Mrs Mashael Al Luhaybi
PhD Student
Research area(s)
Research interests include:
- Data-Driven AI
- Machine Learning (ML)
- Artificial Intelligence (Al)
- Deep Learning (DL)
- Intelligent Data Analysis (IDA)
- Time Series Analysis
- Predictive Modeling
- Sequential Clustering
Research Interests
My current research focuses primarily on modelling academic performance. This typically involves analyzing and predicting the probabilities of unknown events using probabilistic models (Bayesian Networks). I am also interested in clustering Time-Series trajectories to profile and detect students' engagement types and learning patterns.
Research project(s) and grant(s)
Brunel Student Assessment and Retention Project (STARS Project).
Research links
- •The Prediction of Student Failure Using Classification Methods : A Case study
- •Predicting Disease Complications Using a Stepwise Hidden Variable Approach for Learning Dynamic Bayesian Networks
- •Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks
- •Identification of Student “Types” from Online Self- Assessment Temporal Trajectories With Dynamic Time Warping for Performance Prediction