Prediction of Secondary-School Student Cognitive Performance in Informatics

Dimitrios Varsos; Nikolaos Zygouris; Kostas Kolomvatsos; Antonios Dadaliaris; Georgios Dimitriou

doi:10.3991/ijep.v16i1.56637

Authors

Dimitrios A. Varsos University of Thessaly, Lamia, Greece https://orcid.org/0009-0003-4167-858X
Nikolaos C. Zygouris University of Thessaly, Lamia, Greece https://orcid.org/0000-0001-8348-6533
Kostas Kolomvatsos University of Thessaly, Lamia, Greece
Antonios N. Dadaliaris University of Thessaly, Lamia, Greece https://orcid.org/0000-0003-0777-6867
Georgios Dimitriou University of Thessaly, Lamia, Greece https://orcid.org/0000-0002-1726-9044

DOI:

https://doi.org/10.3991/ijep.v16i1.56637

Keywords:

Predictive Modelling, Machine Learning, Classification Algorithms, Flipped Classroom, Jigsaw Technique, Educational Robotics, STEM Epistemology

Abstract

Machine learning techniques for the prediction of performance in the learning process are primarily studied at the post-secondary level of education. Data sets are usually large at that level, resulting in predictive models that have a high accuracy. In contrast, limited research has been conducted at the secondary school level, mostly due to the typically small data sizes and the unique educational challenges of that level. In the present study we aimed to address such issues by implementing a model to predict the final performance of lower secondary school students in a course on Informatics, using a teaching scenario that combined the flipped classroom, a variation of the jigsaw technique and educational robotics. Given the student grades from the first third of the year, as well as demographics data, we used an IBM data analytics tool to create and test several predictive models. The CHAID algorithm achieved the highest accuracy (82.14%) and AUC value, outperforming others like Quest, C5, BN, and RF. The tool also pinpointed the educational activity that was the most significant predictor of final performance, indicating its strong instructional value. Despite the relatively small dataset, the results suggest that careful parameter selection can yield models that predict learner performance with a high accuracy and assist educators in continuously improving their teaching for better student outcomes.

Author Biographies

Dimitrios A. Varsos, University of Thessaly, Lamia, Greece

Dimitrios Varsos is a PhD candidate in the Department of Computer Science and Telecommunications at the University of Thessaly, focusing on Teaching of Computer Science and ICT in Secondary Education. He holds an Integrated Master's Degree in Electrical and Computer Engineering and a MSc in Informatics and Computational Biomedicine. He is currently a Computer Science teacher in the Music School of Lamia, Greece. His interests include Teaching methods, Educational Technology, Educational Robotics, Programming, Artificial Intelligence, Machine Learning (E-mail: dvarsos@uth.gr).

Nikolaos C. Zygouris, University of Thessaly, Lamia, Greece

Nikolaos Zygouris completed his undergraduate studies in Psychology at the University of Crete and obtained a Ph.D. in Clinical Neuropsychology from the Department of Special Education at the University of Thessaly. He pursued advanced training in repetitive Transcranial Magnetic Stimulation (rTMS) at the Temerty Center for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada, and in the use of Event-Related Potentials (ERPs) at the EBneuro Academy in Florence, Italy. He has previously served as a Research Associate at the Neuropsychology Laboratory of the University of Thessaly. He currently serves as an Associate Professor in Neuropsychological Assessment of Children and Adolescents, with a specialization in Informatics and New Technologies, at the Department of Computer Science and Telecommunications, University of Thessaly. He is also the Director of the Laboratory of Digital Neuropsychological Assessment (edu.cs.uth.gr). His research interests include the application of electrophysiological methods for brain function assessment and rehabilitation, as well as the design of digital tools and web-based platforms for the evaluation and intervention of cognitive functions, learning difficulties, ADHD, anxiety, and mood disorders. He has authored or co-authored over one hundred scientific publications in peer-reviewed journals, conference proceedings, and academic volumes.

