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Mary Ann Simpson; Kole A. Norberg; Stephen E. Fancsali – International Educational Data Mining Society, 2024
In a broad analysis of a large, diverse sample of students, we found robust support for the groundbreaking assertion that student learning rates in various educational technologies are "astonishingly" similar (Koedinger, Carvalho, Liu, & McLaughlin, 2023, "An astonishing regularity in student learning rate," Proceedings of…
Descriptors: Educational Technology, Learning, Replication (Evaluation), Intelligent Tutoring Systems
Valentina Grion; Juliana Raffaghelli; Beatrice Doria; Anna Serbati – Educational Research and Evaluation, 2024
Feedback is crucial for improving student learning. In this regard, overcoming the transmissive conception of feedback in favour of its dialogic function introduces new reflections concerning the internal generative feedback process. In this regard, Nicol [(2020). The power of internal feedback: Exploiting natural comparator processes.…
Descriptors: Student Attitudes, Self Evaluation (Individuals), Feedback (Response), Individual Differences
Inan, Fethi Ahmet; Ari, Fatih; Flores, Raymond; Zaier, Amani; Arslan-Ari, Ismahan – International Journal on E-Learning, 2021
This study explored the effectiveness of an adaptive web-based learning tutorial designed to teach three modules of a college level introductory statistics course. Specifically, the impact of the tutorial on student knowledge, motivation, and study time was examined. One hundred thirty four college students were randomly assigned to study from…
Descriptors: Web Based Instruction, Instructional Effectiveness, College Students, Introductory Courses
Heck, Tanja; Meurers, Detmar; Nuxoll, Florian – Research-publishing.net, 2022
Foreign language teaching achieves best learning outcomes when individual differences of learners are taken into account. While it is difficult for teachers to support internal differentiation in the classroom, digital tools can adaptively propose individual learning paths through activities so that students can practice with appropriately…
Descriptors: Intelligent Tutoring Systems, Outcomes of Education, Second Language Learning, Second Language Instruction
Taub, Michelle; Azevedo, Roger – International Journal of Artificial Intelligence in Education, 2019
The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students' (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the…
Descriptors: Prior Learning, Eye Movements, Metacognition, Learning Processes
Sense, Florian; van der Velde, Maarten; van Rijn, Hedderik – Journal of Learning Analytics, 2021
Modern educational technology has the potential to support students to use their study time more effectively. Learning analytics can indicate relevant individual differences between learners, which adaptive learning systems can use to tailor the learning experience to individual learners. For fact learning, cognitive models of human memory are…
Descriptors: Predictor Variables, Undergraduate Students, Learning Analytics, Cognitive Psychology
Ibili, Emin; Çat, Mevlüt; Resnyansky, Dmitry; Sahin, Sami; Billinghurst, Mark – International Journal of Mathematical Education in Science and Technology, 2020
The aim of this research was to examine the effect of Augmented Reality (AR) supported geometry teaching on students' 3D thinking skills. This research consisted of 3 steps: (1) developing a 3D thinking ability scale, (ii) design and development of an AR Geometry Tutorial System (ARGTS) and (iii) implementation and assessment of geometry teaching…
Descriptors: Geometry, Thinking Skills, Mathematics Instruction, Pretests Posttests
Eagle, Michael; Corbett, Albert; Stamper, John; Mclaren, Bruce – International Educational Data Mining Society, 2018
In this work we use prior to tutor-session data to generate an individualized student knowledge model. Intelligent learning environments use student models to individualize curriculum sequencing and help messages. Researchers decompose the learning tasks into sets of Knowledge Components (KCs) that represent individual units of knowledge; the…
Descriptors: Individualized Instruction, Models, Data Analysis, Knowledge Level
Ayedoun, Emmanuel; Hayashi, Yuki; Seta, Kazuhisa – IEEE Transactions on Learning Technologies, 2020
This article proposes a computer-based approach to effectively enhance second language learners' willingness to communicate in the target language. To do so, we implemented a conversational agent embedding a dialogue management model based on two conversational strategies (i.e., communication strategies and affective backchannels), serving as…
Descriptors: Scaffolding (Teaching Technique), Teaching Methods, Second Language Learning, Second Language Instruction
Clement, Benjamin; Oudeyer, Pierre-Yves; Lopes, Manuel – International Educational Data Mining Society, 2016
Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally…
Descriptors: Intelligent Tutoring Systems, Markov Processes, Models, Teaching Methods
Rastegarmoghadam, Mahin; Ziarati, Koorush – Education and Information Technologies, 2017
Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…
Descriptors: Teaching Methods, Problem Solving, Intelligent Tutoring Systems, Educational Technology
Allen, Laura K.; Mills, Caitlin; Jacovina, Matthew E.; Crossley, Scott; D'Mello, Sidney; McNamara, Danielle S. – Grantee Submission, 2016
Writing training systems have been developed to provide students with instruction and deliberate practice on their writing. Although generally successful in providing accurate scores, a common criticism of these systems is their lack of personalization and adaptive instruction. In particular, these systems tend to place the strongest emphasis on…
Descriptors: Learner Engagement, Psychological Patterns, Writing Instruction, Essays
Snow, Erica L.; Jackson, G. Tanner; Varner, Laura K.; McNamara, Danielle S. – Grantee Submission, 2013
Research on individual differences indicates that students vary in how they interact with and perform while using intelligent tutoring systems (ITSs). However, less research has investigated how individual differences affect students' interactions with game-based features. This study examines how learning outcomes and interactions with specific…
Descriptors: Individual Differences, Academic Achievement, Intelligent Tutoring Systems, Educational Games
Erica L. Snow; Mathew E. Jacovina; Laura K. Allen; Jianmin Dai; Danielle S. McNamara – Grantee Submission, 2014
This study investigates variations in how users exert agency and control over their choice patterns within the game-based ITS, iSTART-2, and how these individual differences relate to performance. Seventy-six college students interacted freely with iSTART-2 for approximately 2 hours. The current work captures and classifies variations in students'…
Descriptors: Personal Autonomy, College Students, Individual Differences, Game Based Learning
Weston, Jennifer L.; McNamara, Danielle S. – Grantee Submission, 2013
Intelligent tutoring systems yield data with many properties that render it potentially ideal to examine using multi-level models (MLM). Repeated observations with dependencies may be optimally examined using MLM because it can account for deviations from normality. This paper examines the applicability of MLM to data from the intelligent tutoring…
Descriptors: Intelligent Tutoring Systems, Hierarchical Linear Modeling, Correlation, Writing Instruction
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