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Açık ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi

Yıl 2019, Sayı: 2, 225 - 235, 01.08.2019

Öz

Bilgi ve iletişim teknolojilerindeki gelişmeler ve yaşam boyu öğrenmeye olan taleple birlikte açık ve uzaktan öğrenmeye olan ilgi giderek artmaktadır. Açık ve uzaktan öğrenmede, öğrenenin kendi öğrenme yaşantısından sorumlu olduğu düşünülmektedir. Öğrenme yaşantısı boyunca bireyler farklı nedenlerle öğrenimlerini bırakmak, ara vermek ya da öğrenimlerini terk etmek zorunda kalmaktadırlar. Öğrenimi bırakma nedenlerinin belirlenmesinin, bu sorunların çözümü noktasında önemli veriler sağlayacağı düşünülmektedir. Türkiye’de açık ve uzaktan öğrenme sistemiyle eğitim-öğretim yapan kurumlardaki öğrenimi bırakma nedenleriyle ilgili yapılmış kapsamlı bir araştırmaya rastlanmamıştır. Bu çalışmada literatürde yer alan öğrenimi bırakma ile ilgili araştırmalar incelenmiş ve ayrılma nedenleri bu araştırmalar doğrultusunda belirlenmeye çalışılmıştır. Literatürde yer alan öğrenimi bırakma nedenleri, bu konuda geliştirilmiş kuramlar çerçevesinde ele alınmış, açık ve uzaktan öğrenmedeki öğrenimi bırakmaya neden olan faktörler incelenmiştir. Öğrenenlerin öğrenimlerini bırakma nedenlerinin belirlenerek, çözüm yollarının geliştirilmesi oldukça önemlidir. Bu çalışmayla elde edilen deneyimin açık ve uzaktan öğrenmede öğrenimi bırakma ile ilgili yapılacak çalışmalara kuramsal zemin oluşturması amaçlanmıştır. Bununla birlikte, literatürde belirtilen öğrenimi bırakma nedenlerinin çözümü noktasında öneriler yapılmıştır

