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Koji Osawa – RELC Journal: A Journal of Language Teaching and Research, 2024
With the recent rapid technological advance, second language (L2) educators have increasingly incorporated technologies into writing pedagogy. Two of the major technologies to promote L2 writing are e-portfolios and automated written corrective feedback (AWCF). Notably, feedback-rich portfolios facilitate L2 learners' self-regulation and writing…
Descriptors: Artificial Intelligence, Computer Software, Writing Instruction, Writing Evaluation
Almut Ketzer-Nöltge, Editor; Nicola Würffel, Editor – Peter Lang Publishing Group, 2024
For over four decades, textbooks have been enhanced with digital components, and today, it is almost impossible to find a textbook that does not contain any. Does this mean that textbooks have been fully digitalized and that we have reached a point where the integration of digital media into textbooks is the norm? Since there is no clear consensus…
Descriptors: Textbooks, Electronic Books, Computer Uses in Education, Educational History
Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – Grantee Submission, 2024
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
Loksa, Dastyni; Margulieux, Lauren; Becker, Brett A.; Craig, Michelle; Denny, Paul; Pettit, Raymond; Prather, James – ACM Transactions on Computing Education, 2022
Metacognition and self-regulation are important skills for successful learning and have been discussed and researched extensively in the general education literature for several decades. More recently, there has been growing interest in understanding how metacognitive and self-regulatory skills contribute to student success in the context of…
Descriptors: Metacognition, Programming, Computer Science Education, Learning Processes
Walter L. Leite; Samrat Roy; Nilanjana Chakraborty; George Michailidis; A. Corinne Huggins-Manley; Sidney K. D'Mello; Mohamad Kazem Shirani Faradonbeh; Emily Jensen; Huan Kuang; Zeyuan Jing – Grantee Submission, 2022
This study presents a novel video recommendation system for an algebra virtual learning environment (VLE) that leverages ideas and methods from engagement measurement, item response theory, and reinforcement learning. Following Vygotsky's Zone of Proximal Development (ZPD) theory, but considering low affect and high affect students separately, we…
Descriptors: Artificial Intelligence, Video Technology, Technology Uses in Education, Program Effectiveness
Munshi, Anabil; Biswas, Gautam; Baker, Ryan; Ocumpaugh, Jaclyn; Hutt, Stephen; Paquette, Luc – Journal of Computer Assisted Learning, 2023
Background: Providing adaptive scaffolds to help learners develop effective self-regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open-ended learning environments (OELE), where novice learners often face difficulties in completing their learning…
Descriptors: Scaffolding (Teaching Technique), Metacognition, Independent Study, Intelligent Tutoring Systems
Perrotta, Carlo; Selwyn, Neil – Learning, Media and Technology, 2020
In Applied AI, or 'machine learning', methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Teaching Methods, Online Courses
Tacoma, Sietske; Heeren, Bastiaan; Jeuring, Johan; Drijvers, Paul – International Journal of Artificial Intelligence in Education, 2020
Hypothesis testing involves a complex stepwise procedure that is challenging for many students in introductory university statistics courses. In this paper we assess how feedback from an Intelligent Tutoring System can address the logic of hypothesis testing and whether such feedback contributes to first-year social sciences students' proficiency…
Descriptors: Hypothesis Testing, Feedback (Response), Intelligent Tutoring Systems, Introductory Courses
Borracci, Giuliana; Gauthier, Erica; Jennings, Jay; Sale, Kyle; Muldner, Kasia – Journal of Educational Computing Research, 2020
We investigated the impact of assistance on learning and affect during problem-solving activities with a computer tutor we built using the Cognitive Tutor Authoring Tools framework. The tutor delivered its primary form of assistance in the form of worked-out examples. We manipulated the level of assistance the examples in the tutor provided, by…
Descriptors: Intelligent Tutoring Systems, Mathematics Instruction, Mathematics Education, Algebra
Chen, Xingliang; Mitrovic, Antonija; Mathews, Moffat – IEEE Transactions on Learning Technologies, 2020
Problem solving, worked examples, and erroneous examples have proven to be effective learning activities in Intelligent Tutoring Systems (ITSs). However, it is generally unknown how to select learning activities adaptively in ITSs to maximize learning. In the previous work of A. Shareghi Najar and A. Mitrovic, alternating worked examples with…
Descriptors: Problem Solving, Intelligent Tutoring Systems, Learning Activities, Educational Technology
Pavlik, Philip I., Jr.; Olney, Andrew M.; Banker, Amanda; Eglington, Luke; Yarbro, Jeffrey – Grantee Submission, 2020
An intelligent textbook may be considered to be an interaction layer that lies between the text and the student, helping the student to master the content in the text. The Mobile Fact and Concept Training System (MoFaCTS) is an adaptive instructional system for simple content that has been developed into an interaction layer to mediate textbook…
Descriptors: Textbooks, Intelligent Tutoring Systems, Electronic Learning, Instructional Design
Lee, Youngnam; Kim, Byungsoo; Shin, Dongmin; Kim, JungHoon; Baek, Jineon; Lee, Jinhwan; Choi, Youngduck – International Educational Data Mining Society, 2020
Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their…
Descriptors: Intelligent Tutoring Systems, Prediction, Scores, Learner Engagement
Linking Dialogue with Student Modelling to Create an Adaptive Tutoring System for Conceptual Physics
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
Scaffolded Self-Explanation with Visual Representations Promotes Efficient Learning in Early Algebra
Tomohiro Nagashima; Anna N. Bartel; Stephanie Tseng; Nicholas A. Vest; Elena M. Silla; Martha W. Alibali; Vincent Aleven – Grantee Submission, 2021
Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self- explanation, a novel form of instructional scaffolding in which visual representations are used to…
Descriptors: Visual Aids, Scaffolding (Teaching Technique), Mathematics Instruction, Algebra

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