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Verdú, Elena; Regueras, Luisa M.; Gal, Eran; de Castro, Juan P.; Verdú, María J.; Kohen-Vacs, Dan – Educational Technology Research and Development, 2017
INTUITEL is a research project aiming to offer a personalized learning environment. The INTUITEL approach includes an Intelligent Tutoring System that gives students recommendations and feedback about what the best learning path is for them according to their profile, learning progress, context and environmental influences. INTUITEL combines…
Descriptors: Technology Integration, Intelligent Tutoring Systems, Computer Networks, Computer System Design
Jamet, Eric; Fernandez, Jonathan – Educational Technology Research and Development, 2016
The present study investigated whether learning how to use a web service with an interactive tutorial can be enhanced by cueing. We expected the attentional guidance provided by visual cues to facilitate the selection of information in static screen displays that corresponded to spoken explanations. Unlike most previous studies in this area, we…
Descriptors: Intelligent Tutoring Systems, Web Sites, Cues, Visual Stimuli
Matthew E. Jacovina; Erica L. Snow; G. Tanner Jackson; Danielle S. McNamara – Grantee Submission, 2015
To optimize the benefits of game-based practice within Intelligent Tutoring Systems (ITSs), researchers examine how game features influence students' motivation and performance. The current study examined the influence of game features and individual differences (reading ability and learning intentions) on motivation and performance. Participants…
Descriptors: Game Based Learning, Intelligent Tutoring Systems, Learning Motivation, Performance
Kusairi, Sentot; Alfad, Haritzah; Zulaikah, Siti – Journal of Turkish Science Education, 2017
Fluid statics is one of the most difficult topics for students to learn. Formative assessment and remedial instruction can help students master the concepts. However, identifying students' challenges for formative purposes and facilitating remedial learning is not easy given to the number of students and variation of the problems encountered. An…
Descriptors: Intelligent Tutoring Systems, Mastery Learning, Physics, Science Instruction
Luz, Bruno N.; Santos, Rafael; Alves, Bruno; Areão, Andreza S.; Yokoyama, Marcos H.; Guimarães, Marcelo P. – International Association for Development of the Information Society, 2015
The main purpose of this paper is to present the importance of Interactive Learning Objects (ILO) to improve the teaching-learning process by assuring a constant interaction among teachers and students, which in turn, allows students to be constantly supported by the teacher. The paper describes the ontology that defines the ILO available on the…
Descriptors: Resource Units, Metadata, Interaction, Learning Processes
Xiong, Xiaolu; Zhao, Siyuan; Van Inwegen, Eric G.; Beck, Joseph E. – International Educational Data Mining Society, 2016
Over the last couple of decades, there have been a large variety of approaches towards modeling student knowledge within intelligent tutoring systems. With the booming development of deep learning and large-scale artificial neural networks, there have been empirical successes in a number of machine learning and data mining applications, including…
Descriptors: Intelligent Tutoring Systems, Computer Software, Bayesian Statistics, Knowledge Level
McCarthy, Tessa; Rosenblum, L. Penny; Johnson, Benny G.; Dittel, Jeffrey; Kearns, Devin M. – Journal of Visual Impairment & Blindness, 2016
Introduction: This study evaluated the usability and effectiveness of an artificial intelligence Braille Tutor designed to supplement the instruction of students with visual impairments as they learned to write braille contractions. Methods: A mixed-methods design was used, which incorporated a single-subject, adapted alternating treatments design…
Descriptors: Intelligent Tutoring Systems, Supplementary Education, Assistive Technology, Braille
Santos, Olga Cristina, Ed.; Boticario, Jesus Gonzalez, Ed.; Romero, Cristobal, Ed.; Pechenizkiy, Mykola, Ed.; Merceron, Agathe, Ed.; Mitros, Piotr, Ed.; Luna, Jose Maria, Ed.; Mihaescu, Cristian, Ed.; Moreno, Pablo, Ed.; Hershkovitz, Arnon, Ed.; Ventura, Sebastian, Ed.; Desmarais, Michel, Ed. – International Educational Data Mining Society, 2015
The 8th International Conference on Educational Data Mining (EDM 2015) is held under auspices of the International Educational Data Mining Society at UNED, the National University for Distance Education in Spain. The conference held in Madrid, Spain, July 26-29, 2015, follows the seven previous editions (London 2014, Memphis 2013, Chania 2012,…
Descriptors: Data Analysis, Educational Research, Computer Uses in Education, Integrated Learning Systems
Gowda, Sujith M.; Baker, Ryan S.; Corbett, Albert T.; Rossi, Lisa M. – International Journal of Artificial Intelligence in Education, 2013
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning--learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions:…
Descriptors: Learning Processes, Transfer of Training, Outcomes of Education, Intelligent Tutoring Systems
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
Schmoelz, Alexander; Swertz, Christian; Forstner, Alexandra; Barberi, Alessandro – Science Education International, 2014
This contribution looks at the Intelligent Tutoring Interface for Technology Enhanced Learning, which integrates multistage-learning and inquiry-based learning in an adaptive e-learning system. Based on a common pedagogical ontology, adaptive e-learning systems can be enabled to recommend learning objects and activities, which follow inquiry-based…
Descriptors: Inquiry, Active Learning, Intelligent Tutoring Systems, Electronic Learning
Trevors, Gregory; Duffy, Melissa; Azevedo, Roger – Educational Technology Research and Development, 2014
Hypermedia learning environments (HLE) unevenly present new challenges and opportunities to learning processes and outcomes depending on learner characteristics and instructional supports. In this experimental study, we examined how one such HLE--MetaTutor, an intelligent, multi-agent tutoring system designed to scaffold cognitive and…
Descriptors: Notetaking, Intelligent Tutoring Systems, Hypermedia, Scaffolding (Teaching Technique)
MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…
Descriptors: Factor Analysis, Regression (Statistics), Knowledge Level, Markov Processes
Wan, Hao; Beck, Joseph Barbosa – International Educational Data Mining Society, 2015
The phenomenon of wheel spinning refers to students attempting to solve problems on a particular skill, but becoming stuck due to an inability to learn the skill. Past research has found that students who do not master a skill quickly tend not to master it at all. One question is why do students wheel spin? A plausible hypothesis is that students…
Descriptors: Skill Development, Problem Solving, Knowledge Level, Learning Processes

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