A Human-Centered Approach to Academic Performance Prediction Using Personality Factors in Educational AI

被引：1
作者：
Aslam, Muhammad Adnan [1 ]
Murtaza, Fiza [1 ]
Haq, Muhammad Ehatisham Ul [1 ]
Yasin, Amanullah [1 ]
Azam, Muhammad Awais [2 ]
机构：
[1] Air Univ, Fac Comp & Artificial Intelligence FCAI, Dept Creat Technol, Islamabad 44000, Pakistan
[2] Sch Informat Technol, Technol & Innovat Res Grp, Whitecliffe, Wellington 6145, New Zealand
来源：
INFORMATION | 2024年 / 15卷 / 12期
关键词：
human-centered AI; explainable AI; academic performance prediction; personality factors; educational data analytics; machine learning; student well-being; ethical AI; transparent decision-making; AI in education; MODEL;
D O I：
10.3390/info15120777
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
As artificial intelligence (AI) becomes increasingly integrated into educational environments, adopting a human-centered approach is essential for enhancing student outcomes. This study investigates the role of personality factors in predicting academic performance, emphasizing the need for explainable and ethical AI systems. Utilizing the SAPEx-D (Student Academic Performance Exploration) dataset from Air University, Islamabad, which comprises 494 records, we explore how individual personality traits can impact academic success. We employed advanced regression models, including Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Linear Regression, and Support Vector Regression, to predict students' Cumulative Grade Point Average (CGPA). Our findings reveal that the Gradient Boosting Regressor achieved an R-squared value of 0.63 with the lowest Mean Squared Error (MSE); incorporating personality factors elevated the R-squared to 0.83, significantly improving predictive accuracy. For letter grade classification, the incorporation of personality factors improved the accuracy for distinct classes to 0.67 and to 0.85 for broader class categories. The integration of the Shapley Additive Explanations (SHAPs) technique further allowed for the interpretation of how personality traits interact with other factors, underscoring their role in shaping academic outcomes. This research highlights the importance of designing AI systems that are not only accurate but also interpretable and aligned with human values, thereby fostering a more equitable educational landscape. Future work will expand on these findings by exploring the interaction effects of personality traits and applying more sophisticated machine learning techniques.
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页数：33
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