes to track students' ordinal development in learning. Using the concept of polytomous attributes…
Descriptors: Skill Development, Cognitive Measurement, Models, Educational Diagnosis
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H. Chin; C. M. Chew – Educational Psychology, 2025
Despite the importance of parallelism concept in understanding advanced geometric concepts, past studies have constantly reported poor mastery of this concept. To pinpoint students' current states in the progression of parallelism concept acquisition, this study aimed to profile students' knowledge states and model their learning pathways.…
Descriptors: Cognitive Measurement, Diagnostic Tests, Knowledge Level, Learning Processes
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Xu, Tianshu; Wu, Xiaopeng; Sun, Siyu; Kong, Qiping – Psychology in the Schools, 2023
Considering the importance of mathematics in modern society, it is crucial to understand the cognitive processes involved in the acquisition of complex mathematical competency. As a new generation of evaluation theory, cognitive diagnosis has its unique advantages in personalized evaluation. Based on the mathematical cognitive framework of Trends…
Descriptors: Cognitive Processes, Mathematics Skills, Competence, Grade 4
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Wu, Lin-Jung; Chang, Kuo-En – Interactive Learning Environments, 2023
To achieve adaptive learning, a dynamic assessment system equipped with a cognitive diagnosis was developed for this study, which adopts a three-stage model of diagnosis-intervention-assessment. To examine how this system influenced spatial geometry learning, the study used a quasi-experimental method to investigate student learning outcomes…
Descriptors: Cognitive Measurement, Alternative Assessment, Spatial Ability, Geometry