Publication Date
In 2025 | 1 |
Since 2024 | 3 |
Since 2021 (last 5 years) | 10 |
Since 2016 (last 10 years) | 14 |
Since 2006 (last 20 years) | 14 |
Descriptor
Learning Analytics | 14 |
Prediction | 14 |
Academic Achievement | 5 |
Artificial Intelligence | 5 |
Models | 5 |
Computer Software | 4 |
Learning Processes | 4 |
Student Behavior | 4 |
Algorithms | 3 |
College Students | 3 |
Electronic Learning | 3 |
More ▼ |
Source
Author
Publication Type
Reports - Descriptive | 14 |
Journal Articles | 11 |
Speeches/Meeting Papers | 2 |
Guides - General | 1 |
Education Level
Higher Education | 7 |
Postsecondary Education | 7 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Hanqiang Liu; Xiao Chen; Feng Zhao – Education and Information Technologies, 2024
Massive open online courses (MOOCs) have become one of the most popular ways of learning in recent years due to their flexibility and convenience. However, high dropout rate has become a prominent problem that hinders the further development of MOOCs. Therefore, the prediction of student dropouts is the key to further enhance the MOOCs platform.…
Descriptors: MOOCs, Video Technology, Behavior Patterns, Prediction
Khajonmote, Withamon; Chinsook, Kittipong; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jansawang, Natchanok; Jantakoon, Thada – Journal of Education and Learning, 2022
The system architecture of big data in massive open online courses (BD-MOOCs System Architecture) is composed of six components. The first component was comprised of big data tools and technologies such as Hadoop, YARN, HDFS, Spark, Hive, Sqoop, and Flume. The second component was educational data science, which is composed of the following four…
Descriptors: MOOCs, Data Collection, Student Behavior, Computer Software
Hua Ma; Wen Zhao; Yuqi Tang; Peiji Huang; Haibin Zhu; Wensheng Tang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…
Descriptors: College Students, Learning Analytics, Learning Management Systems, Academic Achievement
Khalid Oqaidi; Sarah Aouhassi; Khalifa Mansouri – International Association for Development of the Information Society, 2022
The dropout of students is one of the major obstacles that ruin the improvement of higher education quality. To facilitate the study of students' dropout in Moroccan universities, this paper aims to establish a clustering approach model based on machine learning algorithms to determine Moroccan universities categories. Our objective in this…
Descriptors: Models, Prediction, Dropouts, Learning Analytics
Worsley, Marcelo; Martinez-Maldonado, Roberto; D'Angelo, Cynthia – Journal of Learning Analytics, 2021
Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12…
Descriptors: Learning Analytics, Artificial Intelligence, Data Collection, Statistical Inference
Kil, David; Baldasare, Angela; Milliron, Mark – Current Issues in Education, 2021
Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'être of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy…
Descriptors: Learning Analytics, Academic Achievement, College Students, Electronic Learning
Jamal Eddine Rafiq; Abdelali Zakrani; Mohammed Amraouy; Said Nouh; Abdellah Bennane – Turkish Online Journal of Distance Education, 2025
The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aims to predict learners' performance in online learning settings. This systemic model integrates cognitive, social,…
Descriptors: Models, Online Courses, Educational Improvement, Learning Processes
Yilmaz, Fahri; Çakir, Hasan – Journal of Learning and Teaching in Digital Age, 2021
The purpose of this study is to define learning analytics, to introduce concepts related to learning analytics and to introduce potential study topics related to learning analytics. Today's education model has changed with evolving social and economic conditions over time. This change in education has created such new situations as individualized…
Descriptors: Learning Analytics, Definitions, Educational Change, Individualized Instruction
Acosta, Alejandra – New America, 2020
Predictive analytics has taken higher education by storm, with its promise of closing equity gaps, raising student retention rates, and increasing tuition revenue by keeping students enrolled. Many colleges and universities have made an investment in predictive analytics for student success initiatives, and even more are looking into implementing,…
Descriptors: Prediction, Learning Analytics, Higher Education, Information Dissemination
De Jesús Liriano, Rubén; Sevillano, María C. – Distance Learning, 2019
In recent years, the academic industry has seen a widespread development of digital technology and learning analytics. Certainly, the exchange of automatic processing has opened a broad set of new ways and opportunities, allowing additional emphasis on interaction and connectivity. This new learning approach provides improvement in the development…
Descriptors: Learning Processes, Higher Education, Learning Analytics, Educational Technology
Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
Archer, Elizabeth; Prinsloo, Paul – Assessment & Evaluation in Higher Education, 2020
Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments…
Descriptors: Learning Analytics, Student Evaluation, Educational Objectives, Student Behavior
Khosravi, Hassan; Shabaninejad, Shiva; Bakharia, Aneesha; Sadiq, Shazia; Indulska, Marta; Gasevic, Dragan – Journal of Learning Analytics, 2021
Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…
Descriptors: Learning Analytics, Visual Aids, Artificial Intelligence, Information Retrieval
Roux, Lisa; Dagorret, Pantxika; Etcheverry, Patrick; Nodenot, Thierry; Marquesuzaa, Christophe; Lopisteguy, Philippe – International Association for Development of the Information Society, 2021
Distance computer-assisted learning is increasingly common, owing largely to the expansion and development of e-technology. Nevertheless, the available tools of the learning platforms have demonstrated their limits during the pandemic context, since many students, who were used to "face-to-face" education, got discouraged and dropped out…
Descriptors: Distance Education, Computer Software, Teacher Student Relationship, Supervision