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Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques

Yıl 2024, Cilt: 36 Sayı: 1, 301 - 310, 28.03.2024

Songül Karabatak Özal Yıldırım Murat Karabatak

https://doi.org/10.35234/fumbd.1332199

Öz

Thanks to the advancement of technology, vast amounts of data are being generated in various fields on a daily basis. The research on identifying hidden patterns and extracting useful information from big data has become increasingly important. In the field of education, the availability of large datasets has allowed for the emergence of data mining techniques as an alternative to traditional statistical methods. Unlike traditional statistical methods, data mining can uncover hidden relationships between variables, thus avoiding the loss of valuable information and enabling the utilization of essential data in education. By unlocking valuable insights and predicting important relationships, educational data mining (EDM) has the potential to enhance and improve the quality of education. This study aims to demonstrate the predictive power of EDM through a sample application and draw attention to its implications. The dataset used in this study consists of survey responses collected from university students. The variables in the dataset include academic self-efficacy, sense of community, academic achievement averages, and various demographic variables of distance education students. Descriptive modeling was employed to identify latent patterns between variables, while a predictive model was utilized to estimate variables. In order to achieve this, both association rule mining and classification algorithms were employed. The findings of this study indicate that EDM can effectively identify relationships between variables and make accurate predictions.

Anahtar Kelimeler

educational data mining academic self-efficiency community feeling academik achievement

