Abstract
University/College selection is a daunting task for young adults and their parents alike. This research presents True-Ed Select, a machine learning framework that simplifies the college selection process. The framework uses a four-layered approach comprising user survey, machine learning, consolidation, and recommendation. The first layer collects both the objective and subjective attributes from users that best characterize their ideal college experience. The second layer employs machine learning techniques to analyze the objective and subjective attributes. The third layer combines the results from the machine learning techniques. The fourth layer inputs the consolidated result and presents a user-friendly list of top educational institutions that best match the user’s interests. We use our framework to analyze over 3500 United States post-secondary institutions and show search space reduction to top 20 institutions. This drastically reduced search space facilitates effective and assured college selection for end users.
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Notes
- 1.
The terms ‘university’, ‘college’, ‘institution’, and ‘school’ refer to a post-secondary educational institution and may be used interchangeably.
References
IPEDS: Integrated Postsecondary Education Data System. https://nces.ed.gov/ipeds/. Accessed 14 Mar 2022
Sami: Zip Code Latitude Longitude City State County (2022) https://www.mathworks.com/matlabcentral/fileexchange/45905-zip-code-latitude-longitude-city-state-county, MATLAB Central File Exchange. Retrieved 14 March 2022. https://www.mathworks.com/matlabcentral/fileexchange/45905-zip-code-latitude-longitude-city-state-county. Accessed 14 Mar 2022
Guo, W.-W., Liu, F.: Research on collaborative filtering personalized recommendation algorithm based on deep learning optimization. In: 2019 International Conference on Robots Intelligent System (ICRIS), pp. 90–93 (2019)
He, H., Lin, J.: Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In: Proceedings of the 2016 Conference of the North American chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 937–948 (2016)
Hu, O., FungYuen, K.K., Craig, P.: Towards a recommendation approach for university program selection using primitive cognitive network process. In: 2017 International Conference on Service Systems and Service Management, pp. 1–4 (2017)
Lee, C.P., Ng, Z.B., Low, Y.E., Lim, K.M.: Expert system for university program recommendation. In: 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), pp. 1–6 (2020)
Muladi, U.P., Qomaria, U.: Predicting high school graduates using naive Bayes in state university entrance selections. In: 2020 4th International Conference on Vocational Education and Training (ICOVET), pp. 155–159 (2020)
Nayak, P.K., Madireddy, S., Case, D.M., Stylios, C.D.: Using fuzzy cognitive maps to model university desirability and selection. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1976–1981 (2017)
Nikhil, N., Srivastava, M.M.: Content based document recommender using deep learning. In: 2017 International Conference on Inventive Computing and Informatics (ICICI), pp. 486–489. IEEE (2017)
Powar, V., Girase, S., Mukhopadhyay, D., Jadhav, A., Khude, S., Mandlik, S.: Analysing recommendation of colleges for students using data mining techniques. In: 2017 International Conference on Advances in Computing, Communication and Control (ICAC3), pp. 1–5 (2017)
Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P., Nielek, R., Rutkowski, L.: A content-based recommendation system using neuro-fuzzy approach. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2018)
Sharma, V., Trehan, T., Chanana, R., Dawn, S.: StudieMe: college recommendation system. In: 2019 3rd International Conference on Recent Developments in Control, Automation Power Engineering (RDCAPE), pp. 227–232 (2019)
Shen, V., He, X., Gao, J., Deng, L., Mesnil, G.: A latent semantic model with convolutional-pooling structure for information retrieval. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, pp. 101–110, New York, NY, USA. Association for Computing Machinery (2014)
Van Meteren, R., Van Someren, M.: Using content-based filtering for recommendation. In: Proceedings of the machine learning in the new information age: MLnet/ECML2000 Workshop, vol. 30, pp. 47–56 (2000)
Zhao, W., Zhang, W.: Collaborative filtering service recommendation algorithm based on trusted user and recommendation evaluation. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC), pp. 2248–2255 (2018)
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Cearley, J., Pallipuram, V.K. (2023). True-Ed Select Enters Social Computing: A Machine Learning Based University Selection Framework. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-18344-7_7
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