Machine learning methods for determining the relationship between academic success and data of social network profile
https://doi.org/10.26907/1562-5419-2019-22-2-95-118
Abstract
The paper is aimed to propose the machine learning model for determining the relationship between data of social network profile and academic success of students and predicting the success using the data.
About the Authors
И. Ихсанов
Высшая школа информационных технологий и интеллектуальных систем Казанского (Приволжского) федерального университета
Russian Federation
Russian Federation
И. Шахова
Высшая школа информационных технологий и интеллектуальных систем Казанского (Приволжского) федерального университета
Russian Federation
Russian Federation
References
1. Pritchard M. Using Emotional and Social Factors to Predict Student Success // Journal of College Student Development. 2003. V. 44, No 1. P. 18–28.
2. What your Facebook likes say about you. URL: https://www.cbc.ca/ news/technology/facebook-likes-like-a-gift-1.3893298.
3. Психометрический вступительный экзамен в Израиле // Официальный сайт путеводителя по Израилю. URL: https://guide-israel.ru/country/ 37376-psixometricheskij- vstupitelnyj- ekzamen/.
4. Shuotian B., Tingshao Z., Li C. Big-Five Personality Prediction Based on User Behaviors at Social Network Sites // Cornell University, Tech. Rep. 2012.
5. Friedrichsen M., Mühl-Benninghaus W. Handbook of Social Media Managment Value Chain and Business Models in changing media marketing. Springer-Verlag Berlin Heidelberg, 2013. 880 p.
6. Junco R. Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance // Computers in Human Behavior. 2012. V. 28, No 1. P. 187–198.
7. Junco R. The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement Received // Magazine Computers & Education. 2012. V. 58, No 1. P. 162–171.
8. Kosinski M., Stillwell D., Graepel T. Private traits and attributes are predictable from digital records of human behavior // Magazine PNAS. 2013. V. 110, No 15. P. 5802–5805.
9. Мацута В.В., Киселев П.Б., Фещенко А.Б., Гойко В.Л., Сузанова Е.А., Степаненко А.А. Методы и инструменты выявления перспективных абитуриентов в социальных сетях // Открытое и дистанционное образование. 2017. № 4. C. 45–52.
10. Penetration of leading social networks in Russia as of 4th quarter 2017 // Statistica. URL: https://www.statista.com/statistics/284447/russia-social-network-penetration/.
11. Мотивы проявления студентами колледжей социальной активности в социальных сетях: регионального аспекта // Электронный научный архив УрФУ. URL: http://elar.urfu.ru/bitstream/ 10995/59123/1/978-5-91256-403-1_2018_053.pdf.
12. Cоциальная сеть Вконтакте. URL: https://vk.com/.
13. Политика конфиденциальности VK.com // Cоциальная сеть Вконтакте. URL: https://vk.com/privacy.
14. VK.com python API wrapper // GitHub. URL: https://github.com/ voronind/vk.
15. Kaggle. URL: https://www.kaggle.com/.
16. What are outliers in the data // Engineering statistics handbook. URL: https://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm.
17. Histograms and density plots in python // Towards data science. URL: https://towardsdatascience.com/histograms-and-density-plots-in-python-f6bda88f5ac0.
18. How to normalize and standardize your machine learning data in weka // Machine learning mastery. URL: https://machinelearningmastery.com/normalize- standardize-machine-learning-data-weka/.
19. Generalized Linear Models // Scikit-learn. URL: https://scikit-learn.org/ stable/supervised_learning.html.
20. Overfitting vs underfitting: a conceptual explanation // Towards data science. URL: https://towardsdatascience.com/overfitting-vs-underfitting-a- conceptual-explanation-d94ee20ca7f9.
21. Что такое кросс-валидация // Data Science. URL: http://datascientist. one/cross-validation/.
22. What is the difference between a parameter and a Hyperparameter? // Machine Learning Mastery. URL: https://machinelearningmastery.com/difference- between-a-parameter-and-a-hyperparameter/.
Review
For citations:
, Machine learning methods for determining the relationship between academic success and data of social network profile. Russian Digital Libraries Journal. 2019;22(2):95-118. (In Russ.) https://doi.org/10.26907/1562-5419-2019-22-2-95-118
Views:
24
Email this article 