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González-Esparza, Lydia Marion; Jin, Hao-Yue; Lu, Chang; Cutumisu, Maria – AERA Online Paper Repository, 2022
Detecting wheel-spinning behaviors of students who interact with an Intelligent Tutoring System (ITS) is important for generating pertinent and effective feedback and developing more enriching learning experiences. This analysis compares decision tree and bagged tree models of student productive persistence (i.e., mastering a skill) using the…
Descriptors: Student Behavior, Intelligent Tutoring Systems, Feedback (Response), Persistence
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Husni Almoubayyed; Stephen E. Fancsali; Steve Ritter – International Educational Data Mining Society, 2023
Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond…
Descriptors: Prediction, Models, Reading Ability, Computer Software
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Mark Wilson; Kathleen Scalise; Perman Gochyyev – Educational Psychology, 2019
In this article, we describe a software system for assessment development in online learning environments in contexts where there are robust links to cognitive modelling including domain and student modelling. BEAR Assessment System Software (BASS) establishes both a theoretical basis for the domain modelling logic, and offers tools for delivery,…
Descriptors: Computer Software, Electronic Learning, Test Construction, Intelligent Tutoring Systems
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Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
The gold-standard for evaluating the effect of an educational intervention on student outcomes is running a randomized controlled trial (RCT). However, RCTs may often be small due to logistical considerations, and resulting treatment effect estimates may lack precision. Recent methods improve experimental precision by incorporating information…
Descriptors: Intervention, Outcomes of Education, Randomized Controlled Trials, Data Use
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Katz, Sandra; Albacete, Patricia; Chounta, Irene-Angelica; Jordan, Pamela; McLaren, Bruce M.; Zapata-Rivera, Diego – International Journal of Artificial Intelligence in Education, 2021
Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However,…
Descriptors: Physics, Science Instruction, Pretests Posttests, Scores
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Albacete, Patricia; Jordan, Pamela; Lusetich, Dennis; Katz, Sandra; Chounta, Irene-Angelica; McLaren, Bruce M. – Grantee Submission, 2018
This paper discusses how a dialogue-based tutoring system makes decisions to proactively scaffold students during conceptual discussions about physics. The tutor uses a student model to predict the likelihood that the student will answer the next question in a dialogue script correctly. Based on these predictions, the tutor will, step by step,…
Descriptors: Intelligent Tutoring Systems, Scaffolding (Teaching Technique), Physics, Science Instruction
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Nguyen, Huy; Wang, Yeyu; Stamper, John; McLaren, Bruce M. – International Educational Data Mining Society, 2019
Knowledge components (KCs) define the underlying skill model of intelligent educational software, and they are critical to understanding and improving the efficacy of learning technology. In this research, we show how learning curve analysis is used to fit a KC model--one that was created after use of the learning technology--which can then be…
Descriptors: Middle School Students, Knowledge Representation, Models, Computer Games
Michelle P. Banawan; Jinnie Shin; Tracy Arner; Renu Balyan; Walter L. Leite; Danielle S. McNamara – Grantee Submission, 2023
Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse…
Descriptors: Algebra, Discourse Analysis, Semantics, Syntax
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Timms, Michael; DeVelle, Sacha; Lay, Dulce – Australian Journal of Education, 2016
It is well known that learners using intelligent learning environments make different use of the feedback provided by the intelligent learning environment and exhibit different patterns of behaviour. Traditional approaches to measuring such behaviour have focused on observational methods, think-aloud protocols, ratings and log data. More recently,…
Descriptors: Feedback (Response), Learning Processes, Intelligent Tutoring Systems, Models
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Wetzel, Jon; VanLehn, Kurt; Butler, Dillan; Chaudhari, Pradeep; Desai, Avaneesh; Feng, Jingxian; Grover, Sachin; Joiner, Reid; Kong-Sivert, Mackenzie; Patade, Vallabh; Samala, Ritesh; Tiwari, Megha; van de Sande, Brett – Interactive Learning Environments, 2017
This paper describes Dragoon, a simple intelligent tutoring system which teaches the construction of models of dynamic systems. Modelling is one of seven practices dictated in two new sets of educational standards in the U.S.A., and Dragoon is one of the first systems for teaching model construction for dynamic systems. Dragoon can be classified…
Descriptors: Intelligent Tutoring Systems, Models, Computer Interfaces, Comparative Analysis
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Zheng, Guoguo; Fancsali, Stephen E.; Ritter, Steven; Berman, Susan R. – Journal of Learning Analytics, 2019
If we wish to embed assessment for accountability within instruction, we need to better understand the relative contribution of different types of learner data to statistical models that predict scores and discrete achievement levels on assessments used for accountability purposes. The present work scales up and extends predictive models of math…
Descriptors: Formative Evaluation, Predictor Variables, Summative Evaluation, Scores
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Albacete, Patricia; Silliman, Scott; Jordan, Pamela – Grantee Submission, 2017
Intelligent tutoring systems (ITS), like human tutors, try to adapt to student's knowledge level so that the instruction is tailored to their needs. One aspect of this adaptation relies on the ability to have an understanding of the student's initial knowledge so as to build on it, avoiding teaching what the student already knows and focusing on…
Descriptors: Intelligent Tutoring Systems, Knowledge Level, Multiple Choice Tests, Computer Assisted Testing
Sahba Akhavan Niaki – ProQuest LLC, 2018
The increasing amount of available subjective text data in internet such as product reviews, movie critiques and social media comments provides golden opportunities for information retrieval researchers to extract useful information out of such datasets. Topic modeling and sentiment analysis are two widely researched fields that separately try to…
Descriptors: Models, Classification, Content Analysis, Documentation
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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
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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
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