Enock Quansah Effah
Biographic Data
| ID | 8971597 |
|---|---|
| NAME | Enock Quansah Effah |
| GIVEN NAMES | Enock Quansah |
| FAMILY NAME | Effah |
| SIGNATURE | EFFAH E Q |
| AFFILIATIONS | WWF Tanzania |
| ORCID | 0009-0006-9479-1166 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2026 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Intelligent profiling beyond grades using a stacking ensemble framework for student success prediction
The study integrated machine learning (ML) framework for predicting students’ success in academics via 2-phased experiments with emotional intelligence and personality traits. In the initial experiment, unsupervised learning (K-Means clustering) was utilized to unearth hidden success levels from unlabeled data. This precedes training of standalone ML classifiers [Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB…
A 3-tier machine learning framework for early detection of learning difficulties in basic school settings
Early identification of learners at risk of learning difficulties (LDs) is essential for timely educational support, particularly in low-resource school settings where access to formal diagnostic services is limited. However, existing school-based identification practices often rely on subjective teacher judgment, leading to delayed or inconsistent intervention. This study proposes an explainable machine-learning (ML)–based risk assessment and de…
No prominent works on this page.
Intelligent profiling beyond grades using a stacking ensemble framework for student success prediction
The study integrated machine learning (ML) framework for predicting students’ success in academics via 2-phased experiments with emotional intelligence and personality traits. In the initial experiment, unsupervised learning (K-Means clustering) was utilized to unearth hidden success levels from unlabeled data. This precedes training of standalone ML classifiers [Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB…
A 3-tier machine learning framework for early detection of learning difficulties in basic school settings
Early identification of learners at risk of learning difficulties (LDs) is essential for timely educational support, particularly in low-resource school settings where access to formal diagnostic services is limited. However, existing school-based identification practices often rely on subjective teacher judgment, leading to delayed or inconsistent intervention. This study proposes an explainable machine-learning (ML)–based risk assessment and de…
Intelligent Tutoring Systems and Adaptive Learning (2 works) · Online Learning and Analytics (2 works) · Active learning (machine learning) (1 works) · Component (thermodynamics) (1 works) · Computational learning theory (1 works) · Decision tree (1 works) · Emotional Intelligence and Performance (1 works) · Empathy (1 works) · Ensemble forecasting (1 works) · Ensemble learning (1 works)