Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 3 |
Descriptor
Correlation | 3 |
Open Source Technology | 3 |
Computer Software | 2 |
Data Analysis | 2 |
Foreign Countries | 2 |
Integrated Learning Systems | 2 |
Academic Achievement | 1 |
Assignments | 1 |
Barriers | 1 |
College Instruction | 1 |
College Students | 1 |
More ▼ |
Author
Fancsali, Stephen E. | 1 |
Huisman, Magda | 1 |
Murphy, April | 1 |
Ritter, Steve | 1 |
Taylor, Estelle | 1 |
Ventura, Sebastian | 1 |
Zafra, Amelia | 1 |
van Aswegen, Kobus | 1 |
Publication Type
Speeches/Meeting Papers | 3 |
Reports - Research | 2 |
Reports - Descriptive | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
South Africa | 1 |
Spain | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Fancsali, Stephen E.; Murphy, April; Ritter, Steve – International Educational Data Mining Society, 2022
Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test…
Descriptors: Learning Analytics, Educational Technology, Barriers, Data Analysis
van Aswegen, Kobus; Huisman, Magda; Taylor, Estelle – International Association for Development of the Information Society, 2014
E-learning systems, or learning management systems, as it is known in the field, sit at the heart of educational systems and are used to systematically deliver on-line content and facilitate the learning experience around that content. It becomes essential to ensure that Learning Management Systems of a high standard are being developed. In the…
Descriptors: Electronic Learning, Management Systems, Educational Quality, Foreign Countries
Zafra, Amelia; Ventura, Sebastian – International Working Group on Educational Data Mining, 2009
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…
Descriptors: Foreign Countries, Programming, Academic Achievement, Grades (Scholastic)