Secil Caskurlu
Biographic Data
| ID | 6770780 |
|---|---|
| NAME | Secil Caskurlu |
| GIVEN NAMES | Secil |
| FAMILY NAME | Caskurlu |
| SIGNATURE | CASKURLU S |
| AFFILIATIONS | Purdue University West Lafayette |
| ORCID | 0000-0001-8716-9586 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
To what extent has machine learning achieved in predicting online at-risk students? Evidence from quantitative meta-analysis
Machine learning and data mining techniques hold promise in predicting at-risk students in online learning. This meta-analysis aimed to provide quantitative evidence to validate whether and to what extent machine learning techniques have been achieved in identifying online at-risk students. Meta-regressions examined the impacts of predictor data types, classical or deep learning approaches, and prediction stages on performance. A random-effects m…
Applications of Machine Learning for at‐Risk Student Prediction in Online Education: A 10‐Year Systematic Review of Literature
Data-Driven Decision-Making in Instructional Design: Instructional Designers’ Practices and Strategies
Development of a critical appraisal tool for assessing the reporting quality of qualitative studies: A worked example
The qualitative evidence behind the factors impacting online learning experiences as informed by the community of inquiry framework: A thematic synthesis
Cognitive load and online course quality: Insights from instructional designers in a higher education context
This multiple case study investigates instructional designers’ perceptions of online course quality, their use of cognitive load strategies when designing online courses, and whether utilization of these strategies contribute to online course quality. The participants of this study were instructional designers ( n = 5) who worked in various campus programs at a large Midwestern university. Data sources included pre‐interview survey, semi‐structur…
A meta-analysis addressing the relationship between teaching presence and students’ satisfaction and learning
Social presence in relation to students' satisfaction and learning in the online environment: A meta-analysis
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Social presence in relation to students' satisfaction and learning in the online environment: A meta-analysis
A meta-analysis addressing the relationship between teaching presence and students’ satisfaction and learning
The qualitative evidence behind the factors impacting online learning experiences as informed by the community of inquiry framework: A thematic synthesis
Cognitive load and online course quality: Insights from instructional designers in a higher education context
This multiple case study investigates instructional designers’ perceptions of online course quality, their use of cognitive load strategies when designing online courses, and whether utilization of these strategies contribute to online course quality. The participants of this study were instructional designers ( n = 5) who worked in various campus programs at a large Midwestern university. Data sources included pre‐interview survey, semi‐structur…
Development of a critical appraisal tool for assessing the reporting quality of qualitative studies: A worked example
Applications of Machine Learning for at‐Risk Student Prediction in Online Education: A 10‐Year Systematic Review of Literature
Data-Driven Decision-Making in Instructional Design: Instructional Designers’ Practices and Strategies
To what extent has machine learning achieved in predicting online at-risk students? Evidence from quantitative meta-analysis
Machine learning and data mining techniques hold promise in predicting at-risk students in online learning. This meta-analysis aimed to provide quantitative evidence to validate whether and to what extent machine learning techniques have been achieved in identifying online at-risk students. Meta-regressions examined the impacts of predictor data types, classical or deep learning approaches, and prediction stages on performance. A random-effects m…
Computer Science (6 works) · Psychology (6 works) · Mathematics education (4 works) · Online and Blended Learning (4 works) · Online Learning and Analytics (3 works) · Artificial Intelligence (2 works) · Cognition (2 works) · Educational technology (2 works) · Engineering (2 works) · Instructional design (2 works)