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Mihalca, Loredana; Mengelkamp, Christoph – Journal of Educational Psychology, 2020
Both accurate monitoring and adequate control are crucial for effective self-regulation when learning from problem-solving tasks. Prior research has shown that self-regulated learning is especially harmful for low prior knowledge students, given their difficulties with accurate monitoring and control decisions. Although many studies have indicated…
Descriptors: Problem Solving, Accuracy, Decision Making, Metacognition
Bongers, Amanda; Beauvoir, Berthorie; Streja, Nicholas; Northof, Georg; Flynn, Alison B. – Chemistry Education Research and Practice, 2020
In chemistry, novices and experts use mental models to simulate and reason about sub-microscopic processes. Animations are thus important tools for learning in chemistry to convey reaction dynamics and molecular motion. While there are many animations available and studies showing the benefit of learning from animations, there are also limitations…
Descriptors: Science Instruction, Schemata (Cognition), Scientific Concepts, Animation
Corradi, David; Clarebout, Geraldine; Elen, Jan – Journal of Science Education and Technology, 2015
Previous research on multiple external representations (MER) indicates that sequencing representations (compared with presenting them as a whole) can, in some cases, increase conceptual understanding if there is interference between internal and external representations. We tested this mechanism by sequencing different combinations of scientific…
Descriptors: Chemistry, Science Instruction, Prior Learning, Knowledge Level
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
Chounta, Irene-Angelica; Albacete, Patricia; Jordan, Pamela; Katz, Sandra; McLaren, Bruce M. – Grantee Submission, 2017
In this paper, we propose a computational approach to model the Zone of Proximal Development (ZPD) using predicted probabilities of correctness and engaging students in reflective dialogue. To that end, we employ a predictive model that uses a linear function of a variety of parameters, including difficulty and student knowledge and we analyze the…
Descriptors: Learning Theories, Sociocultural Patterns, Intelligent Tutoring Systems, Physics
Chounta, Irene-Angelica; McLaren, Bruce M.; Albacete, Patricia; Jordan, Pamela; Katz, Sandra – Grantee Submission, 2017
In this paper, we propose a computational approach to modeling the Zone of Proximal Development of students who learn using a natural language tutoring system for physics. We employ a student model that predicts students' performance based on their prior knowledge and their activity when using a dialogue tutor to practice with conceptual,…
Descriptors: Learning Theories, Sociocultural Patterns, Intelligent Tutoring Systems, Physics