Quantitative Analysis of Behavioral Engagement Based on Machine Learning Algorithm
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Abstract
To quantitatively characterize learners’ behavioral engagement in specialized electromagnetic-related coursework and extract its core predictive determinants, this paper presents a machine-learning-enabled quantitative analytical framework centered on Random Forest Regression (RFR). Targeting one designated course as the empirical research setting, we adopted the IPTCSSE scale paired with a 10-point Likert scale to collect behavioral engagement data. Out of 4,700 distributed questionnaires, 3,992 valid samples were retained for subsequent statistical and algorithmic analysis. Complementing conventional SPSS 17.0 statistical processing with RFR overcomes the limitations of traditional regression-only analysis in nonlinear factor weight estimation. Reliability testing yielded a satisfactory Cronbach’s Alpha value for the IPTCSSE scale, and Pearson correlation analysis validated its construct validity; KMO measure and Bartlett’s sphericity test confirmed the dataset’s eligibility for factor extraction. Seven critical drivers of behavioral engagement were identified: attention to practice, effort level, instructional guidance, instructional media, autonomous study, course attendance, and in-class interaction. Descriptive analysis demonstrated generally favorable ratings for all factors with significant positive inter-factor correlations, where classroom interaction obtained the highest score and effort level the lowest. RFR further quantified factor predictive contributions: autonomous study and attendance shared identical impact weights (eigenvalue=0.03), whereas classroom interaction exhibited the minimal weight (eigenvalue=0.00). This work verifies the feasibility of applying ensemble machine learning to quantify learner behavioral engagement within education, discusses implementation limitations regarding sample coverage and algorithm tuning, and offers empirical insights for optimizing instructional engagement in advanced teaching.
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References
N. Ningbin, C. Xiuqin, and H. Yin, “Significance and possibility of VR technology embedded in the teaching of ideological and political theory course in colleges and universities,” IEEE Access, vol. 8, pp. 209835–209843, 2020.
M. A. Griffin, S. K. Parker, and A. Neal, “Is behavioral engagement a distinct and useful construct?,” Industrial and Organizational Psychology, vol. 1, no. 1, pp. 48–51, 2008.
M. Schreiner, T. Fischer, and R. Riedl, “Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda,” Electronic Commerce Research, vol. 21, pp. 329–345, 2021.
S. A. Stumpf, W. G. Tymon Jr, and N. H. van Dam, “Felt and behavioral engagement in workgroups of professionals,” Journal of Vocational Behavior, vol. 83, no. 3, pp. 255–264, 2013.
E. S. Lane and S. E. Harris, “A new tool for measuring student behavioral engagement in large university classes,” Journal of College Science Teaching, vol. 44, no. 6, pp. 83–91, 2015.
W. Tafesse and A. Wien, “Using message strategy to drive consumer behavioral engagement on social media,” Journal of Consumer Marketing, vol. 35, no. 3, pp. 241–253, 2018.
M. Li, “Learning behaviors and cognitive participation in online-offline hybrid learning environment,” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 1, pp. 146–159, 2022.
D. T. T. Rani Gul, S. Batool, U. Ishfaq, and M. H. Nawaz, “Effect of different classroom predicators on students behavioral engagement,” Journal of Positive School Psychology, vol. 6, no. 8, pp. 3759–3778, 2022.
J. N. Hughes, W. Wu, and S. G. West, “Teacher performance goal practices and elementary students’ behavioral engagement: A developmental perspective,” Journal of School Psychology, vol. 49, no. 1, pp. 1–23, 2011.
K. A. Gamage, A. Gamage, and S. C. Dehideniya, “Online and hybrid teaching and learning: Enhance effective student engagement and experience,” Education Sciences, vol. 12, no. 10, p. 651, 2022.
V. Hospel, B. Galand, and M. Janosz, “Multidimensionality of behavioural engagement: Empirical support and implications,” International Journal of Educational Research, vol. 77, pp. 37–49, 2016.
D. Liu, R. Santhanam, and J. Webster, “Toward meaningful engagement,” MIS Quarterly, vol. 41, no. 4, pp. 1011–1034, 2017.
A. González and P. V. Paoloni, “Behavioral engagement and disaffection in school activities: exploring a model of motivational facilitators and performance outcomes,” Annals of Psychology, vol. 31, no. 3, pp. 869–878, 2015.
