Forecasting students' adaptability in online entrepreneurship education using modified ensemble machine learning model
Amit Malik,
Edeh Michael Onyema,
Surjeet Dalal,
Umesh Kumar Lilhore,
Darpan Anand,
Ashish Sharma,
Sarita Simaiya
Affiliations
Amit Malik
Department of Computer Science and Engineering, SRM University, Delhi-NCR, Sonipat, Haryana, India
Edeh Michael Onyema
Department of Vocational and Technical Education, Faculty of Education, Alex Ekwueme Federal University, Ndufu-Alike, Abakaliki, Nigeria; Adjunct Faculty, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
Surjeet Dalal
Department of Computer Science and Engineering, Amity University Haryana, Gurugram, Haryana, India; Corresponding author.
Umesh Kumar Lilhore
Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India
Darpan Anand
Department of Computer Engineering, Sir Padampat Singhania University, Udaipur, India
Ashish Sharma
Department of Computer Engineering and Applications, GLA University, Mathura, UP, 281406, India
Sarita Simaiya
Department of Computer Science and Engineering, SRM University, Delhi-NCR, Sonipat, Haryana, India; Department of Vocational and Technical Education, Faculty of Education, Alex Ekwueme Federal University, Ndufu-Alike, Abakaliki, Nigeria; Adjunct Faculty, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India; Department of Computer Science and Engineering, Amity University Haryana, Gurugram, Haryana, India; Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India; Department of Computer Engineering, Sir Padampat Singhania University, Udaipur, India; Department of Computer Engineering and Applications, GLA University, Mathura, UP, 281406, India; Apex Institute of Technology (CSE), Chandigarh University Gharuan, Mohali, Punjab, India
Entrepreneurship education has become essential in recent years. This education system may not be unconnected with the global agitation for value creation, employability skills and job creation. Engaging in entrepreneurial training provides students with the skills needed to enhance their ability to create marketable and profitable solutions to emerging problems. To do this, many emerging entrepreneurs rely on technology to engage in entrepreneurship education. This study presents a machine learning technique to predict the adaptability level of students in online entrepreneurship education. The suitability of different algorithms like Random Forest, C5.0, CART and Artificial Neural Network was examined using the Kaggle Educational dataset. The algorithms recorded a high accuracy rate and affirmed machine learning techniques' ability to forecast students' adaptation to online entrepreneurship training. The findings of this research contribute to the field of online entrepreneurship education by providing a reliable and efficient approach for predicting students' adaptability. The proposed modified ensemble machine learning model can assist educators and administrators in identifying students who may require additional support, tailoring instructional strategies, and designing targeted interventions to enhance their adaptability and overall learning experience in online entrepreneurship education.
