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Ecological Momentary Assessment as a Delivery Service for Progress Monitoring Internalizing Concerns
Ishan N. Vengurlekar; Carly Oddleifson; Chelsea Salvatore; Stephen P. Kilgus; Evan H. Dart – Journal of Applied School Psychology, 2025
Progress monitoring data provide important information on student functioning in response to an intervention. Yet, there are several barriers to effective progress monitoring of internalizing symptoms among youth. To address these concerns, the current paper conceptualized the use of ecological momentary assessment (EMA) as a service delivery…
Descriptors: Middle School Students, Progress Monitoring, Student Improvement, Emotional Response
Donnelly, Shawn; Parmar, Rene – Journal of Computers in Mathematics and Science Teaching, 2022
The purpose of this study is to evaluate the effectiveness of IXL Math online software for homework in raising the achievement of middle-school students on the New York State Math exam, with special focus on effects by student ethnicity. The study includes an analysis of the relationship between exam scale scores and time spent using the IXL…
Descriptors: Mathematics Instruction, Middle School Students, Racial Differences, Ethnicity
Miray Dogan; Arda Celik; Hasan Arslan – European Journal of Education, 2025
This research investigates how artificial intelligence (AI) influences higher education, specifically exploring the perspectives of academicians regarding associated risks and opportunities. The study is aimed at the implementation of AI within university settings and its impact on both educators and students. Given the swift integration of AI,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Software, Access to Internet
Baloch, Sidrah; Kane, Thomas J.; Scherer, Ethan; Staiger, Douglas O. – Annenberg Institute for School Reform at Brown University, 2021
Educators must balance the needs of students who start the school year behind grade level with their obligation to teach grade-appropriate content to all students. Educational software could help educators strike this balance by targeting content to students' differing levels of mastery. Using a regression discontinuity design and detailed…
Descriptors: Educational Technology, Computer Software, Computer Uses in Education, Acceleration (Education)
Anna Y. Q. Huang; Jei Wei Chang; Albert C. M. Yang; Hiroaki Ogata; Shun Ting Li; Ruo Xuan Yen; Stephen J. H. Yang – Educational Technology & Society, 2023
To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from…
Descriptors: Academic Achievement, Tutoring, Artificial Intelligence, Individualized Instruction
Hui, Bowen – International Journal of Information and Learning Technology, 2022
Purpose: The purpose of this work is to illustrate the processes involved in managing teams in order to assist designers and developers to build software that support teamwork. A deeper investigation into the role of team analytics is discussed in this article. Design/methodology/approach: Many researchers over the past several decades studied the…
Descriptors: Design, Guidelines, Research Needs, Teamwork
Marras, Mirko; Vignoud, Julien Tuân Tu; Käser, Tanja – International Educational Data Mining Society, 2021
Early predictors of student success are becoming a key tool in flipped and online courses to ensure that no student is left behind along course activities. However, with an increased interest in this area, it has become hard to keep track of what the state of the art in early success prediction is. Moreover, prior work on early success prediction…
Descriptors: Benchmarking, Predictor Variables, Academic Achievement, Flipped Classroom
Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence
Egan, Cate A.; Merica, Christopher B.; Paul, David R.; Bond, Laura; Rose, Seth; Martin, Andrew; Vella, Chantal – Health Education Journal, 2023
Objectives: In the USA, 18% of school-aged young people are classified as obese, and rural populations appear to be particularly at risk. Achieving high levels of fitness reduces the risk of obesity and underlying health conditions. To better understand youth obesity trends and fitness levels, annual fitness testing ([FT], that is, surveillance)…
Descriptors: Physical Fitness, Health Behavior, Teacher Attitudes, Distance Education
Mark Bodner; Andrew Coulson – Grantee Submission, 2021
A randomized controlled trial group design study funded by IES NCR Grant R305A090527 was conducted in which 16,307 3rd, 4th, and 5th grade students in 52 school grade-level clusters were randomly assigned to receive ST Math (program revision Gen3), a supplemental mathematics software instructional intervention, or to a business-as-usual…
Descriptors: Mathematics Instruction, Computer Software, Computer Uses in Education, Elementary School Students
Kostopoulos, Georgios; Karlos, Stamatis; Kotsiantis, Sotiris – IEEE Transactions on Learning Technologies, 2019
Educational data mining has gained a lot of attention among scientists in recent years and constitutes an efficient tool for unraveling the concealed knowledge in educational data. Recently, semisupervised learning methods have been gradually implemented in the educational process demonstrating their usability and effectiveness. Cotraining is a…
Descriptors: Academic Achievement, Case Studies, Usability, Data Analysis
Qazdar, Aimad; Er-Raha, Brahim; Cherkaoui, Chihab; Mammass, Driss – Education and Information Technologies, 2019
The use of machine learning with educational data mining (EDM) to predict learner performance has always been an important research area. Predicting academic results is one of the solutions that aims to monitor the progress of students and anticipates students at risk of failing the academic pathways. In this paper, we present a framework for…
Descriptors: Data Analysis, Academic Achievement, At Risk Students, High School Students
Shawn M. Donnelly – ProQuest LLC, 2021
The purpose of this study is to evaluate the effectiveness of IXL Math online software in raising student achievement on the New York State Math exam, with special focus on effects by student gender, ethnicity and disability status. The study includes an analysis of the relationship between scale scores and time spent using the IXL system, number…
Descriptors: Computer Software, Electronic Learning, Academic Achievement, Urban Schools
Vernon, D. Sue; Schumaker, Jean B. – Journal of Special Education Technology, 2022
This study reports the effects of an interactive multimedia computer program for teaching social skills to youths with social-adjustment problems. Twelve youths (aged 11-17) were referred by the county diversion program or school guidance counselors. Seven were formally classified as having disabilities and had active Individualized Education…
Descriptors: Interpersonal Competence, At Risk Students, Computer Software, Multimedia Instruction
Mohammadi, Sima; Zandi, Hamed – TESL-EJ, 2023
As students' achievement is correlated with self-regulation, finding interventions promoting self-regulated learning (SRL) in online courses is a current focus of research. However, few studies have explored the potential of contract learning in scaffolding and developing SRL in non-traditional learners who have work and family and are at risk of…
Descriptors: Scaffolding (Teaching Technique), Independent Study, English (Second Language), Second Language Learning