Kostas Kolomvatsos, University of Thessaly, Lamia, Greece

Kostas M. Kolomvatsos received his B.Sc. in Informatics from the Department of Informatics at the Athens University of Economics and Business, his M.Sc. in Computer Science - New Technologies in Informatics and Telecommunications and his Ph.D. from the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens (UoA). Currently, he serves as an Associate Professor in the Department of Informatics and Telecommunications, University of Thessaly. He is the founder of the Intelligent Pervasive Systems (iPRISM) research group (http://www.iprism.eu/) and a research collaborator of the Pervasive Computing Research Group, Department of Informatics and Telecommunications, University of Athens and the Essense research group, School of Computing Science, University of Glasgow. He was a Marie Sklodowska-Curie Research Fellow (Individual Grant) in the MSCA-2016 call. He has participated in and coordinated several European and National research projects. His research interests are in the definition of Intelligent Systems and techniques adopting Artificial Intelligence, (Deep) Machine Learning, Computational Intelligence and Soft Computing for Pervasive (Data/Edge) Computing, Distributed Systems, Internet of Things, and the management of Large Scale Data. In these areas, he has published over 170 articles.

Antonios N. Dadaliaris, University of Thessaly, Lamia, Greece

Antonios Dadaliaris is an Assistant Professor in the Department of Informatics and Telecommunications at the University of Thessaly. He obtained his degree in Computer, Telecommunications and Net-works Engineering in 2005, his master's degree in Computer Science and Technology in 2008, and his PhD from the same department in 2012. His research interests primarily focus on floorplanning and placement of integrated circuits, as well as the broader field of VLSI physical design. Additionally, he is interested in the development and implementation of EDA/CAD tools within standard integrated circuit design flows.

Georgios Dimitriou, University of Thessaly, Lamia, Greece

Georgios Dimitriou received a PhD degree from the University of Illinois, USA. Since September of 2001 he has been teaching undergraduate and graduate courses at the Departments of Electrical and Computer Engineering, and Informatics and Telecommunications of the University of Thessaly. Since October 2017 he holds an Assistant Professor position at the Department of Informatics and Telecommunications of the University of Thessaly. He is currently the Director of the Computer Architecture, Compilers and Security Lab of the department. His research interests include computer architecture, focusing on processors and parallel systems, compilers, focusing on high-level synthesis and optimizations, and also teaching methods of informatics and engineering.

References

[1] J. M. Sáez-López, M. Román-González, and E. Vázquez-Cano, “Visual program-ming languages integrated across the curriculum in elementary school: A two-year case study using ‘scratch’ in five schools,” Comput Educ, vol. 97, pp. 129–141, Jun. 2016, doi: 10.1016/j.compedu.2016.03.003.

[2] R. J. Segura, F. J. del Pino, C. J. Ogáyar, and A. J. Rueda, “VR-OCKS: A virtual reality game for learning the basic concepts of programming,” Computer Applications in Engineering Education, vol. 28, no. 1, pp. 31–41, Jan. 2020, doi: 10.1002/cae.22172.

[3] R. Kadar, N. Abdul Wahab, J. Othman, M. Shamsuddin, and S. B. Mahlan, “A Study of Difficulties in Teaching and Learning Programming: A Systematic Literature Re-view,” International Journal of Academic Research in Progressive Education and Develop-ment, vol. 10, no. 3, Aug. 2021, doi: 10.6007/ijarped/v10-i3/11100.

[4] E. Fatourou, N. C. Zygouris, T. Loukopoulos, and G. I. Stamoulis, “Teaching con-current programming concepts using scratch in primary school: Methodology and evalua-tion,” International Journal of Engineering Pedagogy, vol. 8, no. 4, pp. 89–105, 2018, doi: 10.3991/ijep.v8i4.8216.

[5] A. Ioannou and E. Makridou, “Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work,” Educ Inf Technol (Dordr), vol. 23, no. 6, pp. 2531–2544, Nov. 2018, doi: 10.1007/s10639-018-9729-z.