Kaynakça

  • Haberal I. Analysis of web log using data mining algorithms. MSc thesis, Baskent University, Ankara, Turkey, 2007.
  • Chen MS, Han J, Yu, PS. Data mining: an overview from a database perspective. IEEE Trans. Knowl Data Eng 1996; 8(6): 866-883.
  • Angeli C, Howard SK, Ma J, Yang J, Kirschner PA. Data mining in educational technology classroom research: Can it make a contribution?. Comput Educ 2017; 113: 226-242.
  • Ateş Y, Karabatak M. (2017). Multiple minimum support values for quantitative association rules. Science and Engineering Journal of Firat University 2017; 29(2): 57-65
  • Peña-Ayala A. (Ed.) Educational Data Mining: Applications and Trends (Vol. 524). Switzerland: Springer International Publishing, 2014.
  • Pujari AK. Data Mining Techniques. Hyderabad: Universities Press (India) Private Limited, (2001).
  • Kaya H, Köymen K. Data mining concept and its application areas. Fırat University Journal of Middle Eastern Studies 2008; 6(2): 159-164.
  • Thuraisingham B. A primer for understanding and applying data mining. It Professional 2000; 2(1): 28-31.
  • Velickov S, Solomatine D. (2000). Predictive data mining: practical examples. In 2nd Joint Workshop on Applied AI in Civil Engineering. 2000; 1: 1-17.
  • Peng Y, Kou G, Shi Y, Chen Z. (2008). A descriptive framework for the field of data mining and knowledge discovery. International Journal of Information Technology & Decision Making 2008; 7(4): 639-682.
  • Cemaloğlu N, Duykuluoğlu A. Data Mining in Social Sciences. Ankara: PegemA, 2020.
  • Sevindik T, Kayışlı K, Ünlükahraman O. Data mining in web-based education. Turk J Comput Math Educ 2012; 3(3): 183-193.
  • Hand DJ, Mannila H, Smyth P. Principles of Data Mining. Massachusetts: MIT Press, 2001.
  • Asif R, Merceron A, Ali SA, Haider NG. Analyzing undergraduate students' performance using educational data mining. Comput Educ 2017; 113: 177-194.
  • Chalaris M, Gritzalis S, Maragoudakis M, Sgouropoulou C, Tsolakidis A. Improving quality of educational processes providing new knowledge using data mining techniques. Procedia-Social and Behavioral Sciences 2014; 147: 390-397.
  • Romero C, Ventura S, Pechenizkiy M, Baker RS. (Eds.). Handbook of Educational Data Mining. USA: CRC Press, 2010.
  • Murugananthan V, ShivaKumar BL. An adaptive educational data mining technique for mining educational data models in e-learning systems. Indian Journal of Science and Technology 2016; 9(3): 1-5.
  • Romero C, Ventura S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2010; 40(6): 601-618.
  • Berland M, Baker RS, Blikstein P. Educational data mining and learning analytics: Applications to constructionist research. Technol, Knowl Learn 2014; 19(1-2): 205-220.
  • Liu C, Zou D, Chen X, Xie H, Chan WH. A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019). Asia Pacific Education Review 2021; 1-20.
  • Hung HC, Liu IF, Liang CT, Su YS. Applying educational data mining to explore students’ learning patterns in the flipped learning approach for coding education. Symmetry 2020; 12(2): 213.
  • Baker RS, Corbett AT. Assessment of robust learning with educational data mining. Research & Practice in Assessment 2014; 9: 38-50.
  • Şengür D, Karabatak S. Data mining techniques based on students achievements analysis. Turkish Journal of Science & Technology 2018; 13(2): 53-59.
  • Göksu IB. Learners' evaluation based on data mining in a web-based learning environment. Journal of Computer and Education Research 2015; 3(5): 78-95.
  • Delavari N, Beikzadeh MR, Phon-Amnuaisuk S. Data mining application in higher learning institutions. Informatics in Education 2008; 7(1): 31-54.
  • Scheuer O, McLaren BM. Educational data mining. In Encyclopedia of the Sciences of Learning 2012; 1075-1079.
  • Bilici Z, Özdemir D. Data mining studies in education: Literature review for the years 2014-2020. Journal of Bayburt Education Faculty 2022; 17(33): 342-376.
  • Baker RS. Data mining for education. International Encyclopedia of Education 2010; 7(3): 112-118.
  • Charitopoulos A, Rangoussi M, Koulouriotis D. On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010–2018. Int J Artif Intell Educ 2020; 30(3): 371-430.
  • Mehta MH, Chauhan NC, Gokhale A. Predicting institute graduation rate with genetic algorithm assisted regression for education data mining. ICTACT Journal on Soft Computing 2021;11(2): 2266-2278.
  • Fernandes CM, Bouthillette F, Raboud JM, Bullock L, Moore CF, Christenson JM, ... Way M. Violence in the emergency department: a survey of health care workers. Cmaj 1999; 161(10): 1245-1248.
  • Rodrigues MW, Isotani S, Zarate LE. Educational Data Mining: A review of evaluation process in the e-learning. Telematics and Informatics 2018; 35(6): 1701-1717.
  • Büyüköztürk Ş, Kiliç-Çakmak E, Akgün ÖE, Karadeniz Ş, Demirel F. Scientific Research Methods. Ankara: PegemA, 2011.
  • Gliner JA, Morgan GA, Leech, NL. Research Methods in Applied Settings: An Integrated Approach to Design and Analysis. Routledge, (2016).
  • Jerusalem M, Schwarzer R. Fragebogen zur Erfassung von "Selbstwirksamkeit. Skalen zur Befindlichkeit und Persoenlichkeit In R. Schwarzer (Hrsg.). (Forschungsbericht No. 5). Berlin: Freie Universitaet, Institut fuer Psychologie,1981.
  • Yilmaz M., Gurçay D., Ekici G. Adaptatıon of the Academıc Self-Effıcacy Scale to Turkish. H. U. Journal of Education, 2007; 33(33): 253-259.
  • Rovai AP, Wighting MJ, Lucking R. The Classroom and school community inventory: Development, refinement, and validation of a self-report measure for educational research. The Internet and Higher Education 2004; 7(4): 263-280.
  • Ilgaz H., Aşkar P. The Development of a Community Feeling Scale toward Online Distance Education Environments. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 2009: 1(1): 27-34.
  • Arslan E. (2012). Data mining methods, Hyperlink: [https://emraharslanbm.wordpress.com/2012/07/17/ veri-madenciligi-yontemleri/]. Retrieved on 02 August 2021.
  • Tanyıldızı E, Karabatak M, Yıldırım G, Özpolat Z. Performance analysis of classification algorithms in wart treatment. Science and Engineering Journal of Firat University 2018; 30(2): 249-256.
  • Akar Ö, Güngör O. Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation 2013; 1(2): 139-146.
  • Alan A, Karabatak M. Evaluation of the factors affecting performance on the data set-classification relationship. Science and Engineering Journal of Firat University 2020; 32(2): 531-540
  • Uslu, M. Association rule. Hyperlink: [https://www.slideshare.net/uslumetin/ birliktelik-kurallar-kullanlarak-pazar-sepeti-analizi-market-basket-analysis-using-association-rules], 2016. Retrieved on 05 December 2021.
  • Zaiane OR. Web usage mining for a better web-based learning environment. Proceedings of the 4th IASTED International Conference on Advanced Technology for Education, 2001. https://era.library.ualberta.ca/items/0a182195-ce39-4b5d-a1c1-291ed91a0f36
  • Injadat M, Moubayed A, Nassif AB, Shami A. Systematic ensemble model selection approach for educational data mining. Knowledge-Based Syst, 2020, 105992.
  • Tekin A, Polat E. Evaluation of teacher candidates' techno-pedagogical education competencies with the rule of association. In: Nabiyev, V, Erümit AK, editors. Artificial Intelligence in Education From Theory to Practice. Ankara: PegemA, 2020.`

