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Liang Zhang; Jionghao Lin; John Sabatini; Conrad Borchers; Daniel Weitekamp; Meng Cao; John Hollander; Xiangen Hu; Arthur C. Graesser – IEEE Transactions on Learning Technologies, 2025
Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%90% missing observations) in most real-world applications. This data…
Descriptors: Artificial Intelligence, Academic Achievement, Data, Evaluation Methods
Christina Elizabeth Pigg – ProQuest LLC, 2024
The purpose of this ex post facto quantitative study was to examine the correlation between the scores of preservice teachers on 240 Tutoring STR practice tests and their scores on the actual STR exam and to explore the extent to which test preparation programs predicted performance on certification exams. In addition, this study compared the…
Descriptors: Test Preparation, Preservice Teachers, Teacher Certification, Licensing Examinations (Professions)
Schmucker, Robin; Wang, Jingbo; Hu, Shijia; Mitchell, Tom M. – Journal of Educational Data Mining, 2022
We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of log data from earlier…
Descriptors: Academic Achievement, Electronic Learning, Artificial Intelligence, Predictor Variables
Assielou, Kouamé Abel; Haba, Cissé Théodore; Kadjo, Tanon Lambert; Goore, Bi Tra; Yao, Kouakou Daniel – Journal of Education and e-Learning Research, 2021
Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to…
Descriptors: Psychological Patterns, Predictor Variables, Intelligent Tutoring Systems, Secondary School Students
Tomohiro Nagashima; Stephanie Tseng; Elizabeth Ling; Anna N. Bartel; Nicholas A. Vest; Elena M. Silla; Martha W. Alibali; Vincent Aleven – Grantee Submission, 2022
Learners' choices as to whether and how to use visual representations during learning are an important yet understudied aspect of self-regulated learning. To gain insight, we developed a "choice-based" intelligent tutor in which students can choose whether and when to use diagrams to aid their problem solving in algebra. In an…
Descriptors: Middle School Students, Visual Aids, Intelligent Tutoring Systems, Independent Study
Ying Fang; Tong Li; Linh Huynh; Katerina Christhilf; Rod D. Roscoe; Danielle S. McNamara – Grantee Submission, 2023
Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has…
Descriptors: Literacy, Student Evaluation, Educational Games, Literacy Education
Owolabi Paul Adelana; Musa Adekunle Ayanwale; Ismaila Temitayo Sanusi – Cogent Education, 2024
This study addresses the challenge of teaching genetics effectively to high school students, a topic known to be particularly challenging. Leveraging the growing importance of artificial intelligence (AI) in education, the research explores the perspectives, attitudes, and behavioral intentions of pre-service teachers regarding the integration of…
Descriptors: Preservice Teachers, Biology, Science Teachers, Intention
Odiel Estrada-Molina; Juanjo Mena; Alexander López-Padrón – International Review of Research in Open and Distributed Learning, 2024
No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Open Education, Educational Trends
Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
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
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
Makhlouf, Jihed; Mine, Tsunenori – Journal of Educational Data Mining, 2020
In recent years, we have seen the continuous and rapid increase of job openings in Science, Technology, Engineering and Math (STEM)-related fields. Unfortunately, these positions are not met with an equal number of workers ready to fill them. Efforts are being made to find durable solutions for this phenomena, and they start by encouraging young…
Descriptors: Learning Analytics, STEM Education, Science Careers, Career Choice
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)
Almeda, Ma. Victoria; Baker, Ryan S. – Journal of Educational Data Mining, 2020
Given the increasing need for skilled workers in science, technology, engineering, and mathematics (STEM), there is a burgeoning interest to encourage young students to pursue a career in STEM fields. Middle school is an opportune time to guide students' interests towards STEM disciplines, as they begin to think about and plan for their career…
Descriptors: Student Participation, Predictor Variables, STEM Education, Science Careers
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

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