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Narjes Rohani; Behnam Rohani; Areti Manataki – Journal of Educational Data Mining, 2024
The prediction of student performance and the analysis of students' learning behaviour play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behaviour, educators can gain valuable insights into the factors that influence students' academic outcomes and identify areas of…
Descriptors: Mathematics Education, Models, Prediction, Knowledge Level
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Shoaib, Muhammad; Sayed, Nasir; Amara, Nedra; Latif, Abdul; Azam, Sikandar; Muhammad, Sajjad – Education and Information Technologies, 2022
Technology and data analysis have evolved into a resource-rich tool for collecting, researching and comparing student achievement levels in the classroom. There are sufficient resources to discover student success through data analysis by routinely collecting extensive data on student behaviour and curriculum structure. Educational Data Mining…
Descriptors: Prediction, Artificial Intelligence, Student Behavior, Academic Achievement
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Umer, Rahila; Susnjak, Teo; Mathrani, Anuradha; Suriadi, Lim – Interactive Learning Environments, 2023
Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…
Descriptors: Prediction, Student Behavior, Models, Academic Achievement
Wilhelmina Van Dijk; Cynthia U. Norris; Stephanie Al Otaiba; Christopher Schatschneider; Sara A. Hart – Grantee Submission, 2022
This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 students between five and twelve years old participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442…
Descriptors: Data, Reading Instruction, Intervention, Family Environment
Juan D’Brot; W. Chris Brandt – Region 5 Comprehensive Center, 2024
In today's educational landscape, state and local educational agencies (SEAs and LEAs) often experience challenges connecting large-scale accountability data with actual school improvement initiatives. These challenges tend to be rooted in incoherent design and use of data systems for continuous improvement. As we aim to support SEAs in…
Descriptors: Educational Improvement, Data Collection, State Departments of Education, School Districts
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Chinsook, Kittipong; Khajonmote, Withamon; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jantakoon, Thada – Higher Education Studies, 2022
Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in…
Descriptors: Student Behavior, Learning Analytics, Computer Software, Rating Scales
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Cenka, Baginda Anggun Nan; Santoso, Harry B.; Junus, Kasiyah – Knowledge Management & E-Learning, 2022
Online learning implementation has been growing year by year across countries, including Indonesia. Many higher education institutions use a Learning Management System (LMS) to facilitate online learning. Unfortunately, many issues arise during online learning implementation, such as a lack of student behaviour monitoring. This study adopts an…
Descriptors: Knowledge Management, Electronic Learning, Integrated Learning Systems, Student Behavior
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Crescenzi-Lanna, Lucrezia – British Journal of Educational Technology, 2020
Learning Analytics and Multimodal Learning Analytics are changing the way of analysing the learning process while students interact with an educational content. This paper presents a systematic literature review aimed at describing practices in recent Multimodal Learning Analytics and Learning Analytics research literature in order to identify…
Descriptors: Learning Modalities, Learning Analytics, Student Behavior, Progress Monitoring
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Ricker, Gina M.; Koziarski, Mathew; Walters, Alyssa M. – Journal of Online Learning Research, 2020
The relationship between student activity data and performance in the online classroom is well-documented, yet the parameters of this relationship and their implications for K-12 online schools are not yet well understood. This study examined the role of student chronotype (defined here as the time of day a student is most active in an online…
Descriptors: Electronic Learning, Online Courses, Student Behavior, Data Collection
Knudson, Joel – California Collaborative on District Reform, 2020
School closures in response to the COVID-19 pandemic have dramatically changed the conditions in which students learn and experience schooling. Disparities in students' access to learning and in their academic outcomes are likely to exacerbate longstanding challenges and inequities. Now more than ever, educators need information that will help…
Descriptors: Data Use, Educational Improvement, Equal Education, Data Collection
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K. Brigid Flannery; Mimi McGrath Kato; Angus Kittelman; Nadia Katul Sampson; Kent McIntosh – Behavioral Disorders, 2024
The purpose of this study was to provide initial evidence of the effectiveness of Check-In/Check-Out-High School (CICO-HS) on high school student outcomes. Check-In/Check-Out-High School is a version of CICO, an established Tier 2 intervention designed to improve student academic and social behavior, adapted to increase effectiveness and…
Descriptors: High School Students, Intervention, Positive Behavior Supports, Program Effectiveness
Yahya, Muhammad; Wijoyo, Hadion – Online Submission, 2020
This research aims to develop a student data management system at 9 Tapung Junior High School (SMP) Negeri 9 Tapung, which has not documented using a computer or school web using text-based programming languages like HTML, JavaScript, CSS, and PHP. The student information system design at SMP Negeri 9 Tapung in its manufacture uses a literacy…
Descriptors: Junior High Schools, Information Systems, Student Records, Student Characteristics
Flannery, K. Brigid; Kato, Mimi McGrath; Horner, Robert H. – Technical Assistance Center on Positive Behavioral Interventions and Supports, 2019
Using data for decision-making is critical for schoolwide leadership teams and has been shown to enhance both social and academic outcomes for students (Faria et al., 2017). Using data effectively, however, requires that teams have a clear vision about the type of data, format of data presentation, and process for using data. To avoid expending…
Descriptors: High School Students, Data Use, Outcomes of Education, Positive Behavior Supports
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Montgomery, Amanda P.; Mousavi, Amin; Carbonaro, Michael; Hayward, Denyse V.; Dunn, William – British Journal of Educational Technology, 2019
Blended learning (BL) is a popular e-Learning model in higher education that has the potential to take advantage of learning analytics (LA) to support student learning. This study utilized LA to investigate fourth-year undergraduates' (n = 157) use of self-regulated learning (SRL) within the online components of a previously unexamined BL…
Descriptors: Blended Learning, Educational Technology, Higher Education, Undergraduate Students
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Kahan, Tali; Soffer, Tal; Nachmias, Rafi – International Review of Research in Open and Distributed Learning, 2017
In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better…
Descriptors: College Students, Student Behavior, Online Courses, Large Group Instruction
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