Covid-19 Pandemisi Sürecinde Öğrencilerin Akademik Öz Yeterlilik, Topluluk Hissi ve Akademik Başarılarının Veri Madenciliği Teknikleri ile Değerlendirilmesi

Yıl 2024, Cilt: 36 Sayı: 1, 301 - 310, 28.03.2024

Songül Karabatak Özal Yıldırım Murat Karabatak

https://doi.org/10.35234/fumbd.1332199

Öz

Günlük hayatın bir parçası haline gelen teknolojiler sayesinde hemen her alanda devasa veri yığınları oluşmaktadır. Büyük verideki gizli örüntülerin tespit edilmesi ve faydalı bilgilerin keşfedilmesine yönelik araştırmalar önem kazanmıştır. Eğitim alanında biriken veri miktarı, bu alanda geleneksel istatistiksel yöntemlere alternatif olarak veri madenciliği tekniklerinin ön plana çıkmasını sağlamıştır. Geleneksel istatistiksel yöntemlerde bazı değişkenler arasındaki gizli ilişkiler göz ardı edilebilmektedir. Bu da bazı bilgilerin kaybolmasına ya da eğitim gibi temel alanlarda gerekli verilerin kullanılamamasına neden olabiliyor. Ancak eğitimsel veri madenciliği (EVM), eğitimin kalitesini iyileştirmek ve geliştirmek için değerli verilerin kilidini açabilir ve önemli ilişkileri tahmin edebilir. Bu nedenle bu çalışma, EVM'nin tahmin gücüne dikkat çekmek için örnek bir EVM uygulaması gerçekleştirmeyi amaçlamıştır. Veri seti üniversite öğrencilerinden toplanan görüşlerden oluşmaktadır. Bu veri setinin değişkenlerini uzaktan eğitim öğrencilerinin akademik öz yeterlilikleri, topluluk hissi, akademik başarı ortalamaları ve bazı demografik değişkenler oluşturmuştur. Betimsel model, çalışmadaki değişkenler arasındaki örtük örüntüleri ortaya çıkarmış ve değişkenleri tahmin etmek için yordayıcı bir model kullanılmıştır. Bunun için birliktelik kuralı yöntemi ve sınıflandırma algoritması da kullanılmıştır. Çalışma sonunda EVM'nin değişkenler arasındaki ilişkileri etkili bir şekilde bulabildiği ve değişkenleri tahmin edebildiği sonucuna varılmıştır.

