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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
Nagashima, Tomohiro; Bartel, Anna N.; Silla, Elena M.; Vest, Nicholas A.; Alibali, Martha W.; Aleven, Vincent – Grantee Submission, 2020
Many studies have shown that visual representations can enhance student understanding of STEM concepts. However, prior research suggests that visual representations alone are not necessarily effective across a broad range of students. To address this problem, we created a novel, scaffolded form of diagrammatic self-explanation in which students…
Descriptors: Algebra, Teaching Methods, Visual Aids, Concept Formation
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
Rau, Martina A.; Aleven, Vincent; Rummel, Nikol – Instructional Science: An International Journal of the Learning Sciences, 2017
Prior research shows that representational competencies that enable students to use graphical representations to reason and solve tasks is key to learning in many science, technology, engineering, and mathematics domains. We focus on two types of representational competencies: (1) "sense making" of connections by verbally explaining how…
Descriptors: Elementary School Students, Grade 3, Grade 4, Grade 5
Rau, Martina A.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2017
Prior research shows that representational competencies that enable students to use graphical representations to reason and solve tasks is key to learning in many science, technology, engineering, and mathematics (STEM) domains. We focus on two types of representational competencies: (1) "sense making" of connections by verbally…
Descriptors: Elementary School Students, Grade 3, Grade 4, Grade 5
Olsen, Jennifer K.; Belenky, Daniel M.; Aleven, Vincent; Rummel, Nikol; Sewall, Jonathan; Ringenberg, Michael – Grantee Submission, 2013
Authoring tools for Intelligent Tutoring System (ITS) have been shown to decrease the amount of time that it takes to develop an ITS. However, most of these tools currently do not extend to collaborative ITSs. In this paper, we illustrate an extension to the Cognitive Tutor Authoring Tools (CTAT) to allow for development of collaborative ITSs that…
Descriptors: Intelligent Tutoring Systems, Programming Languages, Fractions, Learning Processes
Olsen, Jennifer K.; Rummel, Nikol; Aleven, Vincent – Grantee Submission, 2016
Intelligent Tutoring Systems (ITSs) are beneficial for individual students learning in several domains, including mathematics where they have been used to support both secondary and elementary students. Collaborative learning may be beneficial to include in ITSs, particularly for conceptual knowledge. There is little work on collaborative ITSs,…
Descriptors: Intelligent Tutoring Systems, Cooperative Learning, Elementary School Students, Elementary School Mathematics
Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma – Grantee Submission, 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: Sequential Learning, Data Collection, Information Retrieval, Evaluation Methods
Roll, Ido; Baker, Ryan S. J. d.; Aleven, Vincent; Koedinger, Kenneth R. – Journal of the Learning Sciences, 2014
Seeking the right level of help at the right time can support learning. However, in the context of online problem-solving environments, it is still not entirely clear which help-seeking strategies are desired. We use fine-grained data from 38 high school students who worked with the Geometry Cognitive Tutor for 2 months to better understand the…
Descriptors: Help Seeking, Comparative Analysis, Behavior Patterns, Intelligent Tutoring Systems
Bernacki, Matthew L.; Nokes-Malach, Timothy J.; Aleven, Vincent – Metacognition and Learning, 2015
Self-regulated learning (SRL) theorists propose that learners' motivations and cognitive and metacognitive processes interact dynamically during learning, yet researchers typically measure motivational constructs as stable factors. In this study, self-efficacy was assessed frequently to observe its variability during learning and how learners'…
Descriptors: Self Efficacy, Metacognition, Cues, Algebra
Aleven, Vincent; Roll, Ido; McLaren, Bruce M.; Koedinger, Kenneth R. – Educational Psychologist, 2010
Assessment of students' self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in theEv specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, help seeking while…
Descriptors: Feedback (Response), Help Seeking, Intelligent Tutoring Systems, Tutoring

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