Students Attitude towards Video-based Learning Machine Learning Analysis with Rapid Software
Abstract
The recent computer and Internet technologies have dramatically impacted many facets of education. There has been a rapid rise mainly since the COVID19 pandemic in the use of video-based learning implemented via online classroom setting. Regardless of its usefulness and practicality, the educational technologies adoption has its challenges faced by educators. The main challenge is to get intuition of the students’ attitude that will influence the students’ performances. In supporting the intervention approaches, machine learning techniques have been widely utilized. Another challenge is the difficulty to implement machine learning analysis by the educators. The emergence of rapid software platform can be useful for them, but the existence of these software is unrenowned. The goals of this paper are to: 1) provide fundamental experimental works of the machine learning implementation based on a new rapid software framework and 2) present the ability of machine learning in classifying students’ attitude towards video-based learning. Data were collected from a university level accounting course (n=103), involving students who have different experienced or exposure on video-based online learning. Three machine learning algorithms (Support Vector Machine, Random Forest, and Decision Tree) have been tested on the dataset in a rapid software platform. The results show that all the three machine learning algorithms produced high accuracy (above 95%) prediction results based on the hold-out testing dataset. Furthermore, considering inputs of students perceive on the useful and ease of use of video-based learning as well as excluding the demography attributes in the machine learning models seems more useful in the tested case. This paper presents the fundamental design and easy implementation of machine learning for education domain useful for the inexpert data scientists in many fields.
References
[2] J. Gillett-Swan, “The challenges of online learning: Supporting and engaging the isolated learner,” J. Learn. Des., vol. 10, no. 1, pp. 20–30, 2017.
[3] L. Cheng, A. D. Ritzhaupt, and P. Antonenko, “Effects of the flipped classroom instructional strategy on students’ learning outcomes: A meta-analysis,” Educ. Technol. Res. Dev., vol. 67, no. 4, pp. 793–824, 2019.
[4] E. P. Wagner, H. Sasser, and W. J. DiBiase, “Predicting students at risk in general chemistry using pre-semester assessments and demographic information,” J. Chem. Educ., vol. 79, no. 6, p. 749, 2002.
[5] D. Kučak, V. Juričić, and G. DJambić, “MACHINE LEARNING IN EDUCATION-A SURVEY OF CURRENT RESEARCH TRENDS.,” Ann. DAAAM Proc., vol. 29, 2018.
[6] J. L. Rastrollo-Guerrero, J. A. Gómez-Pulido, and A. Durán-Dom\’\inguez, “Analyzing and predicting students’ performance by means of machine learning: A review,” Appl. Sci., vol. 10, no. 3, p. 1042, 2020.
[7] R. Umer, T. Susnjak, A. Mathrani, and S. Suriadi, “On predicting academic performance with process mining in learning analytics,” J. Res. Innov. Teach. Learn., vol. 10, no. 2, pp. 160–176, 2017, doi: 10.1108/jrit-09-2017-0022.
[8] L. Kemper, G. Vorhoff, and B. U. Wigger, “Predicting student dropout: A machine learning approach,” Eur. J. High. Educ., vol. 10, no. 1, pp. 28–47, 2020.
[9] J. Y. Chung and S. Lee, “Dropout early warning systems for high school students using machine learning,” Child. Youth Serv. Rev., vol. 96, pp. 346–353, 2019.
[10] Y. Mourdi, M. Sadgal, H. El Kabtane, and W. B. Fathi, “A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs,” Int. J. Web Inf. Syst., 2019.
[11] A. Akram et al., “Predicting students’ academic procrastination in blended learning course using homework submission data,” Ieee Access, vol. 7, pp. 102487–102498, 2019.
[12] D. Hooshyar, M. Pedaste, and Y. Yang, “Mining educational data to predict students’ performance through procrastination behavior,” Entropy, vol. 22, no. 1, p. 12, 2019.
[13] K. Suresh, J. Meghana, and M. E. Pooja, “Predicting the E-Learners Learning Style by using Support Vector Regression Technique,” in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 350–355.
[14] A. M. F. Yousef et al., “Automatic Identification of Student’s Cognitive Style from Online Laboratory Experimentation using Machine Learning Techniques,” in 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2021, pp. 143–149.
[15] R. Ibrahim, N. S. Leng, R. C. M. Yusoff, G. N. Samy, S. Masrom, and Z. I. Rizman, “E-learning acceptance based on technology acceptance model (TAM),” J. Fundam. Appl. Sci., vol. 9, no. 4S, pp. 871–889, 2017.
[16] B. Bischl et al., “mlr: Machine Learning in R,” J. Mach. Learn. Res., vol. 17, no. 1, pp. 5938–5942, 2016.
[17] S. Raschka and V. Mirjalili, Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd, 2019.
[18] J. Hao and T. K. Ho, “Machine learning made easy: a review of scikit-learn package in python programming language,” J. Educ. Behav. Stat., vol. 44, no. 3, pp. 348–361, 2019.
[19] D. Pal and S. Patra, “University students’ perception of video-based learning in times of COVID-19: A TAM/TTF perspective,” Int. J. Human--Computer Interact., vol. 37, no. 10, pp. 903–921, 2021.
[20] A. S. Abd Rahman, S. Masrom, R. Abdul Rahman, and R. Ibrahim, “Rapid Software Framework for the Implementation of Machine Learning Classification Models,” Int. J. Emerg. Technol. Adv. Eng., vol. 11, pp. 8–18, 2021, doi: 10.46338/ijetae0821_02.
