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Cameron, Tracy A.; Schaughency, Elizabeth; Taumoepeau, Mele; McPherson, Craig; Carroll, Jane L. D. – School Psychology, 2023
Oral language and early literacy skills are theorized to provide the foundation for reading acquisition. To understand these relations, methods are needed that depict dynamic skill development in the context of reading acquisition. We modeled contributions of school-entry skills and early skill trajectories to later reading with 105 5-year-old…
Descriptors: Foreign Countries, Elementary School Students, Emergent Literacy, Oral Language
Khan, Ijaz; Ahmad, Abdul Rahim; Jabeur, Nafaa; Mahdi, Mohammed Najah – Smart Learning Environments, 2021
A major problem an instructor experiences is the systematic monitoring of students' academic progress in a course. The moment the students, with unsatisfactory academic progress, are identified the instructor can take measures to offer additional support to the struggling students. The fact is that the modern-day educational institutes tend to…
Descriptors: Artificial Intelligence, Academic Achievement, Progress Monitoring, Data Collection
Tanaka, Tetsuo; Ueda, Mari – International Association for Development of the Information Society, 2023
In this study, the authors have developed a web-based programming exercise system currently implemented in classrooms. This system not only provides students with a web-based programming environment but also tracks the time spent on exercises, logging operations such as program editing, building, execution, and testing. Additionally, it records…
Descriptors: Scores, Prediction, Programming, Artificial Intelligence
Loren Lydia Baranko Faught – ProQuest LLC, 2023
Early intervention is a method institutions use to identify and support students who are having academic difficulty and might be designated as "at-risk", or more likely to leave an institution (Villano et al., 2018). Institutions often adopt early alert systems to support early intervention efforts and student retention (Barefoot et al.,…
Descriptors: Intervention, At Risk Students, Progress Monitoring, Program Implementation
Fatima, Saba – ProQuest LLC, 2023
Predicting students' performance to identify which students are at risk of receiving a D/Fail/Withdraw (DFW) grade and ensuring their timely graduation is not just desirable but also necessary in most educational entities. In the US, not only is the Science, Technology, Engineering, and Mathematics (STEM) major becoming less popular among…
Descriptors: Artificial Intelligence, Prediction, Outcomes of Education, At Risk Students
Chad J. Coleman – ProQuest LLC, 2021
Determining which students are at-risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Elementary Secondary Education
Hu, Yung-Hsiang – International Review of Research in Open and Distributed Learning, 2022
Early warning systems (EWSs) have been successfully used in online classes, especially in massive open online courses, where it is nearly impossible for students to interact face-to-face with their teachers. Although teachers in higher education institutions typically have smaller class sizes, they also face the challenge of being unable to have…
Descriptors: Dropout Prevention, At Risk Students, Online Courses, Private Colleges
Alcaraz, Raul; Martinez-Rodrigo, Arturo; Zangroniz, Roberto; Rieta, Jose Joaquin – IEEE Transactions on Learning Technologies, 2021
Early warning systems (EWSs) have proven to be useful in identifying students at risk of failing both online and conventional courses. Although some general systems have reported acceptable ability to work in modules with different characteristics, those designed from a course-specific perspective have recently provided better outcomes. Hence, the…
Descriptors: Prediction, At Risk Students, Academic Failure, Electronic Equipment
Forthmann, Boris; Förster, Natalie; Souvignier, Elmar – Journal of Intelligence, 2022
Monitoring the progress of student learning is an important part of teachers' data-based decision making. One such tool that can equip teachers with information about students' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and…
Descriptors: Learning Processes, Progress Monitoring, Robustness (Statistics), Bayesian Statistics
van Dijk, Wilhelmina; Pico, Danielle L.; Kaplan, Rachel; Contesse, Valentina; Lane, Holly B. – Computers in the Schools, 2022
The use of online literacy applications is proliferating in elementary classrooms. Using data generated by these applications is assumed to be helpful for teachers to identify struggling readers. Unfortunately, many teachers are unsure how to use and interpret the plethora of data from these apps. In this longitudinal study, we followed a cohort…
Descriptors: Kindergarten, Grade 1, Reading Difficulties, Data Use
Sönmez, Selami – Universal Journal of Educational Research, 2018
Descartes expresses his opinion on the method very clear with the quote: "The whole secret of the method; starting from the circle and gradually going up the steps to the most complicated ". When it is thought that the knowledge of the absolute and unchanging truth in the positive sciences has not yet been reached, it should not be…
Descriptors: Scientific Research, Research Methodology, Classification, Prediction
David M. Alexandro – ProQuest LLC, 2018
In response to the high school dropout crisis, which comes with great economic and social costs, early warning systems (EWSs) have been developed to systematically predict and improve student outcomes. The purpose of this study is to evaluate different statistical and machine learning methods to predict high school student performance and improve…
Descriptors: At Risk Students, Progress Monitoring, Artificial Intelligence, Prediction
Parker, David C.; Van Norman, Ethan; Nelson, Peter M. – Learning Disabilities Research & Practice, 2018
The accuracy of decision rules for progress monitoring data is influenced by multiple factors. This study examined the accuracy of decision rule recommendations with over 4,500 second-and third-grade students receiving a tier II reading intervention program. The sensitivity and specificity of three decision rule recommendations for predicting…
Descriptors: Progress Monitoring, Accuracy, Grade 2, Grade 3
Mozahem, Najib Ali – International Journal of Mobile and Blended Learning, 2020
Higher education institutes are increasingly turning their attention to web-based learning management systems. The purpose of this study is to investigate whether data collected from LMS can be used to predict student performance in classrooms that use LMS to supplement face-to-face teaching. Data was collected from eight courses spread across two…
Descriptors: Integrated Learning Systems, Data Use, Prediction, Academic Achievement
Ashenafi, Michael Mogessie; Ronchetti, Marco; Riccardi, Giuseppe – International Educational Data Mining Society, 2016
Predicting overall student performance and monitoring progress have attracted more attention in the past five years than before. Demographic data, high school grades and test result constitute much of the data used for building prediction models. This study demonstrates how data from a peer-assessment environment can be used to build student…
Descriptors: Peer Evaluation, Progress Monitoring, Performance, Undergraduate Students