arXiv subjects

Gayathri Nadarajan

Publications and source records attributed to Gayathri Nadarajan.

3 recordsLinked to original sources

arXiv2026

Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education

The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This study examines cross-cultural differences in student perceptions of GenAI use, comparing responses from students at Canadian and South Korean universities. Using a scenario-based survey administered in Fall 2024, we analyzed how students judged the ethicality and rule compliance of AI-assisted coding practices. Results reveal that Canadian students were consistently more likely to perceive the use of GenAI as both unethical and against institutional policies compared to Korean students, despite functionally identical institutional policies. Statistical analysis, including Mann-Whitney U tests and correlation coefficients, demonstrated significant differences across nearly all scenarios. Analysis of the factors used in generating scenarios indicated that the amount of AI-generated code incorporated into assignments most strongly influenced ethical judgments. Findings were interpreted through Hofstede's cultural dimensions framework, suggesting that cultural factors such as power distance, individualism, and uncertainty avoidance significantly shape students' ethical reasoning regarding GenAI. Our results contribute to the growing body of evidence emphasizing that equitable AI integration in education must be culturally responsive, taking into account diverse conceptions of academic integrity. We advocate for the development of nuanced AI-use guidelines that are sensitive to local cultural contexts while upholding fundamental principles of academic honesty. This study highlights the need for ongoing cross-cultural research to inform ethical AI policies and support responsible GenAI use in global higher education settings.

cs.CY↗

arXiv2025

Automatic Piecewise Linear Regression for Predicting Student Learning Satisfaction

Although student learning satisfaction has been widely studied, modern techniques such as interpretable machine learning and neural networks have not been sufficiently explored. This study demonstrates that a recent model that combines boosting with interpretability, automatic piecewise linear regression(APLR), offers the best fit for predicting learning satisfaction among several state-of-the-art approaches. Through the analysis of APLR's numerical and visual interpretations, students' time management and concentration abilities, perceived helpfulness to classmates, and participation in offline courses have the most significant positive impact on learning satisfaction. Surprisingly, involvement in creative activities did not positively affect learning satisfaction. Moreover, the contributing factors can be interpreted on an individual level, allowing educators to customize instructions according to student profiles.

cs.AI↗

arXiv2024

Exploring Factors Affecting Student Learning Satisfaction during COVID-19 in South Korea

Understanding students' preferences and learning satisfaction during COVID-19 has focused on learning attributes such as self-efficacy, performance, and engagement. Although existing efforts have constructed statistical models capable of accurately identifying significant factors impacting learning satisfaction, they do not necessarily explain the complex relationships among these factors in depth. This study aimed to understand several facets related to student learning preferences and satisfaction during the pandemic such as individual learner characteristics, instructional design elements and social and environmental factors. Responses from 302 students from Sungkyunkwan University, South Korea were collected between 2021 and 2022. Information gathered included their gender, study major, satisfaction and motivation levels when learning, perceived performance, emotional state and learning environment. Wilcoxon Rank sum test and Explainable Boosting Machine (EBM) were performed to determine significant differences in specific cohorts. The two core findings of the study are as follows:1) Using Wilcoxon Rank Sum test, we can attest with 95% confidence that students who took offline classes had significantly higher learning satisfaction, among other attributes, than those who took online classes, as with STEM versus HASS students; 2) An explainable boosting machine (EBM) model fitted to 95.08% accuracy determined the top five factors affecting students' learning satisfaction as their perceived performance, their perception on participating in class activities, their study majors, their ability to conduct discussions in class and the study space availability at home. Positive perceived performance and ability to discuss with classmates had a positive impact on learning satisfaction, while negative perception on class activities participation had a negative impact on learning satisfaction.

cs.CY↗