Student Engagement Prediction in Online Session
Article Sidebar
Main Article Content
Abstract
The individuals who make up the globe constantly advance into the future and improve both their personal lives and the conditions in which they live. One ‘s education is the basis of one ‘s knowledge. Humans' education has a significant impact on their behavior and IQ. Through the use of diverse pedagogical techniques, instructors always play a part in changing students' ways of thinking and developing their social and cognitive abilities. However, getting students to participate in an online class is still difficult. In this study, we created an intelligent predictive system that aids instructors in anticipating students' levels of interest based on the information they learn in an online session and in motivating them through regular feedback. The level of students' engagement is divided into three tiers based on their online session activities (Not engaged, passively engaged, and actively engaged). The given data was subjected to the application of Decision Trees (DT), Random Forest Classifiers (RF), Logistic Regression (LR), and Long Short-Term Memory Networks are among the numerous machine learning approaches (LSTM). According to performance measurements, LSTM is the most accurate machine learning algorithm. The instructors can get in touch with the students and inspire them by improving their teaching approaches based on the results the system produces.
Article Details
References
M. H. Baturay, “An Overview of the World of MOOCs,” Procedia Soc Behav Sci, vol. 174, pp. 427–433, Feb. 2015, https://doi.org/10.1016/j.sbspro.2015.01.685
I. Maiz, “Research on MOOCs: Trends and Methodologies.”
Robert M. Carini and George D. Kuh, “Student engagement and student learning: Testing the linkages,” Res High Educ, vol. 47, no. 1, Feb. 2006.
M. D. Dixson, “Measuring Student Engagement in the Online Course: The Online Student Engagement Scale (OSE).”
J. S. Lee, “The relationship between student engagement and academic performance: Is it a myth or reality?” Journal of Educational Research, vol. 107, no. 3, pp. 177–185, May 2014, https://doi.org/10.1080/00220671.2013.807491.
J. VanderPlas, “Python Data Science Handbook: Essential Tools for Working with Data.”
M. Atherton, M. Shah, J. Vazquez, Z. Griffiths, B. Jackson, and C. Burgess, “Using learning analytics to assess student engagement and academic outcomes in open access enabling programs,” Open Learning, vol. 32, no. 2, pp. 119–136, May 2017,
https://doi.org/10.1080/02680513.2017.1309646
Mutahi, “Studying Engagement and Performance with Learning Technology in an African Classroom,” Association for Computing Machinery, pp. 148–152, Mar. 2017.
Timothy Rodgers, “Student engagement in the e-learning process and the impact on their grades,” International Journal of Cyber Society and Education, vol. 1, no. 2, Dec. 2008, Accessed: Jan. 18, 2023. [Online]. https://www.learntechlib.org/p/209167
Zhuang, “Understanding Engagement through Search Behaviour,” Association for Computing Machinery, pp. 1957–1966, Nov. 2017, https://doi.org/10.1145/3132847.3132978
Arvid Kappas, “The Affective Computing Approach to Affect Measurement,” vol. 10, no. 2, Sep. 2017, Accessed: Jan. 18, 2023. [Online]. https://doi.org/10.1177/1754073917696583
F. H. Veiga, “Proposal to the PISA of a New Scale of Students’ Engagement in School,” Procedia Soc Behav Sci, vol. 46, pp. 1224–1231, 2012, https://doi.org/10.1016/j.sbspro.2012.05.279
B. J. Mandernach, “Assessment of Student Engagement in Higher Education: A Synthesis of Literature and Assessment Tools,” 2015.
B. Motz, J. Quick, N. Schroeder, J. Zook, and M. Gunkel, “The validity and utility of activity logs as a measure of student engagement,” in ACM International Conference Proceeding Series, Mar. 2019, pp. 300–309. https://doi.org/10.1145/3303772.3303789
R.-M. Conrad and • J Ana Donaldson, “Engaging the Online Learner: Activities and Resources for Creative Instruction.”
C. Iwendi, S. Ponnan, R. Munirathinam, K. Srinivasan, and C. Y. Chang, “An efficient and unique TF/IDF algorithmic model-based data analysis for handling applications with big data streaming,” Electronics (Switzerland), vol. 8, no. 11, Nov. 2019, https://doi.org/10.3390/electronics8111331
C. Iwendi, S. Khan, J. H. Anajemba, M. Mittal, M. Alenezi, and M. Alazab, “The use of ensemble models for multiple class and binary class classification for improving intrusion detection systems,” Sensors (Switzerland), vol. 20, no. 9, May 2020,
https://doi.org/10.3390/s20092559
F. Ofori and R. Gitonga, “Using Machine Learning Algorithms to Predict Students’ Performance and Improve Learning Outcome: A Literature-Based Review Cloud security View project Cloud security View project.” [Online]. https://www.researchgate.net/publication/340209478