Anahtar Kelimeler

eğitsel veri madenciliği akademik öz-yeterlilik topluluk hissi akademik başarı

Kaynakça

  • Haberal I. Analysis of web log using data mining algorithms. MSc thesis, Baskent University, Ankara, Turkey, 2007.
  • Chen MS, Han J, Yu, PS. Data mining: an overview from a database perspective. IEEE Trans. Knowl Data Eng 1996; 8(6): 866-883.
  • Angeli C, Howard SK, Ma J, Yang J, Kirschner PA. Data mining in educational technology classroom research: Can it make a contribution?. Comput Educ 2017; 113: 226-242.
  • Ateş Y, Karabatak M. (2017). Multiple minimum support values for quantitative association rules. Science and Engineering Journal of Firat University 2017; 29(2): 57-65
  • Peña-Ayala A. (Ed.) Educational Data Mining: Applications and Trends (Vol. 524). Switzerland: Springer International Publishing, 2014.
  • Pujari AK. Data Mining Techniques. Hyderabad: Universities Press (India) Private Limited, (2001).
  • Kaya H, Köymen K. Data mining concept and its application areas. Fırat University Journal of Middle Eastern Studies 2008; 6(2): 159-164.
  • Thuraisingham B. A primer for understanding and applying data mining. It Professional 2000; 2(1): 28-31.
  • Velickov S, Solomatine D. (2000). Predictive data mining: practical examples. In 2nd Joint Workshop on Applied AI in Civil Engineering. 2000; 1: 1-17.
  • Peng Y, Kou G, Shi Y, Chen Z. (2008). A descriptive framework for the field of data mining and knowledge discovery. International Journal of Information Technology & Decision Making 2008; 7(4): 639-682.
  • Cemaloğlu N, Duykuluoğlu A. Data Mining in Social Sciences. Ankara: PegemA, 2020.
  • Sevindik T, Kayışlı K, Ünlükahraman O. Data mining in web-based education. Turk J Comput Math Educ 2012; 3(3): 183-193.
  • Hand DJ, Mannila H, Smyth P. Principles of Data Mining. Massachusetts: MIT Press, 2001.
  • Asif R, Merceron A, Ali SA, Haider NG. Analyzing undergraduate students' performance using educational data mining. Comput Educ 2017; 113: 177-194.
  • Chalaris M, Gritzalis S, Maragoudakis M, Sgouropoulou C, Tsolakidis A. Improving quality of educational processes providing new knowledge using data mining techniques. Procedia-Social and Behavioral Sciences 2014; 147: 390-397.
  • Romero C, Ventura S, Pechenizkiy M, Baker RS. (Eds.). Handbook of Educational Data Mining. USA: CRC Press, 2010.
  • Murugananthan V, ShivaKumar BL. An adaptive educational data mining technique for mining educational data models in e-learning systems. Indian Journal of Science and Technology 2016; 9(3): 1-5.
  • Romero C, Ventura S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2010; 40(6): 601-618.
  • Berland M, Baker RS, Blikstein P. Educational data mining and learning analytics: Applications to constructionist research. Technol, Knowl Learn 2014; 19(1-2): 205-220.
  • Liu C, Zou D, Chen X, Xie H, Chan WH. A bibliometric review on latent topics and trends of the empirical MOOC literature (2008–2019). Asia Pacific Education Review 2021; 1-20.
  • Hung HC, Liu IF, Liang CT, Su YS. Applying educational data mining to explore students’ learning patterns in the flipped learning approach for coding education. Symmetry 2020; 12(2): 213.
  • Baker RS, Corbett AT. Assessment of robust learning with educational data mining. Research & Practice in Assessment 2014; 9: 38-50.
  • Şengür D, Karabatak S. Data mining techniques based on students achievements analysis. Turkish Journal of Science & Technology 2018; 13(2): 53-59.
  • Göksu IB. Learners' evaluation based on data mining in a web-based learning environment. Journal of Computer and Education Research 2015; 3(5): 78-95.
  • Delavari N, Beikzadeh MR, Phon-Amnuaisuk S. Data mining application in higher learning institutions. Informatics in Education 2008; 7(1): 31-54.
  • Scheuer O, McLaren BM. Educational data mining. In Encyclopedia of the Sciences of Learning 2012; 1075-1079.
  • Bilici Z, Özdemir D. Data mining studies in education: Literature review for the years 2014-2020. Journal of Bayburt Education Faculty 2022; 17(33): 342-376.
  • Baker RS. Data mining for education. International Encyclopedia of Education 2010; 7(3): 112-118.
  • Charitopoulos A, Rangoussi M, Koulouriotis D. On the use of soft computing methods in educational data mining and learning analytics research: A review of years 2010–2018. Int J Artif Intell Educ 2020; 30(3): 371-430.
  • Mehta MH, Chauhan NC, Gokhale A. Predicting institute graduation rate with genetic algorithm assisted regression for education data mining. ICTACT Journal on Soft Computing 2021;11(2): 2266-2278.
  • Fernandes CM, Bouthillette F, Raboud JM, Bullock L, Moore CF, Christenson JM, ... Way M. Violence in the emergency department: a survey of health care workers. Cmaj 1999; 161(10): 1245-1248.
  • Rodrigues MW, Isotani S, Zarate LE. Educational Data Mining: A review of evaluation process in the e-learning. Telematics and Informatics 2018; 35(6): 1701-1717.
  • Büyüköztürk Ş, Kiliç-Çakmak E, Akgün ÖE, Karadeniz Ş, Demirel F. Scientific Research Methods. Ankara: PegemA, 2011.
  • Gliner JA, Morgan GA, Leech, NL. Research Methods in Applied Settings: An Integrated Approach to Design and Analysis. Routledge, (2016).
  • Jerusalem M, Schwarzer R. Fragebogen zur Erfassung von "Selbstwirksamkeit. Skalen zur Befindlichkeit und Persoenlichkeit In R. Schwarzer (Hrsg.). (Forschungsbericht No. 5). Berlin: Freie Universitaet, Institut fuer Psychologie,1981.
  • Yilmaz M., Gurçay D., Ekici G. Adaptatıon of the Academıc Self-Effıcacy Scale to Turkish. H. U. Journal of Education, 2007; 33(33): 253-259.
  • Rovai AP, Wighting MJ, Lucking R. The Classroom and school community inventory: Development, refinement, and validation of a self-report measure for educational research. The Internet and Higher Education 2004; 7(4): 263-280.
  • Ilgaz H., Aşkar P. The Development of a Community Feeling Scale toward Online Distance Education Environments. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 2009: 1(1): 27-34.
  • Arslan E. (2012). Data mining methods, Hyperlink: [https://emraharslanbm.wordpress.com/2012/07/17/ veri-madenciligi-yontemleri/]. Retrieved on 02 August 2021.
  • Tanyıldızı E, Karabatak M, Yıldırım G, Özpolat Z. Performance analysis of classification algorithms in wart treatment. Science and Engineering Journal of Firat University 2018; 30(2): 249-256.
  • Akar Ö, Güngör O. Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation 2013; 1(2): 139-146.
  • Alan A, Karabatak M. Evaluation of the factors affecting performance on the data set-classification relationship. Science and Engineering Journal of Firat University 2020; 32(2): 531-540
  • Uslu, M. Association rule. Hyperlink: [https://www.slideshare.net/uslumetin/ birliktelik-kurallar-kullanlarak-pazar-sepeti-analizi-market-basket-analysis-using-association-rules], 2016. Retrieved on 05 December 2021.
  • Zaiane OR. Web usage mining for a better web-based learning environment. Proceedings of the 4th IASTED International Conference on Advanced Technology for Education, 2001. https://era.library.ualberta.ca/items/0a182195-ce39-4b5d-a1c1-291ed91a0f36
  • Injadat M, Moubayed A, Nassif AB, Shami A. Systematic ensemble model selection approach for educational data mining. Knowledge-Based Syst, 2020, 105992.
  • Tekin A, Polat E. Evaluation of teacher candidates' techno-pedagogical education competencies with the rule of association. In: Nabiyev, V, Erümit AK, editors. Artificial Intelligence in Education From Theory to Practice. Ankara: PegemA, 2020.`
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyal Bilimlerde ve Eğitimde Bilgi İşleme
Bölüm MBD
Yazarlar

