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Showing 1 to 15 of 22 results Save | Export
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Dang, Steven C.; Koedinger, Kenneth R. – International Educational Data Mining Society, 2020
Effective teachers recognize the importance of transitioning students into learning activities for the day and accounting for the natural drift of student attention while creating lesson plans. In this work, we analyze temporal patterns of gaming behaviors during work on an intelligent tutoring system with a broader goal of detecting temporal…
Descriptors: Learner Engagement, Intelligent Tutoring Systems, Student Behavior, Student Motivation
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Dang, Steven; Koedinger, Ken – International Educational Data Mining Society, 2019
A student's ability to regulate their thoughts, emotions and behaviors in the face of temptation is linked to their task specific motivational goals and dispositions. Behavioral tasks are designed to strain a targeted resource to differentiate individuals through measures of their performance. In this paper, we explore how student behavior on…
Descriptors: Correlation, Self Management, Student Motivation, Student Behavior
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Weitekamp, Daniel, III.; Harpstead, Erik; MacLellan, Christopher J.; Rachatasumrit, Napol; Koedinger, Kenneth R. – International Educational Data Mining Society, 2019
Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners' performance in…
Descriptors: Computation, Models, Learning, Prediction
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Delahay, Anita; Lovett, Marsha; Anderson, David; Sen, Surajit – Physical Review Physics Education Research, 2023
The preparation gap [Salehi et al., Phys. Rev. Phys. Educ. Res. 15, 020114 (2019)] refers to gaps in students' prior knowledge that can negatively affect their learning as they engage in introductory physics courses. To better characterize the gap, the current study distinguished the impact of various prior knowledge components on learning gains.…
Descriptors: Physics, Science Instruction, Private Colleges, Calculus
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Chen, Lujie Karen; Ramsey, Joseph; Dubrawski, Artur – Journal of Educational Data Mining, 2021
Human one-on-one coaching involves complex multimodal interactions. Successful coaching requires teachers to closely monitor students' cognitive-affective states and provide support of optimal type, timing, and amount. However, most of the existing human tutoring studies focus primarily on verbal interactions and have yet to incorporate the rich…
Descriptors: Causal Models, Coaching (Performance), Statistical Analysis, Correlation
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Eng, Cassondra M.; Tomasic, Anthony S.; Thiessen, Erik D. – Developmental Psychology, 2020
Experiences of contingent responsivity during shared book reading predict better learning outcomes. However, it is unclear whether contingent responsivity from a digital book could provide similar support for children. The effects on story recall and engagement interacting with a digital book that responded contingently on children's vocalizations…
Descriptors: Books, Electronic Publishing, Recall (Psychology), Individual Differences
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Zhang, Chuankai; Huang, Yanzun; Wang, Jingyu; Lu, Dongyang; Fang, Weiqi; Stamper, John; Fancsali, Stephen; Holstein, Kenneth; Aleven, Vincent – International Educational Data Mining Society, 2019
"Wheel spinning" is the phenomenon in which a student fails to master a Knowledge Component (KC), despite significant practice. Ideally, an intelligent tutoring system would detect this phenomenon early, so that the system or a teacher could try alternative instructional strategies. Prior work has put forward several criteria for wheel…
Descriptors: Identification, Intelligent Tutoring Systems, Academic Failure, Criteria
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Eng, Cassondra M.; Pocsai, Melissa; Fulton, Virginia E.; Moron, Suanna P.; Thiessen, Erik D.; Fisher, Anna V. – Developmental Science, 2022
Increased focus on resting-state functional connectivity (rsFC) and the use and accessibility of functional near-infrared spectroscopy (fNIRS) have advanced knowledge on the interconnected nature of neural substrates underlying executive function (EF) development in adults and clinical populations. Less is known about the relationship between rsFC…
Descriptors: Longitudinal Studies, Executive Function, Cognitive Development, Preschool Children
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Martella, Amedee Marchand; Klahr, David; Li, Weiling – Journal of Educational Psychology, 2020
"Active learning" has been used to describe classrooms that have varied widely with respect to instructional topics, age of learners, and the procedures used to operationalize the general notion of the term. In most cases, the specific variant of active learning under investigation has been more effective than the particular control used…
Descriptors: Elementary School Students, Grade 3, Grade 4, Design
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Doroudi, Shayan; Brunskill, Emma – International Educational Data Mining Society, 2017
In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different…
Descriptors: Bayesian Statistics, Research Problems, Models, Learning
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Madaio, Michael; Lasko, Rae; Ogan, Amy; Cassell, Justine – International Educational Data Mining Society, 2017
Social relationships, such as interpersonal closeness or rapport, can lead to improved student learning, but such dynamic, interpersonal phenomena can be difficult for educational support technologies to detect. In this paper, we describe an approach for rapport detection in peer tutoring, using temporal association rules learned from nonverbal,…
Descriptors: Peer Teaching, Tutoring, Peer Relationship, Time
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Doroudi, Shayan; Aleven, Vincent; Brunskill, Emma – International Journal of Artificial Intelligence in Education, 2019
Since the 1960s, researchers have been trying to optimize the sequencing of instructional activities using the tools of reinforcement learning (RL) and sequential decision making under uncertainty. Many researchers have realized that reinforcement learning provides a natural framework for optimal instructional sequencing given a particular model…
Descriptors: Reinforcement, Learning Processes, Sequential Learning, Decision Making
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Zhang, Chuankai; Huang, Yanzun; Wang, Jingyu; Lu, Dongyang; Fang, Weiqi; Stamper, John; Fancsali, Stephen; Holstein, Kenneth; Aleven, Vincent – Grantee Submission, 2019
"Wheel spinning" is the phenomenon in which a student fails to master a Knowledge Component (KC), despite significant practice. Ideally, an intelligent tutoring system would detect this phenomenon early, so that the system or a teacher could try alternative instructional strategies. Prior work has put forward several criteria for wheel…
Descriptors: Identification, Intelligent Tutoring Systems, Academic Failure, Criteria
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Holstein, Kenneth; McLaren, Bruce M.; Aleven, Vincent – Grantee Submission, 2019
As artificial intelligence (AI) increasingly enters K-12 classrooms, what do teachers and students see as the roles of human versus AI instruction, and how might educational AI (AIED) systems best be designed to support these complementary roles? We explore these questions through participatory design and needs validation studies with K12 teachers…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Instructional Design, Elementary Secondary Education
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Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma – International Educational Data Mining Society, 2016
How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint…
Descriptors: Intelligent Tutoring Systems, Sequential Approach, Problem Solving, Learning Processes
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