[6] S. A. Castañeda Rincón, H. Moreno Gudiño, and R. de J. Gil Herrera, “Robotics for inclusive education: Combining active methodologies in a classroom,” Contemp Educ Technol, vol. 16, no. 3, p. ep522, Aug. 2024, doi: 10.30935/cedtech/14939.

[7] A. R. El Mohamad, “Educational Robotics Is a Useful Tool in Education,” 2019. [Online]. Available: https://www.researchgate.net/publication/332401229

[8] B. Birgili, F. N. Seggie, and E. Oğuz, “The trends and outcomes of flipped learning research between 2012 and 2018: A descriptive content analysis,” Journal of Computers in Education, vol. 8, no. 3, pp. 365–394, Sep. 2021, doi: 10.1007/s40692-021-00183-y.

[9] Ş. Kaya and A. Çebi, “Unleashing the potential of flipped learning in K–12: A re-view of experimental studies,” Journal of Research on Technology in Education, pp. 1–15, Feb. 2025, doi: 10.1080/15391523.2025.2456052.

[10] S. R. Sobral, “Flipped classrooms for introductory computer program-ming courses,” International Journal of Information and Education Technology, vol. 11, no. 4, pp. 178–183, 2021, doi: 10.18178/ijiet.2021.11.4.1508.

[11] D. C. D. van Alten, C. Phielix, J. Janssen, and L. Kester, “Effects of flipping the classroom on learning outcomes and satisfaction: A meta-analysis,” Nov. 01, 2019, Elsevier Ltd. doi: 10.1016/j.edurev.2019.05.003.

[12] S. Beltozar-Clemente, O. Iparraguirre-Villanueva, J. Zapata-Paulini, and M. Cabanillas-Carbonell, “Changing Mathematical Paradigms at the University Level: Feed-back from a Flipped Classroom at a Peruvian University,” International Journal of Engi-neering Pedagogy, vol. 13, no. 6, pp. 76–89, 2023, doi: 10.3991/ijep.v13i6.40763.

[13] M. H. A. Hassan and N. A. Othman, “Flipped classroom approach in rig-id body dynamics: A case study of five-semester observation,” International Journal of Engineering Pedagogy, vol. 11, no. 1, pp. 87–94, 2021, doi: 10.3991/IJEP.V11I1.15005.

[14] S. Jacques and T. Lequeu, “The attractiveness of reversing teaching forms feedback on an electrical engineering course,” International Journal of Engineering Pedagogy, vol. 10, no. 3, pp. 21–34, 2020, doi: 10.3991/IJEP.V10I3.12361.

[15] D. T. K. Ng, E. H. L. Ng, and S. K. W. Chu, “Engaging students in crea-tive music making with musical instrument application in an online flipped classroom,” Educ Inf Technol (Dordr), vol. 27, no. 1, pp. 45–64, Jan. 2022, doi: 10.1007/s10639-021-10568-2.

[16] J. Cui and S. Yu, “Fostering deeper learning in a flipped classroom: Ef-fects of knowledge graphs versus concept maps,” British Journal of Educational Technolo-gy, vol. 50, no. 5, pp. 2308–2328, 2019, doi: 10.1111/bjet.12841.

[17] A. Evseeva and A. Solozhenko, “Use of Flipped Classroom Technology in Language Learning,” Procedia Soc Behav Sci, vol. 206, pp. 205–209, Oct. 2015, doi: 10.1016/j.sbspro.2015.10.006.

[18] K. Al-Said, I. Krapotkina, F. Gazizova, and N. Maslennikova, “Distance learning: studying the efficiency of implementing flipped classroom technology in the edu-cational system,” Educ Inf Technol (Dordr), vol. 28, no. 10, pp. 13689–13712, Oct. 2023, doi: 10.1007/s10639-023-11711-x.

[19] K. Smahi, O. Labouidya, and K. El Khadiri, “Enhancing Online Assess-ment Quality in Higher Education: The Design of Moodle Plug-in for Personalized Exam Revision (PER),” International Journal of Engineering Pedagogy, vol. 15, no. 5, pp. 127–140, Jul. 2025, doi: 10.3991/ijep.v15i5.55055.

[20] N. Salma, “Collaborative Learning: An Effective Approach to Promote Language Development,” International Journal of Social Sciences & Educational Studies, vol. 7, no. 2, 2020, doi: 10.23918/ijsses.v7i2p57.

[21] T. Van Dang, T. G. B. Phung, D. M. Vu, and T. T. T. Nguyen, “The Ap-plication of Problem-Based Learning in Soft Skills Courses: An Experiment in Classes with Multidisciplinary Students in Vietnam,” International Journal of Engineering Pedagogy (iJEP), vol. 15, no. 5, pp. 20–41, Jul. 2025, doi: 10.3991/ijep.v15i5.53663.

[22] D. Chopra, G. Kwatra, B. Bhandari, J. K. Sidhu, J. Rai, and C. D. Tripa-thi, “Jigsaw Classroom: Perceptions of Students and Teachers,” Med Sci Educ, vol. 33, no. 4, pp. 853–859, Aug. 2023, doi: 10.1007/s40670-023-01805-z.

[23] A. T. W. Yu, “Using jigsaw method to enhance the learning of research and consultancy techniques for postgraduate students,” Engineering, Construction and Architectural Management, vol. 24, no. 6, pp. 1081–1091, 2017, doi: 10.1108/ECAM-03-2016-0080.

[24] R. Sell, R. Razdan, K. Kase, and T. Rüütmann, “The Role of AI Chatbots in Engineering Education: Experimental Findings and Implementation Strategies,” Interna-tional Journal of Engineering Pedagogy, vol. 15, no. 5, pp. 4–19, Jul. 2025, doi: 10.3991/ijep.v15i5.56681.

[25] R. Galici, T. Käser, G. Fenu, and M. Marras, “How Close are Predictive Models to Teachers in Detecting Learners at Risk?,” in UMAP 2023 - Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, Inc, Jun. 2023, pp. 135–145. doi: 10.1145/3565472.3595620.

[26] Nguyen Thai Nghe, P. Janecek, and P. Haddawy, “A comparative analy-sis of techniques for predicting academic performance,” in 2007 37th annual frontiers in education conference - global engineering: knowledge without borders, opportunities with-out passports, IEEE, Oct. 2007, pp. T2G-7-T2G-12. doi: 10.1109/FIE.2007.4417993.

[27] ΙΒΜ, “What is predictive analytics?,” https://www.ibm.com/topics/predictive-analytics. Accessed: Oct. 19, 2023. [Online]. Avail-able: https://www.ibm.com/topics/predictive-analytics

[28] G. Shmueli, “To explain or to predict?,” Statistical Science, vol. 25, no. 3, pp. 289–310, Aug. 2010, doi: 10.1214/10-STS330.

[29] W. He, “Examining students’ online interaction in a live video streaming environment using data mining and text mining,” Comput Human Behav, vol. 29, no. 1, pp. 90–102, Jan. 2013, doi: 10.1016/j.chb.2012.07.020.

[30] S. Pal, “Mining Educational Data to Reduce Dropout Rates of Engineer-ing Students,” International Journal of Information Engineering and Electronic Business, vol. 4, no. 2, pp. 1–7, Apr. 2012, doi: 10.5815/ijieeb.2012.02.01.

[31] J. Wong et al., “Educational theories and learning analytics: From data to knowledge: The whole is greater than the sum of its parts,” in Utilizing learning analytics to support study success, Springer International Publishing, 2019, pp. 3–25. doi: 10.1007/978-3-319-64792-0_1.

[32] S. Shen, B. Mostafavi, T. Barnes, and M. Chi, “Exploring Induced Peda-gogical Strategies Through a Markov Decision Process Framework: Lessons Learned,” Jour-nal of Educational Data Mining, vol. 10, no. 3, pp. 27–68, Dec. 2018, doi: 10.5281/zenodo.3554714.

[33] S. Edwards and Z. Li, “Applying recent-performance factors analysis to explore student effort invested in programming assignments,” in International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS), Athens: CSREA, 2018, pp. 3–10. Accessed: Jan. 15, 2023. [Online]. Available: https://www.proquest.com/openview/1344abae126cd4240dfdce3764087786/1?pq-origsite=gscholar&cbl=1976352

[34] Á. Hernández-García, E. Acquila-Natale, J. Chaparro-Peláez, and M. Conde, “Predicting teamwork group assessment using log data-based learning analytics,” Comput Human Behav, vol. 89, pp. 373–384, Dec. 2018, doi: 10.1016/j.chb.2018.07.016.

[35] A. Cano and J. D. Leonard, “Interpretable Multiview Early Warning Sys-tem Adapted to Underrepresented Student Populations,” IEEE Transactions on Learning Technologies, vol. 12, no. 2, pp. 198–211, Apr. 2019, doi: 10.1109/TLT.2019.2911079.

[36] J. L. Harvey and S. A. P. Kumar, “A Practical Model for Educators to Predict Student Performance in K-12 Education using Machine Learning,” in 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Institute of Electrical and Electronics Engineers Inc., Dec. 2019, pp. 3004–3011. doi: 10.1109/SSCI44817.2019.9003147.

[37] D. Scaradozzi, L. Screpanti, and L. Cesaretti, “Machine learning for modeling and identification of educational robotics activities,” in 2021 29th Mediterranean Conference on Control and Automation, MED 2021, Institute of Electrical and Electronics Engineers Inc., Jun. 2021, pp. 753–758. doi: 10.1109/MED51440.2021.9480309.

[38] N. Bousbia and I. Belamri, “Which Contribution Does EDM Provide to Computer-Based Learning Environments?,” in Studies in Computational Intelligence, vol. 524, Springer Verlag, 2014, pp. 3–28. doi: 10.1007/978-3-319-02738-8_1.

[39] H. Mi, Z. Gao, Q. Zhang, and Y. Zheng, “Research on Constructing Online Learning Performance Prediction Model Combining Feature Selection and Neural Network,” International Journal of Emerging Technologies in Learning, vol. 17, no. 7, pp. 94–111, 2022, doi: 10.3991/ijet.v17i07.25587.

[40] J. Bravo‐Agapito, C. F. Bonilla, and I. Seoane, “Data mining in foreign language learning,” WIREs Data Mining and Knowledge Discovery, vol. 10, no. 1, pp. 1–16, Jan. 2020, doi: 10.1002/widm.1287.

[41] K. I. Chan, P. I. S. Lei, and P. C. I. Pang, “A Literature Review on Edu-cational Data Mining with Secondary School Data,” in ACM International Conference Pro-ceeding Series, Association for Computing Machinery, Apr. 2023. doi: 10.1145/3599640.3599659.

[42] A. Çetinkaya, Ö. K. Baykan, and H. Kırgız, “Analysis of Machine Learn-ing Classification Approaches for Predicting Students’ Programming Aptitude,” Sustainabil-ity (Switzerland), vol. 15, no. 17, Sep. 2023, doi: 10.3390/su151712917.

[43] OECD (2024), “Education at a Glance 2024: OECD Indicators,” Paris. Accessed: Apr. 10, 2025. [Online]. Available: https://doi.org/10.1787/c00cad36-en

[44] S. Hadjerrouit, “Exploring the Effect of Teaching Methods on Students’ Learning of School Informatics,” in Proceedings of Informing Science & IT Education Conference (InSITE) 2015, Tampa, Florida, 2015, pp. 201–219. doi: https://doi.org/10.28945/2183.

[45] A. Kullberg, Å. Ingerman, and F. Marton, “The Variation Theory of Learning,” in Planning and Analyzing Teaching, Routledge, 2024, pp. 20–29. doi: 10.4324/9781003194903-3.

[46] I. Koprinska, J. Stretton, and K. Yacef, “Predicting student performance from multiple data sources,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Ver-lag, 2015, pp. 678–681. doi: 10.1007/978-3-319-19773-9_90.

[47] A. Almalawi, B. Soh, A. Li, and H. Samra, “Predictive Models for Edu-cational Purposes: A Systematic Review,” Dec. 01, 2024, Multidisciplinary Digital Publish-ing Institute (MDPI). doi: 10.3390/bdcc8120187.

[48] S. Van Goidsenhoven, D. Bogdanova, G. Deeva, S. Vanden Broucke, J. De Weerdt, and M. Snoeck, “Predicting student success in a blended learning environment,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Mar. 2020, pp. 17–25. doi: 10.1145/3375462.3375494.

[49] RbtsInMath, “Content Development for Robotics Applications in Flipped Learning LITERATURE REVIEW, RbtsInMath - Developing Mathematics Achievement through Using Robotics Applications in Flipped Learning,” 2025. Accessed: Apr. 10, 2025. [Online]. Available: https://www.rbtsinmath.eu/wp-content/uploads/2025/03/WP2.A1_Literature-Review_ENG.pdf

[50] E. M. Bakheet and A. M. Gravell, “Would Flipped Classroom be My Approach in Teaching Computing Courses: Literature Review,” in 2021 9th International Conference on Information and Education Technology, ICIET 2021, Institute of Electrical and Electronics Engineers Inc., Mar. 2021, pp. 166–170. doi: 10.1109/ICIET51873.2021.9419631.

[51] J. López-Zambrano, J. A. L. Torralbo, and C. Romero, “Early prediction of student learning performance through data mining: A systematic review,” Psicothema, vol. 33, no. 3, pp. 456–465, 2021, doi: 10.7334/psicothema2021.62.

[52] L. Sandra, F. Lumbangaol, and T. Matsuo, “Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review,” TEM Journal, vol. 10, no. 4, pp. 1919–1927, 2021, doi: 10.18421/TEM104-56.

[53] T. Agasisti and A. J. Bowers, “Data analytics and decision making in ed-ucation: towards the educational data scientist as a key actor in schools and higher educa-tion institutions,” in Handbook of Contemporary Education Economics, Johnes Geraint, Johnes Jill, and Agasisti Tommaso, Eds., Edward Elgar Publishing Limited, 2017, ch. 9, pp. 184–210. doi: https://doi.org/10.4337/9781785369070.00014.

[54] K. Kim, H.-S. Kim, J. Shim, and J. S. Park, “A Study in the Early Predic-tion of ICT Literacy Ratings Using Sustainability in Data Mining Techniques,” Sustainabil-ity, vol. 13, no. 4, p. 2141, Feb. 2021, doi: 10.3390/su13042141.

[55] D. A. Varsos, G. Dimitriou, and N. C. Zygouris, “The implementation of the flipped classroom model in the teaching of educational robotics: A study in secondary school students,” in 2022 IEEE Global Engineering Education Conference (EDUCON), IEEE, Mar. 2022, pp. 1429–1436. doi: 10.1109/EDUCON52537.2022.9766812.

[56] E. Vives, C. Poletti, A. Robert, F. Butera, and P. Huguet, “Learning with Jigsaw: A Systematic Review Gathering All the Pieces of the Puzzle More Than 40 Years Later,” Rev Educ Res, 2024, doi: 10.3102/00346543241230064.

[57] Z. Xu, H. Yuan, and Q. Liu, “Student Performance Prediction Based on Blended Learning,” IEEE Transactions on Education, vol. 64, no. 1, pp. 66–73, Feb. 2021, doi: 10.1109/TE.2020.3008751.

[58] P. R. Perez, C. Romero, and S. Ventura, “A java desktop tool for mining moodle data,” in 4th international conference on educational data mining, Eindhoven: Technische Universiteit Eindhoven, Apr. 2011, pp. 319–320. Accessed: Jan. 22, 2023. [Online]. Available: https://educationaldatamining.org/EDM2011/wp-content/uploads/proc/edm11_proceedings.pdf

[59] P. M. Moreno-Marcos, P. J. Muñoz-Merino, C. Alario-Hoyos, I. Estévez-Ayres, and C. Delgado Kloos, “Analyzing the predictive power for anticipating assignment grades in a massive open online course,” Behavior and Information Technology, vol. 37, no. 10–11, pp. 1021–1036, Nov. 2018, doi: 10.1080/0144929X.2018.1458904.

[60] P. M. Moreno-Marcos, T. C. Pong, P. J. Munoz-Merino, and C. D. Kloos, “Analysis of the Factors Influencing Learners’ Performance Prediction with Learning Ana-lytics,” IEEE Access, vol. 8, pp. 5264–5282, 2020, doi: 10.1109/ACCESS.2019.2963503.

[61] E. Alyahyan and D. Düştegör, “Predicting academic success in higher education: literature review and best practices,” Dec. 01, 2020, Springer. doi: 10.1186/s41239-020-0177-7.

[62] D. P. Kroese, Z. I. Botev, T. Taimre, and R. Vaisman, Data Science and Machine Learning, 1st ed. Chapman and Hall/CRC, 2019. doi: 10.1201/9780367816971.

[63] J. Nouri, M. Saqr, and U. Fors, “Predicting performance of students in a flipped classroom using machine learning: towards automated data-driven formative feed-back,” SYSTEMICS, CYBERNETICS AND INFOR-MATICS, vol. 17, no. 2, pp. 17–21, 2019, Accessed: Jan. 15, 2023. [Online]. Available: https://www.iiisci.org/journal/sci/FullText.asp?var=&id=EB614LI19

[64] E. D. Evangelista, “A Hybrid Machine Learning Framework for Predict-ing Students’ Performance in Virtual Learning Environment,” International Journal of Emerging Technologies in Learning, vol. 16, no. 24, pp. 255–272, 2021, doi: 10.3991/ijet.v16i24.26151.

[65] M. O. Edeh et al., “Bootstrapping random forest and CHAID for predic-tion of white spot disease among shrimp farmers,” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-25109-1.

[66] S. Huang and J. Wei, “Student Performance Prediction in Mathematics Course Based on the Random Forest and Simulated Annealing,” Sci Pro-gram, vol. 2022, pp. 1–9, Mar. 2022, doi: 10.1155/2022/9340434.

[67] IBM Corporation, “IBM SPSS Modeler 18.3 Algorithms Guide,” 2021. [Online]. Available: http://www.ibm.com/spss.

[68] M. Ramaswami and R. Bhaskaran, “A CHAID Based Performance Pre-diction Model in Educational Data Mining,” IJCSI International Journal of Computer Sci-ence Issues, vol. 7, no. 1, 2010, [Online]. Available: www.IJCSI.org

[69] I. Peraić and A. Grubišić, “Predicting Academic Performance of Students in a Computer Programming Course using Data Mining,” International Journal of Engineer-ing Education, vol. 39, no. 4, pp. 836–844, Jul. 2023, [Online]. Available: https://www.researchgate.net/publication/372237042

[70] J. A. Martínez-Carrascal, D. Márquez Cebrián, T. Sancho-Vinuesa, and E. Valderrama, “Impact of early activity on flipped classroom performance prediction: A case study for a first-year Engineering course,” Computer Applications in Engineering Edu-cation, vol. 28, no. 3, pp. 590–605, May 2020, doi: 10.1002/cae.22229.

[71] Z. Balogh and M. Kuchárik, “Predicting student grades based on their us-age of LMS moodle using Petri nets,” Applied Sciences (Switzerland), vol. 9, no. 20, Oct. 2019, doi: 10.3390/app9204211.

[72] A. O. Egbedokun and O. M. Afolabi, “Influence of Distance Learners’ Demographic Variables on Motivation and Performance in Flipped Classroom,” Asian Journal of Education and Social Studies, vol. 40, no. 4, pp. 1–9, Mar. 2023, doi: 10.9734/ajess/2023/v40i4878.

[73] J. Swacha and K. Muszyńska, “Predicting Dropout in Programming MOOCs through Demographic Insights,” Electronics (Switzerland), vol. 12, no. 22, Nov. 2023, doi: 10.3390/electronics12224674.