Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 2 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Data Use | 2 |
| Academic Achievement | 1 |
| Attention | 1 |
| Data Analysis | 1 |
| Data Collection | 1 |
| Dropout Rate | 1 |
| Educational Research | 1 |
| Gamification | 1 |
| Grade Prediction | 1 |
| Instructional Improvement | 1 |
| Literature Reviews | 1 |
| More ▼ | |
Source
| Technology, Knowledge and… | 2 |
Author
| Alturki, Sarah | 1 |
| Hulpu?, Ioana | 1 |
| Ivana Ðurdevic Babic | 1 |
| Natalija Bošnjakovic | 1 |
| Stuckenschmidt, Heiner | 1 |
Publication Type
| Information Analyses | 2 |
| Journal Articles | 2 |
| Reports - Descriptive | 1 |
Education Level
Audience
| Researchers | 1 |
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Natalija Bošnjakovic; Ivana Ðurdevic Babic – Technology, Knowledge and Learning, 2025
To improve and facilitate the acquisition of learning outcomes, teachers often use innovative teaching methods such as gamification to keep students' attention and increase their motivation. In recent years, the use of educational data mining (EDM) methods to explore academic topics has increased. With the expansion of EDM, a gap in the literature…
Descriptors: Data Collection, Gamification, Teaching Methods, Attention
Alturki, Sarah; Hulpu?, Ioana; Stuckenschmidt, Heiner – Technology, Knowledge and Learning, 2022
The tremendous growth of educational institutions' electronic data provides the opportunity to extract information that can be used to predict students' overall success, predict students' dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students' needs, and much more. This paper aims to…
Descriptors: Grade Prediction, Academic Achievement, Data Use, Dropout Rate

Peer reviewed
Direct link
