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Peer reviewed
Download full textTärning, Betty; Silvervarg, Annika – Education Sciences, 2019
How should a pedagogical agent in educational software be designed to support student learning? This question is complex seeing as there are many types of pedagogical agents and design features, and the effect on different student groups can vary. In this paper we explore the effects of designing a pedagogical agent's self-efficacy in order to see…
Descriptors: Intelligent Tutoring Systems, Self Efficacy, Educational Games, Student Attitudes
Direct linkRathod, Balraj B.; Murthy, Sahana; Bandyopadhyay, Subhajit – Journal of Chemical Education, 2019
"Is this solution pink enough?" is a persistent question when it comes to phenolphthalein-based titration experiments, one that budding, novice scientists often ask their instructors. Lab instructors usually answer the inquiry with remarks like, "Looks like you have overshot the end point", "Perhaps you should check the…
Descriptors: Handheld Devices, Telecommunications, Chemistry, Intelligent Tutoring Systems
Roscoe, Rod D.; Allen, Laura K.; McNamara, Danielle S. – Journal of Educational Computing Research, 2019
A critical challenge for computer-based writing instruction is providing appropriate and adaptive practice. The current study examined three modes of computer-based writing practice with the goal of identifying those with the greatest learning and motivational value. High school students learned about writing strategies by studying lessons within…
Descriptors: High School Students, Writing Instruction, Computer Assisted Instruction, Writing Strategies
Keuning, Hieke; Jeuring, Johan; Heeren, Bastiaan – ACM Transactions on Computing Education, 2019
Formative feedback, aimed at helping students to improve their work, is an important factor in learning. Many tools that offer programming exercises provide automated feedback on student solutions. We have performed a systematic literature review to find out what kind of feedback is provided, which techniques are used to generate the feedback, how…
Descriptors: Programming, Teaching Methods, Computer Science Education, Feedback (Response)
Hutt, Stephen; Grafsgaard, Joseph F.; D'Mello, Sidney K. – Grantee Submission, 2019
We developed generalizable affect detectors using 133,966 instances of 18 affective states collected from 69,174 students who interacted with an online math learning platform called Algebra Nation over the entire school year. To enable scalability and generalizability, we used generic interaction features (e.g., viewing a video, taking a quiz),…
Descriptors: Affective Behavior, Online Courses, Educational Technology, Technology Uses in Education
Khayi, Nisrine Ait; Rus, Vasile – International Educational Data Mining Society, 2019
In this paper, we applied a number of clustering algorithms on pretest data collected from 264 high-school students. Students took the pre-test at the beginning of a 5-week experiment in which they interacted with an intelligent tutoring system. The primary goal of this work is to identify clusters of students exhibiting similar knowledge…
Descriptors: High School Students, Cluster Grouping, Prior Learning, Intelligent Tutoring Systems
Banjade, Rajendra; Rus, Vasile – International Educational Data Mining Society, 2019
Automatic answer assessment systems typically apply semantic similarity methods where student responses are compared with some reference answers in order to access their correctness. But student responses in dialogue based tutoring systems are often grammatically and semantically incomplete and additional information (e.g., dialogue history) is…
Descriptors: Dialogs (Language), Probability, Intelligent Tutoring Systems, Semantics
Richard, Verna Marie – ProQuest LLC, 2019
The purpose of this mixed methods study was to investigate relationships between students' ALEKS usage, teachers' implementation of ALEKS, and student performance on the 2017-2018 LEAP 2025 mathematics assessment. The quantitative portion of the study involved district-level analyses and teacher-level analyses that explored relationships between…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Standardized Tests, Mathematics Tests
Tlili, Ahmed; Denden, Mouna; Essalmi, Fathi; Jemni, Mohamed; Chang, Maiga; Kinshuk; Chen, Nian-Shing – Interactive Learning Environments, 2023
The ability of automatically modeling learners' personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional…
Descriptors: Learning Analytics, Learning Management Systems, Intelligent Tutoring Systems, Bayesian Statistics
Belda-Medina, Jose; Kokošková, Vendula – International Journal of Educational Technology in Higher Education, 2023
Recent advances in Artificial Intelligence (AI) have paved the way for the integration of text-based and voice-enabled chatbots as adaptive virtual tutors in education. Despite the increasing use of AI-powered chatbots in language learning, there is a lack of studies exploring the attitudes and perceptions of teachers and students towards these…
Descriptors: Technology Integration, Technology Uses in Education, Artificial Intelligence, Man Machine Systems
Office of Educational Technology, US Department of Education, 2023
The U.S. Department of Education (Department) is committed to supporting the use of technology to improve teaching and learning and to support innovation throughout educational systems. This report addresses the clear need for sharing knowledge and developing policies for "Artificial Intelligence," a rapidly advancing class of…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Educational Policy
Standen, Penelope J.; Brown, David J.; Taheri, Mohammad; Galvez Trigo, Maria J.; Boulton, Helen; Burton, Andrew; Hallewell, Madeline J.; Lathe, James G.; Shopland, Nicholas; Blanco Gonzalez, Maria A.; Kwiatkowska, Gosia M.; Milli, Elena; Cobello, Stefano; Mazzucato, Annaleda; Traversi, Marco; Hortal, Enrique – British Journal of Educational Technology, 2020
Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student's affective state, to move them out of negative states that inhibit learning, into…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Identification
Chiu, Mei-Shiu – Journal of Educational Data Mining, 2020
This study aims to identify effective affective states and behaviors of middle-school students' online mathematics learning in predicting their choices to study science, technology, engineering, and mathematics (STEM) in higher education based on a "positive-affect-to-success hypothesis." The dataset (591 students and 316,974 actions)…
Descriptors: Gender Differences, Predictor Variables, STEM Education, Course Selection (Students)
Zatarain Cabada, Ramón; Barrón Estrada, María Lucía; Ríos Félix, José Mario; Alor Hernández, Giner – Interactive Learning Environments, 2020
Emotions play an important role in students learning to master complex intellectual activities such as computer programing. Emotions such as confusion, boredom and frustration in the student are important factors in determining whether the student will master the exercise of learning to program in the short and long term. Motivation also plays an…
Descriptors: Programming, Game Based Learning, Emotional Response, Psychological Patterns
Mitten, Carolyn; Collier, Zachary K.; Leite, Walter L. – Grantee Submission, 2021
Adoption of online resources to support instruction and student performance has amplified with technological advances and increased standards for mathematics education. Because teachers play a critical role in the adoption of technology, analysis of data pertaining to how and why teachers utilize online resources is needed to optimize the design…
Descriptors: Teaching Methods, Educational Resources, Mathematics Instruction, Teacher Role
