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Julian F. Lohmann; Nils Machts; Jens Möller; Steffen Zitzmann – Educational Psychology Review, 2025
We propose a novel approach for modeling judgment accuracy that, for the first time, allows for simultaneously considering the rank, level, and differentiation component, the predominantly applied operationalization of teacher judgment accuracy. These components are conceptualized as latent, unobserved individual abilities. The model is introduced…
Descriptors: Teacher Attitudes, Evaluative Thinking, Accuracy, Models
Samantha Mann; Aldert Vrij; Haneen Deeb – Applied Cognitive Psychology, 2024
We examined the efficacy of a Model Statement to detect opinion lies. A total of 93 participants discussed their opinion about the recent strikes on two occasions, 1 week apart. In one interview they told the truth and in the other interview they lied. Each interview consisted of two phases. In Phase 1 they discussed their alleged opinion (truth…
Descriptors: Opinions, Accuracy, Deception, Credibility
Tong Zhang; Ermei Lu; Quanming Liao; Deliang Sun – Journal of Psychoeducational Assessment, 2025
Purpose: Academic anxiety is a common phenomenon in the college student population, which has an important impact on students' psychological health and academic performance. Therefore, by exploring the effects of college students' professional commitment and achievement goal orientation variables on academic anxiety, it helps to understand…
Descriptors: College Students, Anxiety, Academic Achievement, Student Attitudes
Tanja C. Roembke; Bob McMurray – Cognitive Science, 2025
Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…
Descriptors: Vocabulary Development, Computation, Models, Accuracy
Mahmoud Abdasalam; Ahmad Alzubi; Kolawole Iyiola – Education and Information Technologies, 2025
This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…
Descriptors: Grades (Scholastic), Prediction, Accuracy, Artificial Intelligence
Zhenchang Xia; Nan Dong; Jia Wu; Chuanguo Ma – IEEE Transactions on Learning Technologies, 2024
As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…
Descriptors: Graphs, Artificial Intelligence, Multivariate Analysis, Prediction
Tae Yeon Kwon; A. Corinne Huggins-Manley; Jonathan Templin; Mingying Zheng – Journal of Educational Measurement, 2024
In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical…
Descriptors: Classification, Accuracy, Student Evaluation, Sequential Approach
Sam Kirkham – Cognitive Science, 2025
A fundamental challenge in the cognitive sciences is discovering the dynamics that govern behavior. Take the example of spoken language, which is characterized by a highly variable and complex set of physical movements that map onto the small set of cognitive units that comprise language. What are the fundamental dynamical principles behind the…
Descriptors: Speech Communication, Interpersonal Communication, Nonverbal Communication, Cognitive Science
Caihong Feng; Jingyu Liu; Jianhua Wang; Yunhong Ding; Weidong Ji – Education and Information Technologies, 2025
Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…
Descriptors: Academic Achievement, Prediction, Models, Student Behavior
Jean-Paul Fox – Journal of Educational and Behavioral Statistics, 2025
Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…
Descriptors: Sample Size, Item Response Theory, Accuracy, Bayesian Statistics
Yusuf Kara; Akihito Kamata; Xin Qiao; Cornelis J. Potgieter; Joseph F. T. Nese – Educational and Psychological Measurement, 2024
Words read correctly per minute (WCPM) is the reporting score metric in oral reading fluency (ORF) assessments, which is popularly utilized as part of curriculum-based measurements to screen at-risk readers and to monitor progress of students who receive interventions. Just like other types of assessments with multiple forms, equating would be…
Descriptors: Oral Reading, Reading Fluency, Models, Reading Rate
Ferdinand Valentin Stoye; Claudia Tschammler; Oliver Kuss; Annika Hoyer – Research Synthesis Methods, 2024
The development of new statistical models for the meta-analysis of diagnostic test accuracy studies is still an ongoing field of research, especially with respect to summary receiver operating characteristic (ROC) curves. In the recently published updated version of the "Cochrane Handbook for Systematic Reviews of Diagnostic Test…
Descriptors: Diagnostic Tests, Accuracy, Barriers, Models
David Arthur; Hua-Hua Chang – Journal of Educational and Behavioral Statistics, 2024
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…
Descriptors: Algorithms, Models, Computation, Cognitive Measurement
Sheng Bi; Zeyi Miao; Qizhi Min – IEEE Transactions on Learning Technologies, 2025
The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder-decoder architectures have advanced traditional KGQG approaches. However, these…
Descriptors: Grammar, Models, Questioning Techniques, Graphs
Aiman Mohammad Freihat; Omar Saleh Bani Yassin – Educational Process: International Journal, 2025
Background/purpose: This study aimed to reveal the accuracy of estimation of multiple-choice test items parameters following the models of the item-response theory in measurement. Materials/methods: The researchers depended on the measurement accuracy indicators, which express the absolute difference between the estimated and actual values of the…
Descriptors: Accuracy, Computation, Multiple Choice Tests, Test Items