Songül Karabatak 0000-0002-1303-2429

Özal Yıldırım 0000-0001-5375-3012

Murat Karabatak 0000-0002-6719-7421

Yayımlanma Tarihi 28 Mart 2024
Gönderilme Tarihi 24 Temmuz 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 36 Sayı: 1

Kaynak Göster

APA Karabatak, S., Yıldırım, Ö., & Karabatak, M. (2024). Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 36(1), 301-310. https://doi.org/10.35234/fumbd.1332199
AMA Karabatak S, Yıldırım Ö, Karabatak M. Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. Mart 2024;36(1):301-310. doi:10.35234/fumbd.1332199
Chicago Karabatak, Songül, Özal Yıldırım, ve Murat Karabatak. “Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36, sy. 1 (Mart 2024): 301-10. https://doi.org/10.35234/fumbd.1332199.
EndNote Karabatak S, Yıldırım Ö, Karabatak M (01 Mart 2024) Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36 1 301–310.
IEEE S. Karabatak, Ö. Yıldırım, ve M. Karabatak, “Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 36, sy. 1, ss. 301–310, 2024, doi: 10.35234/fumbd.1332199.
ISNAD Karabatak, Songül vd. “Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 36/1 (Mart 2024), 301-310. https://doi.org/10.35234/fumbd.1332199.
JAMA Karabatak S, Yıldırım Ö, Karabatak M. Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2024;36:301–310.
MLA Karabatak, Songül vd. “Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, c. 36, sy. 1, 2024, ss. 301-10, doi:10.35234/fumbd.1332199.
Vancouver Karabatak S, Yıldırım Ö, Karabatak M. Evaluation of Academic Self-Efficiency, Community Feeling, and Academic Achievement of Students in the Process of the Covid-19 Pandemic by Data Mining Techniques. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2024;36(1):301-10.
 Kapak Resmi İndir