NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 8 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
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
Peer reviewed Peer reviewed
Direct linkDirect link
Whalen, Andrew; Griffiths, Thomas L.; Buchsbaum, Daphna – Cognitive Science, 2018
Social learning has been shown to be an evolutionarily adaptive strategy, but it can be implemented via many different cognitive mechanisms. The adaptive advantage of social learning depends crucially on the ability of each learner to obtain relevant and accurate information from informants. The source of informants' knowledge is a particularly…
Descriptors: Social Development, Socialization, Bayesian Statistics, Behavior Patterns
Peer reviewed Peer reviewed
Direct linkDirect link
Leganes-Fonteneau, Mateo; Nikolaou, Kyriaki; Scott, Ryan; Duka, Theodora – Learning & Memory, 2019
Stimuli conditioned with a substance can generate drug-approach behaviors due to their acquired motivational properties. According to implicit theories of addiction, these stimuli can decrease cognitive control automatically. The present study (n = 49) examined whether reward-associated stimuli can interfere with cognitive processes in the absence…
Descriptors: Knowledge Level, Rewards, Conditioning, Bayesian Statistics
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Mao, Ye; Lin, Chen; Chi, Min – Journal of Educational Data Mining, 2018
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…
Descriptors: Prediction, Pretests Posttests, Bayesian Statistics, Short Term Memory
Peer reviewed Peer reviewed
Direct linkDirect link
Bramley, Neil R.; Lagnado, David A.; Speekenbrink, Maarten – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
Interacting with a system is key to uncovering its causal structure. A computational framework for interventional causal learning has been developed over the last decade, but how real causal learners might achieve or approximate the computations entailed by this framework is still poorly understood. Here we describe an interactive computer task in…
Descriptors: Intervention, Memory, Cognitive Processes, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Taylor, Eric G.; Ahn, Woo-kyoung – Cognitive Psychology, 2012
Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C…
Descriptors: Evidence, Prior Learning, Bayesian Statistics, Correlation
Peer reviewed Peer reviewed
Direct linkDirect link
Regier, Terry; Gahl, Susanne – Cognition, 2004
Syntactic knowledge is widely held to be partially innate, rather than learned. In a classic example, it is sometimes argued that children know the proper use of anaphoric "one," although that knowledge could not have been learned from experience. Lidz et al. [Lidz, J., Waxman, S., & Freedman, J. (2003). What infants know about syntax but couldn't…
Descriptors: Learning Processes, Syntax, Language Acquisition, Cognitive Development
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring