Analyzing university curricula is in high demand among policymakers and other stakeholders nowadays. In this chapter, we presented a data-driven probabilistic student flow model to characterize prerequisite networks. We introduced a novel approach to characterize courses based on their effect on graduation time and illustrated the concept on the electrical engineering program of BME. We also developed a novel software tool based on our proposed approach and we demon-strated that our framework is suitable for evaluating curricular reform and policy changes, moreover it supports a wide range of stakeholders in education.
An interesting line of further research is to refine the model to account for the correlation between the completion of courses e.g. by revealing the Bayes struc-ture of the prerequisite network since success/failure in a course clearly affects the success probability in follow-up courses. Another promising related future direction is to dynamically change the success probabilities for each student by a learning algorithm, based on their prior performance given by the model (instead of pre-categorizing them based on the university entrance score), that also takes into consideration how success or failure in a course correlates with academic performance in other courses. If more data were available then the impact of student and instructor characteristics could be also taken into consideration.
List of my Publications
Journal articles
[M1] B´ela Barab´as, Ottilia F¨ul¨op, and Roland Molontay. “The Co-Authorship Network and Scientific Impact of L´aszl´o Lov´asz”. In: Journal of Combi-natorial Mathematics and CombiCombi-natorial Computing 108 (2019), pp. 187–
192.
[M2] B´ela Barab´as, Ottilia F¨ul¨op, Roland Molontay, and Gyula P´alyi. “Impact of the Discovery of Fluorous Biphasic Systems on Chemistry: A Statistical and Network Analysis”. In: ACS Sustainable Chemistry & Engineering 5.9 (2017), pp. 8108–8118.
[M3] M´at´e Baranyi and Roland Molontay. “Comparing the effectiveness of two remedial mathematics courses using modern regression discontinuity techniques”. In: Interactive Learning Environments (2020).
[M4] Kate Barnes, Tiernon Riesenmy, Minh Duc Trinh, Eli Lleshi, N´ora Balogh, and Roland Molontay. “Dank or Not? – Analyzing and Predicting the Popularity of Memes on Reddit”. In: Applied Network Science (2021).
[M5] Zombor Berezvai, Gergely D´aniel Luk´ats, and Roland Molontay. “A p´enz¨ugyi
¨
oszt¨onz˝ok hat´asa az egyetemi oktat´ok oszt´alyoz´asi gyakorlat´ara”. In:K¨ ozgaz-das´agi Szemle 66.7-8 (2019), pp. 733–750.
[M6] Zombor Berezvai, Gergely D´aniel Luk´ats, and Roland Molontay. “Can Professors Buy Better Evaluation for More Lenient Grading? - The Effect of Grade Inflation on Student Evaluations of Teaching”. In: Assessment
& Evaluation in Higher Education (2020).
[M7] J´ulia Bergmann, Roland Molontay, Mih´aly Szab´o, and D´ora Szekr´enyes.
“Kreditrendszer˝u k´epz´esek mintatanterveinek ´es el˝otanulm´anyi h´al´oinak elemz´ese a hazai matematika alapszakok p´eld´aj´an”. In:Alkalmazott Matem-atikai Lapok 37.1 (2020), pp. 1–37.
[M8] G´abor Horv´ath, Edith Kov´acs, Roland Molontay, and Szabolcs Nov´aczki.
“Copula-based anomaly scoring and localization for large-scale, high-dimensional continuous data”. In:arXiv preprint arXiv:1912.02166 (2019).
Accepted at ACM Transactions on Intelligent Systems and Technology.
[M9] J´ulia Komj´athy, Roland Molontay, and K´aroly Simon. “Transfinite frac-tal dimension of trees and hierarchical scale-free graphs”. In: Journal of Complex Networks 7.5 (2019), pp. 764–791.
[M10] Roland Molontay, No´emi Horv´ath, J´ulia Bergmam, D´ora Szekr´enyes, and Mih´aly Szab´o. “Characterizing Curriculum Prerequisite Networks by a Student Flow Approach”. In: IEEE Transactions on Learning Technolo-gies (2020). Early access.
[M11] Marcell Nagy and Roland Molontay. “Comprehensive Analysis of the Predictive Validity of the University Entrance Score in Hungary”. In:
Assessment & Evaluation in Higher Education (2021).
[M12] Beatrix S´ellei, N´ora Stumphauser, and Roland Molontay. “Traits ver-sus Grades - The incremental predictive power of positive psychological factors over pre-enrollment achievement measures on academic perfor-mance”. In: Applied Sciences (2021).
[M13] Kaludia Zeleny, Roland Molontay, and Szab´o Mih´aly. “A koll´egiumi l´et hat´asa az egyetemi teljes´ıtm´enyre”. In: Statisztikai Szemle 99.1 (2021), pp. 46–79.
Conference papers
[M14] M´at´e Baranyi, Krist´of G´al, Roland Molontay, and Mih´aly Szab´o. “Mod-eling Students’ Academic Performance Using Bayesian Networks”. In:
17th International Conference on Emerging eLearning Technologies and Applications. IEEE. 2019, pp. 42–49.
[M15] M´at´e Baranyi and Roland Molontay. “Effect of Mathematics Remediation on Academic Achievement–A Regression Discontinuity Approach”. In:
2019 International Symposium on Educational Technology (ISET). IEEE.
2019, pp. 29–33.
[M16] M´at´e Baranyi, Marcell Nagy, and Roland Molontay. “Interpretable Deep Learning for University Dropout Prediction”. In: Proceedings of the 21st Annual Conference on Information Technology Education. 2020, pp. 13–
19.
[M17] Attila Egri, Ill´es Horv´ath, Ferenc Kov´acs, and Roland Molontay. “Fin-gerprinting and Reconstruction of Functionals of Discrete Time Markov Chains”. In:International Conference on Analytical and Stochastic Mod-eling Techniques and Applications. Springer. 2016, pp. 140–154.
[M18] Attila Egri, Ill´es Horv´ath, Ferenc Kov´acs, Roland Molontay, and Kriszti´an Varga. “Cross-correlation based clustering and dimension reduction of multivariate time series”. In:2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES). IEEE. 2017, pp. 000241–000246.
[M19] D´aniel M´arton Horv´ath, Roland Molontay, and Mih´aly Szab´o. “Visualiz-ing student flows to track retention and graduation rates”. In:2018 22nd IEEE International Conference on Information Visualisation (IV). 2018, pp. 338–343.
[M20] Ill´es Horv´ath, Istv´an Finta, Ferenc Kov´acs, Andr´as M´esz´aros, Roland Molontay, and Kriszti´an Varga. “Markovian queue with garbage collec-tion”. In:International Conference on Analytical and Stochastic Modeling Techniques and Applications. Springer. 2017, pp. 109–124.
[M21] No´emi Horv´ath, Roland Molontay, and Mih´aly Szab´o. “Who are the most important “suppliers” for universities? - Ranking secondary schools based on their students’ university performance”. In: 2nd Danube Conference for Higher Education Management: In search of excellence in higher ed-ucation. 2019, pp. 133–143.
[M22] Botond Kiss, Marcell Nagy, Roland Molontay, and B´alint Csabay. “Pre-dicting Dropout Using High School and First-semester Academic Achieve-ment Measures”. In:17th International Conference on Emerging eLearn-ing Technologies and Applications. IEEE. 2019, pp. 383–389.
[M23] Roland Molontay and Marcell Nagy. “Two decades of network science: as seen through the co-authorship network of network scientists”. In: Pro-ceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2019, pp. 578–583.
[M24] Roland Molontay and Kitti Varga. “On the complexity of color-avoiding site and bond percolation”. In:International Conference on Current Trends in Theory and Practice of Informatics. Springer. 2019, pp. 354–367.
[M25] Marcell Nagy and Roland Molontay. “On the structural properties of so-cial networks and their measurement-calibrated synthetic counterparts”.
In:Proceedings of the 2019 IEEE/ACM International Conference on Ad-vances in Social Networks Analysis and Mining. 2019, pp. 584–588.
[M26] Marcell Nagy and Roland Molontay. “Predicting dropout in higher educa-tion based on secondary school performance”. In: 2018 IEEE 22nd Inter-national Conference on Intelligent Engineering Systems (INES). IEEE.
2018, pp. 000389–000394.
[M27] Marcell Nagy, Roland Molontay, and Mih´aly Szab´o. “A Web Application for Predicting Academic Performance and Identifying the Contributing Factors”. In: 47th SEFI Conference. 2019.
Preprints and other works
[M28] Olivier Guin, Marcell Nagy, and Roland Molontay. “Comparing Struc-tural Feature-based and Graph Embedding-based Network Classification Methods”. Paper presented at NetSci-X 2020, Tokyo, Japan. 2020.
[M29] P´eter Kov´acs, Marcell Nagy, and Roland Molontay. “Comparative Anal-ysis of Box-Covering Algorithms For Fractal Networks”. in preparation.
2021.
[M30] Roland Molontay. “Fractal Characterization of Complex Networks”. MA thesis. Department of Stochastics, Budapest University of Technology and Economics, 2015.
[M31] Roland Molontay. “Networks and fractals”. BSc Thesis. Department of Stochastics, Budapest University of Technology and Economics, 2013.
[M32] Roland Molontay and Marcell Nagy. “Twenty Years of Network Science:
A Bibliographic and Co-authorship Network Analysis”. In:arXiv preprint arXiv:2001.09006 (2020).
[M33] Marcell Nagy and Roland Molontay. “Comparing Box-Covering Algo-rithms for Fractal Dimension of Complex Networks”. Poster presented at NetSci-X 2020, Tokyo, Japan. 2020.
[M34] Marcell Nagy and Roland Molontay. “Data-driven analysis of complex networks and their model-generated counterparts”. In: arXiv preprint arXiv:1810.08498 (2018).
[M35] Marcell Nagy and Roland Molontay. Twenty Years of Network Science – Supplementary Material. 2020. url: https://github.com/marcessz/
Twenty-Years-of-Network-Science.
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