USE OF MACHINE LEARNING TECHNIQUES FOR THE FORECAST OF STUDENT ACHIEVEMENT IN HIGHER EDUCATION
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Keywords

machine learning in education
adult education
educational data mining

How to Cite

[1]
Ömer F. Akmeşe, H. Kör, and H. Erbay, “USE OF MACHINE LEARNING TECHNIQUES FOR THE FORECAST OF STUDENT ACHIEVEMENT IN HIGHER EDUCATION”, ITLT, vol. 82, no. 2, pp. 297–311, Apr. 2021, doi: 10.33407/itlt.v82i2.4178.

Abstract

The machine learning method, which is a sub-branch of artificial intelligence and which makes predictions with mathematical and statistical operations, is used frequently in education as in every field of life. Nowadays, it is seen that millions of data are recorded continuously, and a large amount of data accumulation has occurred. Although data accumulation increases exponentially, the number of analysts and their capabilities to process these data are insufficient. Although we live in the information age, it is more accurate to say that we live in the data age. By using stored and accumulated data, it is becoming increasingly essential to reveal meaningful relationships and trends and to make predictions for the future. It is important to analyze the data obtained from the education process and to evaluate the success of the students and the factors affecting success. These analyses may also contribute to future training activities. In this study, a data set, including socio-demographic variables of students enrolled in distance education at Hitit University, was used. The authors estimated the success of the students with demographic and social variables such as age, gender, city, family income, family education level. The primary purpose is to provide students with information about their estimated academic achievement at the beginning of the process. Thus, at the beginning of the education process, students' success can be increased by informing the students who are predicted to be unsuccessful. Diversification and enhancement of this data may also support other decision-making mechanisms in the training process. Additionally, the factors affecting students’ academic success were researched, and the students' educational outcomes were evaluated. Prediction success was compared using various machine learning algorithms. As a result of the analysis, it was determined that the Random Forest algorithm was more predictive of student achievement than others.

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References

C. Dede, “Rethinking how students learn,” in Comparing frameworks for 21st century skills., J. A. Bellanca and R. S. Brandt, Eds. 2010, pp. 51–76.

E. H. Toytok and S. Gürel, “Does Project Children’s University increase academic self-efficacy in 6th graders? A weak experimental design,” Sustain., vol. 11, no. 3, Feb. 2019, doi: 10.3390/su11030778.

E. P. Frank and N. Pharo, “Academic Librarians in Data Information Literacy Instruction: A Case Study in Meteorology.”

M. Palmer, “ANA Marketing Maestros: Data is the New Oil.” [Online]. Available:https://ana.blogs.com/maestros/2006/11/data_is_the_new.html .Accessed on: Sep. 18, 2020.

P. Rotella, “Is Data The New Oil?” [Online]. Available:https://www.forbes.com/sites/perryrotella/2012/04/02/is-data-the-new-oil/#3919a64b7db3. Accessed on: Sep. 18, 2020.

R. Schapire, “COS 511, Spring 2014: Home.” [Online]. Available:https://www.cs.princeton.edu/courses/archive/spring14/cos511/ Accessed on: Sep. 18, 2020.

P. Golding and O. Donaldson, “Predicting academic performance,” in Proceedings - Frontiers in Education Conference, FIE, 2006, pp. 21–26, doi: 10.1109/FIE.2006.322661.

D. Kabakchieva, “Predicting Student Performance by Using Data Mining Methods for Classification,” Bulg. Acad. Sci. Cybern. Inf. Technol. vol. 13, no. 1, 2013, doi: 10.2478/cait-2013-0006.

A. Hargreaves, Teaching in the knowledge society: Education in the age of insecurity. Teachers College Press, 2003.

C. Petit, R. Bezemer, and L. Atallah, “A review of recent advances in data analytics for post-operative patient deterioration detection,” Journal of Clinical Monitoring and Computing, vol. 32, no. 3. Springer Netherlands, pp. 391–402, Jun. 01, 2018, doi: 10.1007/s10877-017-0054-7.

O. F. Akmese, G. Dogan, H. Kor, H. Erbay, and E. Demir, “The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis,” Emerg. Med. Int., vol. 2020, pp. 1–8, 2020, doi: 10.1155/2020/7306435.

P. Sinha and P. Sinha, “Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM,” Int. J. Eng. Res. Technol., vol. 4, no. 12, pp. 608–12, 2015.

E. Zahn, “Informationstechnologie und Informationsmanagement,” Allg. Betriebswirtschaftslehre, vol. 2, pp. 376–428, 2001.

E. Uzun, “İnternet tabanlı bilgi erişimi destekli bir otomatik öğrenme sistemi,” 2007.

A. F. KOCAMAZ, “Makine Öğrenmesi Tabanlı Bir Uzman Sistem Tasarımı,” 2012.

K. M. Orabi, Y. M. Kamal, and T. M. Rabah, “Early predictive system for diabetes mellitus disease,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9728, pp. 420–427, doi: 10.1007/978-3-319-41561-1_31.

L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.

J. Han, M. Kamber, and J. Pei, “Introduction,” in Data Mining, 2012, pp. 1–38.

A. Van Barneveld, K. E. Arnold, and J. P. Campbell, “Analytics in Higher Education : Establishing a Common Language,” researchgate.net, no. January, pp. 1–11, 2012.

J. T. Avella, M. Kebritchi, S. G. Nunn, and T. Kanai, “Learning analytics methods, benefits, and challenges in higher education: A systematic literature review,” J. Asynchronous Learn. Netw., vol. 20, no. 2, 2016, doi: 10.24059/olj.v20i2.790.

C. Romero, S. Ventura, M. Pechenizkiy, and R. S. J. D. Baker, Handbook of educational data mining. 2010.

M. M. A. Tair, “Mining Educational Data to Improve Students ’ Performance : A Case Study Mining Educational Data t o Improve Students ’ Performance : A Case Study,” iugspace.iugaza.edu.ps, vol. 2, no. October, 2015.

K. Lee, “Large-Scale and Interpretable Collaborative Filtering for Educational Data,” ML4ED KDD Work., pp. 1–7, 2017.

L. Ali, M. Asadi, D. Gašević, J. Jovanović, and M. Hatala, “Factors influencing beliefs for adoption of a learning analytics tool: An empirical study,” Comput. Educ., vol. 62, pp. 130–148, 2013, doi: 10.1016/j.compedu.2012.10.023.

W. Greller and H. Drachsler, “Translating learning into numbers: A generic framework for learning analytics,” Educ. Technol. Soc., vol. 15, no. 3, pp. 42–57, 2012.

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