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What Works Clearinghouse Rating
Damian Betebenner; Charles A. DePascale – National Center for the Improvement of Educational Assessment, 2024
In the wake of the COVID-19 pandemic, educators and policymakers have scrambled to assess the impact on student learning. Popular metrics that have gained traction are the notions of "years of learning lost" or "months behind," which attempt to quantify the educational setbacks caused by the pandemic. The allure of these…
Descriptors: COVID-19, Pandemics, Progress Monitoring, Academic Achievement
Susana Sánchez Castro; María Ángeles Pascual Sevillano; Javier Fombona Cadavieco – Technology, Knowledge and Learning, 2024
The planned systematized design of the use of serious games in the classroom is presented as a strategy to optimize learning. In this framework, Learning Analytics represents stealth assessment and follow-up method, and a way to personalize such games by simplifying their application for teachers. The aim of this research was to analyze the impact…
Descriptors: Learning Analytics, Linguistic Competence, At Risk Students, Teaching Methods
Lanqin Zheng; Yunchao Fan; Lei Gao; Zichen Huang – Interactive Learning Environments, 2024
Learning analytics has received increasing attention in the field of education. However, few studies have investigated the overall impact of learning analytics interventions on learning achievements. This study aims to close this research gap and examine the sizes of the overall effects of learning analytics interventions on learning achievements…
Descriptors: Learning Analytics, Meta Analysis, Intervention, Academic Achievement
Sudeshna Pal; Patsy Moskal; Anchalee Ngampornchai – International Journal on E-Learning, 2024
This study investigated the effectiveness of blended instruction in enhancing student success in an advanced undergraduate engineering course. The research used learning analytics captured from pre-recorded lecture videos, course grade data, and student surveys. Results revealed positive correlations between lecture video viewership and course…
Descriptors: Blended Learning, Advanced Courses, Engineering Education, Undergraduate Students
Vo, Thi Ngoc Chau; Nguyen, Phung – IEEE Transactions on Learning Technologies, 2021
A course-level early final study status prediction task is to predict as soon as possible the final success of each student after studying a course. It is significant because each successful course accomplishment is required for a degree. Further, early predictions provide enough time to make necessary changes for ultimate success. This article…
Descriptors: Prediction, Academic Achievement, Data Collection, Learning Processes
Nuankaew, Pratya; Nuankaew, Wongpanya Sararat – European Journal of Educational Research, 2022
Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education…
Descriptors: Grade Prediction, Academic Achievement, High School Students, Rural Schools
Sun, Fu-Rong; Hu, Hong-Zhen; Wan, Rong-Gen; Fu, Xiao; Wu, Shu-Jing – Interactive Learning Environments, 2022
To determine the impact of cognitive style on change of concept of engagement in the flipped classroom, a sequential analysis from the perspective of Bloom's Taxonomy was conducted to establish if significant differences existed between the learning achievements and engagement of students with different cognitive styles. The participants were…
Descriptors: Learning Analytics, Preservice Teachers, Educational Change, Learner Engagement
Yang, Christopher C. Y.; Ogata, Hiroaki – Educational Technology & Society, 2023
Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence…
Descriptors: Blended Learning, Sequential Approach, Notetaking, Electronic Publishing
Ean Teng Khor; Dave Darshan – International Journal of Information and Learning Technology, 2024
Purpose: This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course. Design/methodology/approach: The exploration and visualisation of the…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
Zareen Alamgir; Habiba Akram; Saira Karim; Aamir Wali – Informatics in Education, 2024
Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies.…
Descriptors: Data Analysis, Information Retrieval, Content Analysis, Information Technology
Zi Xiang Poh; Ean Teng Khor – International Journal on E-Learning, 2024
Machine learning and data mining techniques have been widely used in educational settings to identify the important features that tend to influence students' learning performance and predict their future performance. However, there is little to no research done in the context of Singapore's education. Hence, this study aims to fill the gap by…
Descriptors: Learning Analytics, Goodness of Fit, Academic Achievement, Online Courses
Weijuan Li – European Journal of Education, 2025
In recent years, the integration of big data and learning analytics has emerged as a significant trend across educational systems worldwide. The implementation of such technologies within universities -- particularly in China -- holds considerable potential for transforming teaching and learning practices. By enabling personalised, data-driven…
Descriptors: Universities, Learning Analytics, Educational Practices, Foreign Countries
J. M. Fernández Oro; P. García Regodeseves; L. Santamaría Bertolín; J. González Pérez; R. Barrio-Perotti; A. Pandal Blanco – Technology, Knowledge and Learning, 2025
Learning Analytics tools are employed to assess student engagement with the Virtual Campus in an undergraduate Fluid Mechanics course at university level in Spain. This is aimed at obtaining a diagnosis of the course problematics which include low attendance rates, poor performance on activity tests and exams and a high number of re-enrolments. A…
Descriptors: Learning Analytics, Electronic Learning, Undergraduate Study, Mechanics (Physics)
Hellings, Jan; Haelermans, Carla – Higher Education: The International Journal of Higher Education Research, 2022
We use a randomised experiment to study the effect of offering half of 556 freshman students a learning analytics dashboard and a weekly email with a link to their dashboard, on student behaviour in the online environment and final exam performance. The dashboard shows their online progress in the learning management systems, their predicted…
Descriptors: Learning Analytics, College Freshmen, Student Behavior, Electronic Learning
Wu, Xinli; Chang, Jie; Lian, Fei; Jiang, Liheng; Liu, Juntong; Yasrab, Robail – International Journal of Information and Communication Technology Education, 2022
The rapid development of big data technology has attracted a variety of sectors, including tertiary education. The purpose of this paper is to construct a precision teaching mode based on big data technology in order to improve teaching quality and further promote education and teaching reform. The proposed mode, based on the theory of precision…
Descriptors: Precision Teaching, Learning Analytics, Teacher Evaluation, Programming Languages

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