Predicting Learners’ Academic Progression Using Subspace Clique Model in Multidimensional Data

Subspace clustering examines the traditional clustering techniques that have previously been considered the best approaches to clustering data. This study uses a subspace clustering approach to predict learners' academic progress over time. Using the subspace clustering method, a model was developed that improves the classic Clique by optimizing clustering performance and addresses the clustering challenges posed by inaccuracies due to additional data size and increased dimensionality. The study used an experimental design that included data validation and training to predict students' academic progress. Clustering evaluation metrics including accuracy, precision, and recall measures were identified. The optimized model recorded a better performance index with 98.90% accuracy, 98.50% precision, and 98.50% recall which directly shows the efficiency of the optimized model in predicting learning academic progress through clustering. In this regard, conclusions are drawn for an alternative approach to predictive modeling through cluster analysis, so that educational institutions have a better opportunity to manage learners by ensuring adequate preparation in terms of resources, policies and knowledge. It highlights career guidance for learners based on their academic progress. The result validates the suitability of the model for clustering multidimensional data.

@article{James2024,
  title     = {Predicting Learners’ Academic Progression Using Subspace Clique Model in Multidimensional Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Oyugi Odhiambo James and Waweru Mwangi and Kennedy Ogada},
  doi       = {10.14569/IJACSA.2024.0151123},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151123}
}