Student Perceptions Analysis of Online Learning: A Machine Learning Approach

Abstract

The covid-19 pandemic is currently occurring affects almost all aspects of life, including education. School From Home (SFH) is one of the ways to prevent the spread of Covid-19. The face-to-face learning method in class turns into online learning using information technology facilities. Even though there are many barriers to implementing classes online, online learning provides a new perspective for students' learning process. One of the factors for the online learning process's success is the interaction between the two main actors in the learning process, i.e., lecturers and students. The study's purpose was to analyze students' perceptions of the online learning process. The research data were obtained from a student questionnaire, which included five main criteria in the learning process: 1) self-management aspects, 2) personal efforts, 3) technology utilization, 4) perceptions of self-roles, and 5) perceptions of the role of the lecturer. Students provide an assessment through a questionnaire about the online learning methods they experience during the Covid-19 pandemic. The random forest algorithm was applied to examine data. The study results were focused on three main criteria (variable importance) that affect students' perceptions of the online learning process. The results described that the students' satisfaction in online learning is influenced by 1) The relationship between students and lecturers. 2) The learning materials need to be changed and adapted to the online learning method; 3) The use of technology to access online learning. The study contributes to improving the online learning method for the student.

Visualization analysis on the research topic and hotspot of online learning by using CiteSpace—Based on the Web of Science core collection (2004–2022)

“…For example, based on the neural network algorithm, Peng (2022) developed a method for intelligently teaching English, based on the new algorithm, and the effectiveness of the system for English online learning has been verified. Suparwito et al (2021) adopted five criteria, namely self-management, personal effort, technology use, self-role recognition, and lecturer role recognition, to analyze students’ views on online learning, and used a random forest algorithm to examine the data. The results showed that the factors affecting students’ satisfaction with online learning included relationships between students and teachers, the adaptation of learning materials to online learning methods, and the use of technology for online learning.…”

“…For example, based on the neural network algorithm, Peng (2022) developed a method for intelligently teaching English, based on the new algorithm, and the effectiveness of the system for English online learning has been verified. Suparwito et al (2021) adopted five criteria, namely selfmanagement, personal effort, technology use, self-role recognition, and lecturer role recognition, to analyze students' views on online learning, and used a random forest algorithm to examine the data.…”

Knowledge Map of Digital Learning and Sustainable Development (2004-2022): a Scientometric Analysis by Using Citespace

“…For example, based on the neural network algorithm, Peng (2022) developed a method for intelligently teaching English, based on the new algorithm, and the effectiveness of the system for English digital learning has been verified [92]. Suparwito (2021) adopted five criteria, namely self-management, personal effort, technology use, self-role recognition, and lecturer role recognition, to analyze students' views on digital learning, and used a random forest algorithm to examine the data [93]. The results showed that the factors affecting students' satisfaction with digital learning included relationships between students and teachers, the adaptation of learning materials to digital learning methods, and the use of technology for digital learning.…”

Machine learning prediction of video-based learning with technology acceptance model

“…Unlike prior studies [7]- [10] that employed traditional statistical method, this study attempts to construct video-based learning usage based on students' perception and attitudes to be analyzed with machine learning prediction technique. Previous research that used machine learning for prediction, classification and detection problems in financial, accounting and education domains highlighted the effectiveness and accuracy of such methods to that of traditional statistical methods in problems such as in detection of financial fraud [12], students and teachers' performance [13], [14], firm performance [15] and education technologies adoption [16]- [23]. Despite the wiser used machine learning in accounting and education areas, yet study on machine learning prediction and classification on accounting education is inadequate.…”

“…This study has two main contributions. First, it attempts to extend prior works [16]- [23] in constructing online education and technologies adoption prediction model using machine learning algorithms in order to deepen current understanding on the acceptance of video-based learning as one of the educational technologies learning tools in online learning environment especially during Covid-19 pandemic. Second, it provides another design and implementation of machine learning prediction in video-based learning by using three constructs of TAM.…”