H. M. Lai, “Understanding what determines university students’ behavioral engagement in a group-based flipped learning context,” Computers & Education, vol. 173, p. 104290, 2021.
S. Büchele, “Evaluating the link between attendance and performance in higher education: The role of classroom engagement dimensions,” Assessment & Evaluation in Higher Education, vol. 46, no. 1, pp. 132–150, 2021.
D. T. T. Rani Gul, S. Batool, U. Ishfaq, and M. H. Nawaz, “Effect of different classroom predicators on students behavioral engagement,” Journal of Positive School Psychology, vol. 6, no. 8, pp. 3759–3778, 2022.
T. D. Nguyen, M. Cannata, and J. Miller, “Understanding student behavioral engagement: Importance of student interaction with peers and teachers,” The Journal of Educational Research, vol. 111, no. 2, pp. 163–174, 2018.
M. Pagani and G. Malacarne, “Experiential engagement and active vs. passive behavior in mobile location-based social networks: The moderating role of privacy,” Journal of Interactive Marketing, vol. 37, no. 1, pp. 133–148, 2017.
L. D. LaDage, S. L. Tornello, J. M. Vallejera, E. E. Baker, Y. Yan, and A. Chowdhury, “Variation in behavioral engagement during an active learning activity leads to differential knowledge gains in college students,” Advances in Physiology Education, vol. 42, no. 1, pp. 99–103, 2018.
M. Pagani and G. Malacarne, “Experiential engagement and active vs. passive behavior in mobile location-based social networks: The moderating role of privacy,” Journal of Interactive Marketing, vol. 37, no. 1, pp. 133–148, 2017.
S. Gomes, L. Costa, C. Martinho, J. Dias, G. Xexéo, and A. Moura Santos, “Modeling students’ behavioral engagement through different in-class behavior styles,” International Journal of STEM Education, vol. 10, no. 1, p. 21, 2023.
J. D. Finn, G. M. Pannozzo, and K. E. Voelkl, “Disruptive and inattentive-withdrawn behavior and achievement among fourth graders,” The Elementary School Journal, vol. 95, no. 5, pp. 421–434, 1995.
E. Cappella, H. Y. Kim, J. W. Neal, and D. R. Jackson, “Classroom peer relationships and behavioral engagement in elementary school: The role of social network equity,” American Journal of Community Psychology, vol. 52, pp. 367–379, 2013.
E. T. Pascarella and P. T. Terenzini, “Student-faculty informal contact and college persistence: A further investigation,” The Journal of Educational Research, vol. 72, no. 4, pp. 214–218, 1979.
J. Serrano-Sánchez, J. Zimmermann, and K. Jonkmann, “When in Rome... A longitudinal investigation of the predictors and the development of student sojourners’ host cultural behavioral engagement,” International Journal of Intercultural Relations, vol. 83, pp. 15–29, 2021.
G. Perugia, R. Van Berkel, M. Díaz-Boladeras, A. Català-Mallofré, M. Rauter-berg, and E. Barakova, “Understanding engagement in dementia through behavior. The ethographic and laban-inspired coding system of engagement (ELICSE) and the evidence-based model of engagement-related behavior (EMODEB),” Frontiers in Psychology, vol. 9, p. 690, 2018.
J. A. Fredricks, P. C. Blumenfeld, and A. H. Paris, “School engagement: Potential of the concept, state of the evidence,” Review of Educational Research, vol. 74, no. 1, pp. 59–109, 2004.
B. O. Hier, C. K. MacKenzie, T. L. Ash, S. C. Maguire, K. A. Nelson, E. C. Helminen,... W. E. Sullivan, “Effects of the good behavior game on students’ academic engagement in remote classrooms during the COVID-19 pandemic,” Journal of Positive Behavior Interventions, vol. 26, no. 1, pp. 14– 26, 2024.
J. H. Nieminen and D. Boud, “Placing authenticity at the heart of student self-assessment: an integrative review,” Teaching in Higher Education, pp. 1–23, 2024.
K. Nepal and R. C. Kafle, “Who can predict their performance more accurately? An investigation of undergraduate students’ self-assessment behavior in mathematics courses,” Metacognition and Learning, pp. 1–17, 2024.