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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
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
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
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
Chavez-Gibson, Sarah – ProQuest LLC, 2013
The purpose of this study is to exam in-depth, the Comprehensive, Powerful, Academic Database (CPAD), a data decision-making tool that determines and identifies students at-risk of dropping out of school, and how the CPAD assists administrators and teachers at an elementary campus to monitor progress, curriculum, and performance to improve student…
Descriptors: Databases, Decision Making, At Risk Students, Dropouts
Bass, Laura H.; Ballard, Angela S. – Research in Higher Education Journal, 2012
A study by Kenney, Kenney, and Dumont (2005) identified a supportive learning environment as one of the five indicators for collegiate student engagement, a concept that extends beyond the classroom to permeate the entire educational environment. A student's level of engagement can be impacted as early as orientation and registration, when he is…
Descriptors: Predictor Variables, Educational Environment, Nontraditional Students, Student Attrition
Castellano, Katherine E.; Ho, Andrew D. – Council of Chief State School Officers, 2013
This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…
Descriptors: Guides, Models, Academic Achievement, Achievement Gains
Froman, Terry; Rubiera, Vilma – Research Services, Miami-Dade County Public Schools, 2008
For the past few years the Florida School Code has set the Florida Comprehensive Assessment Test (FCAT) performance requirements for promotion of 3rd graders and graduation for 10 graders. Grade 3 students who do not score at level 2 or higher on the FCAT SSS Reading must be retained unless exempted for special circumstances. Grade 10 students…
Descriptors: Academic Achievement, Prediction, Grade 3, Grade 10
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring