Techniques Applicable to the Analysis of Educational Data
Thesis title in Czech: | Techniques Applicable to the Analysis of Educational Data |
---|---|
Thesis title in English: | Techniques Applicable to the Analysis of Educational Data |
Key words: | dobývání znalostí|klasifikace|vizualizace|sociální sítě|rozhodovací stromy|klastrování|míry centrality|detekce komunit |
English key words: | data mining|classification|visualization|social networks|decision trees|clustering|centrality measures|community detection |
Academic year of topic announcement: | 2022/2023 |
Thesis type: | diploma thesis |
Thesis language: | angličtina |
Department: | Department of Theoretical Computer Science and Mathematical Logic (32-KTIML) |
Supervisor: | doc. RNDr. Iveta Mrázová, CSc. |
Author: | hidden - assigned and confirmed by the Study Dept. |
Date of registration: | 02.04.2023 |
Date of assignment: | 04.04.2023 |
Confirmed by Study dept. on: | 14.04.2023 |
Opponents: | RNDr. Jan Hric |
Guidelines |
The student shall review the following data mining topics in his diploma thesis:
- overview of the paradigms relevant to decision trees (e.g., ID3 and its variants, CART, CHAID, bagging, random forests, and boosting), - recapitulation and mutual comparison of various paradigms applicable to pre-processing, visualization, and clustering (feature selection, k-means, LVQ, and k-medoid methods as well its scalable versions), - detection of significant data characteristics through approaches from social network analysis like centrality measures (betweenness, closeness, PageRank, HITS, etc.), community detection methods (Girvan-Newman, Kerninghan-Lin, and Louvain algorithms, among others), or sentiment analysis. The student will focus on some of these topics in more detail. Further, he will propose a suitable strategy for analyzing real-world educational data and shall implement the models. Evaluating the obtained results and gained experience shall form an important part of the thesis. |
References |
1. Some of the textbooks available for the chosen area of research, e.g.:
- B. Liu: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, (2007). - Ch. C. Aggarwal: Data Mining: The Textbook, Springer, (2015). - A.-L. Barabási: Network Science, Cambridge University Press, (2016). http://networksciencebook.com/ 2. Journal papers and other publications: - S. Parthasarathy, Y. Ruan, and V. Satuluri: Community Discovery in Social Networks: Applications, Methods, and Emerging Trends (Chapter 4) from: C. D. Aggarwal (Ed.): Social Network Data Analytics, Springer, (2011), https://link.springer.com/content/pdf/10.1007%2F978-1-4419-8462-3_4.pdf - V. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre: Fast unfolding of communities in large networks, in: Journal of Statistical Mechanics: Theory and Experiment, Vol. 10, (2008), 12 p.: doi:10.1088/1742-5468/2008/10/P10008. - A.Voros, Z. Boda, T. Elmer, M. Hoffman, K. Mepham, I. J. Raabe, and Ch. Stadtfeld: Reprint of: The Swiss Studentlife Study: Investigating the emergence of an undergraduate community through dynamic, multidimensional social network data, in: Social Networks, Vol. 69, (2022), pp. 180-193. - Ch. Stadtfeld, A. Voros, T. Elmer, Z. Boda, and I. J. Raabe: Integration in emerging social networks explains academic failure and success, in: PNAS, Vol. 116, No 3, (2019), pp. 792-797. - M. N. Giannakos, I. O. Pappas, L. Jaccheri, and D. G. Sampson: Understanding student retention in computer science education: The role of environment, gains, barriers and usefulness, in: Education and Information Technologies, (2017), 18p. - T. Shaik, X. Tao, Ch. Dann, H. Xie, Y. Li, and L. Galligan: Sentiment analysis and opinion mining on educational data: A survey, in: Natural Language Processing Journal 2, (2023), 11 p. - Retention in Computer Science Undergraduate Programs in the U.S.: Data Challenges and Promising Interventions, ACM, New York, U.S., https://www.acm.org/binaries/content/assets/education/retention-in-cs-undergrad-programs-in-the-us.pdf - S. Zweben and B. Bizot: 2021 Taulbee Survey CS Enrollment Grows at All Degree Levels, With Increased Gender Diversity, CRA, U.S., May 2022, https://cra.org/wp-content/uploads/2022/05/2021-Taulbee-Survey.pdf and https://cra.org/data/ - ACM-NDC Study 2020-2021, https://www.acm.org/binaries/content/assets/education/acm_ndc_2020-2021.pdf - National Center for Education Statistics (NCES), Integrated Postsecondary Education Data System: https://nces.ed.gov/ipeds/use-the-data 3. Relevant articles from leading academic journals, e.g.: Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, Machine Learning, etc. |