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Sun, Jerry Chih-Yuan; Yu, Shih-Jou; Chao, Chih-Hsuan – Educational Psychology, 2019
The current study developed an intelligent learning environment for online education of research ethics and investigated how encouragement and warning intelligent feedback influenced learners' engagement (behavioural, emotional, and cognitive) and cognitive load (mental load and mental effort). Participants included 191 graduate students in Taiwan…
Descriptors: Feedback (Response), Learner Engagement, Cognitive Processes, Difficulty Level
Olsen, Jennifer K.; Rummel, Nikol; Aleven, Vincent – International Journal of Computer-Supported Collaborative Learning, 2019
Research on Computer-Supported Collaborative Learning (CSCL) has provided significant insights into why collaborative learning is effective and how we can effectively provide support for it. Building on this knowledge, we can investigate when collaboration is beneficial to support learning. Specifically, collaborative and individual learning are…
Descriptors: Cooperative Learning, Computer Assisted Instruction, Educational Technology, Intelligent Tutoring Systems
Song, Donggil; Rice, Marilyn; Oh, Eun Young – International Review of Research in Open and Distributed Learning, 2019
Online learning environments could be well understood as a multifaceted phenomenon affected by different aspects of learner participation including synchronous/asynchronous interactions. The aim of this study was to investigate learners' participation in online courses, synchronous interaction with a conversational virtual agent, their…
Descriptors: Online Courses, Educational Technology, Technology Uses in Education, Interaction
Rozo, Hugo; Real, Miguel – Journal of Technology and Science Education, 2019
The present article constitutes a systematic review of the literature with the objective of identifying the appropriate elements that must be considered when designing and creating adaptive digital educational resources. The methodological process was rigorous and systematic, employing an article search in which the texts related to the object of…
Descriptors: Instructional Design, Intelligent Tutoring Systems, Instructional Materials, Educational Technology
Harmon, Jon; Warnakulasooriya, Rasil – International Educational Data Mining Society, 2019
The Additive Factor Model (AFM) is a cognitive diagnostic model that can be used to predict student performance on items in a context that allows for student learning. Within AFM, "skills" have a learning rate, and student acquisition of a skill depends only on the number of opportunities a student has had to exercise that skill and the…
Descriptors: Electronic Learning, Factor Analysis, Goodness of Fit, Item Response Theory
Dang, Steven; Koedinger, Ken – International Educational Data Mining Society, 2019
A student's ability to regulate their thoughts, emotions and behaviors in the face of temptation is linked to their task specific motivational goals and dispositions. Behavioral tasks are designed to strain a targeted resource to differentiate individuals through measures of their performance. In this paper, we explore how student behavior on…
Descriptors: Correlation, Self Management, Student Motivation, Student Behavior
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
McCarthy, Kathryn S.; Roscoe, Rod D.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2019
This study investigated the effect of incorporating spelling and grammar checking tools within an automated writing tutoring system, Writing Pal. High school students (n = 119) wrote and revised six persuasive essays. After initial drafts, all students received formative feedback about writing strategies. Half of the participants were also given…
Descriptors: Spelling, Grammar, Automation, Writing Instruction
Lippert, Anne; Gatewood, Jessica; Cai, Zhiqiang; Graesser, Arthur C. – Grantee Submission, 2019
One out of six adults in the United States possesses low literacy skills. Many advocates believe that technology can pave the way for these adults to gain the skills that they desire. This article describes an adaptive intelligent tutoring system called AutoTutor that is designed to teach adults comprehension strategies across different levels of…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Adult Literacy, Skill Development
Saastamoinen, Kalle; Rissanen, Antti – International Baltic Symposium on Science and Technology Education, 2019
Conventional learning guidance systems are typically automated machines for creating teaching materials: quizzes, exercises, examinations etc. In the future, systems will also offer ease of use, attention to sociality, ability to adapt to the pupil's needs and skill levels, and time savings. Ease-of-use and adaptation can be sought using systems…
Descriptors: Teaching Methods, Intelligent Tutoring Systems, Artificial Intelligence, Usability
Smith, E. Halle; Hollander, John; Graesser, Art C.; Sabatini, John; Hu, Xiangen – English Teaching, 2021
Facing the demands of the pandemic and distance learning, English learners require educational technologies that are accessible, engaging, and effective. Meeting these demands requires educational technology developers to consider learners' sociocultural contexts. Learning theories can be applied to meet individuals' needs to optimize chances for…
Descriptors: Reading Comprehension, English (Second Language), Second Language Learning, Second Language Instruction
Ruiqi Shen – ProQuest LLC, 2021
With the large demand for technology workers all around the world, more people are learning programming. Studies show that human tutoring is the most effective way to learn for novice programmers. However, problems such as the inaccessibility to physical classes, prohibitive costs, and the lack of educators may limit students' opportunities to…
Descriptors: MOOCs, Online Systems, Interactive Video, Computer Assisted Instruction
Tzafilkou, Katerina; Protogeros, Nicolaos – European Educational Researcher, 2020
This study investigates students' mouse behavior during their interaction with a web-based experiential learning environment for Computer Science courses. The research focuses on the detection of correlations between the monitored mouse metrics and students' technology acceptance items of perceived usefulness and ease of use. Findings reveal…
Descriptors: Electronic Learning, Learning Analytics, Student Attitudes, Usability
Haridas, Mithun; Gutjahr, Georg; Raman, Raghu; Ramaraju, Rudraraju; Nedungadi, Prema – Education and Information Technologies, 2020
In many rural Indian schools, English is a second language for teachers and students. Intelligent tutoring systems have good potential because they enable students to learn at their own pace, in an exploratory manner. This paper describes a 3-year longitudinal study of 2123 Indian students who used the intelligent tutoring system, AmritaITS. The…
Descriptors: Foreign Countries, Predictor Variables, English (Second Language), Second Language Learning
Turkmen, Gamze; Caner, Sonay – Turkish Online Journal of Distance Education, 2020
This study aims to provide a comprehensive and in-depth investigation of the debugging process in programming teaching in terms of cognitive and metacognitive aspects, based on programming students who demonstrate low, medium, and high programming performance and to propose instructional strategies for scaffolding novice learners in an effective…
Descriptors: Programming, Novices, Electronic Learning, Troubleshooting

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