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Melissa Lee; Chun-Wei Huang; Kelly Collins; Mingyu Feng – Grantee Submission, 2025
Math anxiety has been found to negatively correlate with math achievement, affecting students' choices to take fewer math classes and avoid math educational opportunities. Educational technology tools can ameliorate some of the negative effects of math anxiety. We examined students' math anxiety, effort in an educational technology platform, and…
Descriptors: Correlation, Mathematics Anxiety, Mathematics Achievement, Outcomes of Education
Conrad Borchers; Alex Houk; Vincent Aleven; Kenneth R. Koedinger – Grantee Submission, 2025
Active learning promises improved educational outcomes yet depends on students' sustained motivation to engage in practice. Goal setting can enhance learner engagement. However, past evidence of the effectiveness of setting goals tends to be limited to non-digital learning settings and does not scale well as it requires active teacher or parent…
Descriptors: Learner Engagement, Educational Benefits, Goal Orientation, Rewards
Li, Shan; Zheng, Juan; Lajoie, Susanne P.; Wiseman, Jeffrey – Educational Technology Research and Development, 2021
Prior research has focused extensively on how emotion tendencies (e.g., duration, frequency, intensity, and valence) affect students' performance, but little is known about emotion variability (i.e., the fluctuations in emotion states) and how emotion variability affects performance. In this paper, emotion variability was examined among 21 medical…
Descriptors: Correlation, Emotional Response, Self Management, Learning Processes
Eglington, Luke G.; Pavlik, Philip I., Jr. – Grantee Submission, 2022
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Dizon, Gilbert – RELC Journal: A Journal of Language Teaching and Research, 2023
This paper provides a research synthesis of intelligent personal assistants (IPAs) -- that is, cloud-based virtual assistants such as Alexa, Google Assistant, and Siri -- for second language (L2) learning. The article also offers a theoretical justification for the use of IPAs in language learning and outlines the affordances and constraints of…
Descriptors: Second Language Learning, Second Language Instruction, Usability, Intelligent Tutoring Systems
Gang Yang; Xiao-Qian Zheng; Qian Li; Miao Han; Yun-Fang Tu – Interactive Learning Environments, 2024
In Chinese, writing is a basic competency that pupils should possess. But it is still challenging for teachers to improve pupils' writing abilities. Therefore, this study proposes an intelligence-based cognitive diagnostic feedback strategy to improve pupils' writing ability and writing learning quality by analyzing their writing performance,…
Descriptors: Foreign Countries, Elementary School Students, Vocabulary Skills, Comparative Analysis
Eitemüller, Carolin; Trauten, Florian; Striewe, Michael; Walpuski, Maik – Journal of Science Education and Technology, 2023
For various reasons, students receive less formative feedback at post-secondary institutions compared to secondary school. Considering feedback as one of the most important influencing factors on learning processes, formative feedback is a promising approach to improving students' performances. In this context, new technologies, such as learning…
Descriptors: Chemistry, Science Instruction, Teaching Methods, Error Patterns
Mangera, Elisabet; Supratno, Haris; Suyatno – Pegem Journal of Education and Instruction, 2023
This studied focus on the relationship between transhumanist and artificial intelligence in the Education Context; Particularly Teaching and Learning Process at private university in Makassar, South Sulawesi, Indonesia. Anchored by a qualitative analysis and participated by five teachers, the data were analyzed in-depth interview. It was designed…
Descriptors: Humanism, Artificial Intelligence, Learning Processes, Postsecondary Education
Wijaya, Adi; Setiawan, Noor Akhmad; Shapiai, Mohd Ibrahim – Electronic Journal of e-Learning, 2023
This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric…
Descriptors: Bibliometrics, Cognitive Style, Diagnostic Tests, Content Analysis
Zeyad Alshaikh – ProQuest LLC, 2021
Programming skills are a vital part of many disciplines but can be challenging to teach and learn. Thus, the programming courses are considered difficult and a major stumbling block. To overcome these challenges, students could benefit from extensive individual support such as tutoring, but there are simply not enough qualified tutors available to…
Descriptors: Questioning Techniques, Teaching Methods, Intelligent Tutoring Systems, Coding
Sarsa, Sami; Leinonen, Juho; Hellas, Arto – Journal of Educational Data Mining, 2022
New knowledge tracing models are continuously being proposed, even at a pace where state-of-the-art models cannot be compared with each other at the time of publication. This leads to a situation where ranking models is hard, and the underlying reasons of the models' performance -- be it architectural choices, hyperparameter tuning, performance…
Descriptors: Learning Processes, Artificial Intelligence, Intelligent Tutoring Systems, Memory
Meng Xia; Robin Schmucker; Conrad Borchers; Vincent Aleven – Grantee Submission, 2025
Mastery learning improves learning proficiency and efficiency. However, the overpractice of skills--students spending time on skills they have already mastered--remains a fundamental challenge for tutoring systems. Previous research has reduced overpractice through the development of better problem selection algorithms and the authoring of focused…
Descriptors: Mastery Learning, Skill Development, Intelligent Tutoring Systems, Technology Uses in Education
Jonathan Brazil; Suijing Yang; Fabienne van der Kleij – Australian Council for Educational Research, 2025
This document provides guiding principles and practical examples for using AI in teaching and learning. Underpinned by a human-centred approach, the PATH principles serve as key guidance to ensure the ethical and effective integration of AI systems into teaching and learning. The PATH principles are: Promote teaching and learning; Advance…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Educational Principles
Tacoma, Sietske; Drijvers, Paul; Jeuring, Johan – Journal of Computer Assisted Learning, 2021
Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on…
Descriptors: Feedback (Response), Intelligent Tutoring Systems, Statistics Education, Higher Education
Shi Pu; Yu Yan; Brandon Zhang – Journal of Educational Data Mining, 2024
We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…
Descriptors: Prediction, Success, Data Analysis, Learning Analytics

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