Student Acceptance and Satisfaction with Machine Learning Applications in Higher Education Institutions

Abstract

Purpose: The purpose of the study was to identify the factors that influence students’ acceptance and satisfaction of machine learning (ML) usage and adoption; to analyze the acceptance rating and satisfaction of the students in higher education institutions on machine learning applications on their educational experiences and to determine the predictors that influence the acceptance and satisfaction of machine learning techniques among students in higher education institutions.

Design/methodology/approach: This study adopted a descriptive research design and a quantitative approach. Primary data was obtained through a survey questionnaire where snowball sampling was employed with a total of 176 students from different HEIs. Chi-square test and regression analysis were employed to assess the association and relationship between students’ demographic profiles, specialization, ML acceptance, and satisfaction.

Findings: The results of the study revealed a very high acceptance rating of machine learning among the students and a high level of satisfaction with the ease of use of machine learning techniques. It was also found that there was no significant association between the classification of major/specialization of the field of study and with Acceptance rating of ML and no significant association between the classification of major/specialization and the Ease of use of Machine Learning techniques.  It was also found that there was no significant association between Gender with the Satisfaction Rating of ML and there was no significant association between the Gender and the Acceptance Rating of ML. There was no impact of the demographic factors viz. Gender, Nationality, Residence, Age, Classification of Major/Specialization of the study, and the familiarity level on the students’ Satisfaction with ML techniques while the socioeconomic factors – Marital Status and the Acceptance Rating of Machine Learning techniques had an impact on the Satisfaction of Machine Learning utilization and adoption in higher education institutions.

Research limitations/implications: The study was focused on students’ acceptance and satisfaction with ML utilization and adoption in higher education institutions. This initiative is limited by its sample size and the geographical focus on specific universities.

Social Implications: This study will help policymakers to develop ML applications with intuitive interfaces to ensure accessibility for students to improve user experience and to ensure that ML tools are adaptable to the needs of students avoiding socioeconomic biases to advocate policies that prioritize equitable technology distribution.

Originality / Value: This research explores diverse factors influencing students’ acceptance and satisfaction with machine learning technologies utilized in HEIs. It gives further scope to examine the nuanced effects of other personal factors and contexts (e.g., part-time students or working professionals) on technology acceptance and to conduct longitudinal studies to explore how satisfaction with ML evolves over time and across different educational stages.