Soonja Yeom
Biographic Data
| ID | 9974236 |
|---|---|
| NAME | Soonja Yeom |
| GIVEN NAMES | Soonja |
| FAMILY NAME | Yeom |
| SIGNATURE | YEOM S |
| AFFILIATIONS | University of Tasmania |
| ORCID | 0000-0002-5843-101X |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Improve Student Risk Prediction with Clustering Techniques: A Systematic Review in Education Data Mining
Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predictive models, have demonstrated potential in identifying at-risk students early and accurately. This systematic review explores how cluster-based prediction models have been applied in educational conte…
Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review
Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success …
Predicting Student Performance Using Clickstream Data and Machine Learning
Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for th…
Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education
This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framewo…
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Revealing Impact Factors on Student Engagement: Learning Analytics Adoption in Online and Blended Courses in Higher Education
This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student enrolments and 406 lecturer and tutor roles, over a five-year period. Based on a theoretical framewo…
Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review
Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success …
Predicting Student Performance Using Clickstream Data and Machine Learning
Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding students’ learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for th…
Improve Student Risk Prediction with Clustering Techniques: A Systematic Review in Education Data Mining
Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predictive models, have demonstrated potential in identifying at-risk students early and accurately. This systematic review explores how cluster-based prediction models have been applied in educational conte…
Online Learning and Analytics (3 works) · Analytics (2 works) · Computer Science (2 works) · Data science (2 works) · Educational Data Mining (2 works) · Learning analytics (2 works) · Artificial Intelligence (1 works) · Blended Learning (1 works) · Clickstream (1 works) · Cluster analysis (1 works)