Research on evaluation of university education informatization level based on clustering technique

Abstract

Today, the utilization of Information Technology tools is considered an inevitable path in the education system. In this regard, assessing the effective integration of Information Technology tools in the educational system holds significant importance. This process can be automated using artificial intelligence techniques, which are the subject of the current study. In this research, initially, a set of 14 indicators related to levels of Education Informatization (EI) in higher education is introduced. Subsequently, a clustering-based strategy is proposed to rank the indicators and determine an optimal subset of these features. Based on this framework, it is demonstrated that using 11 indicators related to educational behaviors can achieve the highest accuracy in evaluating EI levels. The proposed approach employs a group of Support Vector Machines (SVMs) for EI level assessment, where classifier hyperparameters are tuned using reinforcement learning strategy. The performance of the proposed method is evaluated on real-world data and compared with previous works. The results indicate that the proposed method can assess EI levels in universities with an average accuracy of 93.64 %, outperforming compared methods by at least 4.09 %.

Keywords

• Education informatization
• Machine learning
• Clustering
• Reinforcement learning
• Support vector machine