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Walker, Andrew; Belland, Brian R.; Kim, Nam Ju; Lefler, Mason – AERA Online Paper Repository, 2017
Baeysian Network Meta-Analysis represents a rather unique challenge in assessing the quality of included studies. Prior efforts to synthesize computer based scaffolding are in need of a closer examination of research quality. This study examines two quality metrics for meta-analysis, study design, and risk of bias (Higgins et al., 2011). Lower…
Descriptors: Scaffolding (Teaching Technique), STEM Education, Research Design, Risk
Py, Dominique; Després, Christophe; Jacoboni, Pierre – Technology, Instruction, Cognition and Learning, 2015
Although providing open learner models to teachers and learners has proven effective, building accurate learner models remains a very complex task, partly due to the large amount of data that must be analyzed. We propose a method for specifying an open learner model at the conceptual level. This model re-uses constraints or indicators already…
Descriptors: Open Education, Models, Design, Programming Languages
Matsuda, Noboru; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Simulation, Models
Westerfield, Giles; Mitrovic, Antonija; Billinghurst, Mark – International Journal of Artificial Intelligence in Education, 2015
We investigate the combination of Augmented Reality (AR) with Intelligent Tutoring Systems (ITS) to assist with training for manual assembly tasks. Our approach combines AR graphics with adaptive guidance from the ITS to provide a more effective learning experience. We have developed a modular software framework for intelligent AR training…
Descriptors: Intelligent Tutoring Systems, Computer Simulation, Computer Science Education, Instructional Effectiveness
Hall, Lynne; Tazzyman, Sarah; Hume, Colette; Endrass, Birgit; Lim, Mei-Yii; Hofstede, GertJan; Paiva, Ana; Andre, Elisabeth; Kappas, Arvid; Aylett, Ruth – International Journal of Artificial Intelligence in Education, 2015
Providing opportunities for children to engage with intercultural learning has frequently focused on exposure to the ritual, celebrations and festivals of cultures, with the view that such experiences will result in greater acceptance of cultural differences. Intercultural conflict is often avoided, bringing as it does particular pedagogical,…
Descriptors: Multicultural Education, Intelligent Tutoring Systems, Experiential Learning, Children
Ostrow, Korinn; Donnelly, Christopher; Adjei, Seth; Heffernan, Neil – Grantee Submission, 2015
Student modeling within intelligent tutoring systems is a task largely driven by binary models that predict student knowledge or next problem correctness (i.e., Knowledge Tracing (KT)). However, using a binary construct for student assessment often causes researchers to overlook the feedback innate to these platforms. The present study considers a…
Descriptors: Intelligent Tutoring Systems, Models, Difficulty Level, Scores
Mirzaei, Maryam Sadat; Meshgi, Kourosh – Research-publishing.net, 2019
Many language learners have difficulty practicing listening skills using authentic materials, and thus use captions to map text with speech, and they benefit from reading along while listening to comprehend content. However, many learners over-rely on reading the text and many have difficulty in dividing their attention to the multimodal input. We…
Descriptors: Visual Aids, Second Language Learning, Second Language Instruction, Listening Skills
Aravind, Vasudeva Rao; McConnell, Marcella Kay – World Journal on Educational Technology: Current Issues, 2018
Educating our future citizens in science and engineering is vitally important to ensure future advancement. Presently, in the light of environmental sustainability, it is critical that students learn concepts relating to energy, its consumption and future demands. In this article, we harness the state of the educational technology, namely…
Descriptors: Intelligent Tutoring Systems, Science Instruction, Energy, Instructional Design
Huang, Jin-Xia; Kwon, Oh-Woog; Lee, Kyung-Soon; Kim, Young-Kil – Research-publishing.net, 2018
This paper presents a chatbot for a Dialogue-Based Computer Assisted Language Learning (DB-CALL) system. The chatbot helps users learn language via free conversations. To improve the chatbot performance, this paper adopts a Neural Machine Translation (NMT) engine to combine with an existing search-based engine, and also extracts a small domain…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Computer Mediated Communication
McCarthy, Kathryn S.; Soto, Christian Marcelo; Gutierrez de Blume, Antonio P.; Palma, Diego; González, Jordan Ignacio; McNamara, Danielle S. – International Journal of Computer-Assisted Language Learning and Teaching, 2020
iSTART-E is a web-based intelligent tutor developed for Spanish-speaking students to improve their reading comprehension through self-explanation strategy training. This study examined the effects of a blended comprehension strategy intervention on students' reading comprehension skill. Chilean high school students (N = 22) completed nine iSTART-E…
Descriptors: Reading Comprehension, High School Students, Computer Assisted Instruction, Teaching Methods
Kim, Yanghee; Baylor, Amy L. – International Journal of Artificial Intelligence in Education, 2016
In this paper we review the contribution of our original work titled "Simulating Instructional Roles Through Pedagogical Agents" published in the "International Journal of Artificial Intelligence and Education" (Baylor and Kim in "Computers and Human Behavior," 25(2), 450-457, 2005). Our original work operationalized…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Computer Interfaces, Instructional Design
McManus, Margaret M.; Aiken, Robert M. – International Journal of Artificial Intelligence in Education, 2016
Our original research, to design and develop an Intelligent Collaborative Learning System (ICLS), yielded the creation of a Group Leader Tutor software system which utilizes a Collaborative Skills Network to monitor students working collaboratively in a networked environment. The Collaborative Skills Network was a conceptualization of…
Descriptors: Cooperative Learning, Artificial Intelligence, Intelligent Tutoring Systems, Sentences
George, Sébastien; Michel, Christine; Ollagnier-Beldame, Magali – Interactive Learning Environments, 2016
During learning activities, reflexive processes allow learners to realise what they have done, understand why, decide on new actions and gain motivation. They help learners to regulate their actions by themselves, that is, to develop metacognitive regulation skills. Computer environments can support reflexive processes to support human learning,…
Descriptors: Reflection, Metacognition, Technology Uses in Education, Educational Technology
Bull, Susan – Research and Practice in Technology Enhanced Learning, 2016
Today's technology-enabled learning environments are becoming quite different from those of a few years ago, with the increased processing power as well as a wider range of educational tools. This situation produces more data, which can be fed back into the learning process. Open learner models have already been investigated as tools to promote…
Descriptors: Educational Technology, Electronic Learning, Models, Computer Assisted Instruction
Fouh, Eric; Farghally, Mohammed; Hamouda, Sally; Koh, Kyu Han; Shaffer, Clifford A. – International Educational Data Mining Society, 2016
We present an analysis of log data from a semester's use of the OpenDSA eTextbook system with the goal of determining the most difficult course topics in a data structures course. While experienced instructors can identify which topics students most struggle with, this often comes only after much time and effort, and does not provide real-time…
Descriptors: Item Response Theory, Data Analysis, Mathematics, Intelligent Tutoring Systems

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