Kaynakça

  • Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States. Newburyport, MA: Sloan Consortium.
  • Arthur, C. (2006). What is the 1% rule? The Guardian. Retrieved from 20/guardianweeklytechnologysection2
  • Battin-Pearson, S., Newcomb, M. D., Abbott, R. D., Hill, K. G., Catalano, R. F., & Hawkins, J. D. (2000). Predictors of early high school dropout: A test of five theories. Journal of Educational Psychology, 92(3), 568–582. doi:10.1037/0022-0663.92.3.568
  • Bean, J. P., & Eaton, S. (2000). A psychological model of college student retention. In J. M. Braxton (Ed.), Reworking the departure puzzle: New theory and research on college student retention. Nashville: University of Vanberbilt Press.
  • Beekhoven, S., & Dekkers, H. (2005). Early school leaving in the lower vocational track: triangulation of qualitative and quantitative data. Adolescence, 40(157), 197–213.
  • Belloc, F., Maruotti, A., & Petrella, L. (2010). University drop- out: An Italian experience. Higher Education, 60(2), 127–138. doi:10.1007/s10734-009-9290-1
  • Bennett, R. (2003). Determinants of undergraduate student drop out rates in a university business studies department. Journal of Further and Higher Education, 27(2), 123–141. doi:10.1080/030987703200065154
  • Bezerra, L., & Silva, M. (2017). A review of literature on the reasons that cause the high dropout rates in the MOOCS. Revista Espacios, 38(5), 11. Retrieved from http://www. revistaespacios.com/a17v38n05/a17v38n05p11.pdf adresinden erişildi.
  • Bocchi, J., Eastman, J. K., & Swift, C. O. (2004). Retaining the online learner: Profile of students in an online mba program and ımplications for teaching them. Journal of Education for Business, 79(4), 245–253. doi:10.3200/JOEB.79.4.245-253
  • Bruinsma, M. (2003). Effectiveness of higher education: Factors that determine outcomes of university education. Groningen: s.n.
  • Çakmak, Ö. (2018). Eğitimin ekonomiye etkisi ve kalkınmaya etkisi. Dicle Üniversitesi Ziya Gökalp Eğitim Fakültesi Dergisi, 11, 33–41.
  • Castles, J. (2004). Persistence and the adult learner. Active Learning in Higher Education, 5(2), 166–179. doi:10.1177/ 1469787404043813
  • Chen, R. (2008). Financial aid and student dropout in higher education: A heterogeneous research approach. In J. C. Smart (Ed.), Higher education: Handbook of theory and research, Vol. XXIII (pp. 209–239). Dordrecht: Springer.
  • Cheung, L. L. W., & Kan, A. C. N. (2002). Evaluation of factors related to student performance in a distance-learning business communication course. Journal of Education for Business, 77(5), 257–263. doi:10.1080/08832320209599674
  • Christenson, S. L., & Thurlow, M. L. (2004). School dropouts prevention considerations, ınterventions, and challenges. Current Directions in Psychological Science, 13(1), 36–39. doi:10.1111/j.0963-7214.2004.01301010.x
  • Levy, Y., & Murphy, K. E. (2002). Toward a value framework for online learning systems. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences (pp. 40–48). Big Island, Hawaii: IEEE Comput. Soc. doi:10.1109/ HICSS.2002.993854
  • Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48(2), 185–204. doi:10.1016/J.COMPEDU.2004.12.004
  • Li, H. (2002). Distance education: Pros, cons, and the future. In The annual meeting of the Western States Communication Association (p. 37). Long Beach, CA.
  • Li, W., Gao, M., Li, H., Xiong, Q., Wen, J., & Wu, Z. (2016). Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning. In Proceedings of the International Joint Conference on Neural Networks (pp. 3130–3137). IEEE. doi:10.1109/IJCNN.2016.7727598
  • Liu, S. Y., Gomez, J., & Cherng-Jyh, Y. (2009). Community college online course retention and final grade: Predictability of social presence repository citation original publication citation. Journal of Interactive Online Learning, 8(2), 165–182.
  • Lu, X., Wang, S., Huang, J., Chen, W., & Yan, Z. (2017). What decides the dropout in MOOCS? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 316–327). Springer, Cham. doi:10.1007/978-3-319-55705-2_25
  • Mau, W. C. (1995). Educational planning and academic achievement of middle school students: A racial and cultural comparison. Journal of Counseling & Development, 73(5), 518–526. doi:10.1002/j.1556-6676.1995.tb01788.x
  • McMillan, W. J. (2010). Teaching for clinical reasoning – helping students make the conceptual links. Medical Teacher, 32(10), e436–e442. doi:10.3109/01421591003695303
  • Meister, J. (2002). Pillars of e-learning success. New York: Corporate University Exchange.
  • Moore, M., & Kearsley, G. (2005). Distance education a systems view. Canada: Thomson Wadsworth.
  • Morris, L. V., Wu, S.-S., & Finnegan, C. L. (2005). Predicting retention in online general education courses. American Journal of Distance Education, 19(1), 23–36. doi:10.1207/ s15389286ajde1901_3
  • Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29–48. doi:10.1080/01587910500081269
  • Nielson, J. (2006). The 90-9-1 rule for participation inequality in social media and online communities. Nielsen Norman Group. Retrieved from https://www.nngroup.com/articles/ participation-inequality/
  • Osborn, V. (2001). Identifying at-risk students in videoconferencing and web-based distance education. American Journal of Distance Education, 15(1), 41–54. doi:10.1080/08923640109527073
  • Özer, A., Gençtanırım, D., & Ergene, T. (2011). Türk lise öğrencilerinde okul terkinin yordanması: Aracı ve etkileşim değişkenleri ile bir model testi. Eğitim ve Bilim, 36(161), 302– 317.
  • Spady, W. G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64– 85. doi:10.1007/BF02214313
  • Suh, S., Suh, J., & Houston, I. (2007). Predictors of categorical at-risk high school dropouts. Journal of Counseling & Development, 85(2), 196–203. doi:10.1002/j.1556-6678.2007.tb00463.x
  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. doi:10.3102/00346543045001089
  • Wilkowski, J., Deutsch, A., & Russell, D. M. (2014). Student skill and goal achievement in the mapping with google MOOC. In Proceedings of the first ACM conference on Learning (pp. 3–10). New York, New York, USA: ACM Press. doi:10.1145/2556325.2566240
  • Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39(4), 361–377.
  • Xing, W., & Du, D. (2018). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547–570. doi:10.1177/0735633118757015
  • Yuan, L., & Powell, S. (2013). MOOCs and disruptive innovation: Implications for higher education. eLearning Papers, 33(2), 1–8.
  • Yukselturk, E., & Inan, F. A. (2004). Factors affecting online certificate program dropouts. In J. Nall & R. Robson (Eds.), Proceedings of E-Learn 2004--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2253–2273). Association for the Advancement of Computing in Education (AACE).

Examination of Dropout Causes in Open and Distance Learning

Yıl 2019, Sayı: 2, 225 - 235, 01.08.2019

Öz

There is a growing interest in open and distance learning with the advances in information and communication technologies and demand for lifelong learning. In open and distance learning, the learner is thought to be responsible for his or her own learning life. Throughout their learning life, individuals have to drop out their education or leave their education due to different reasons. For determining the reasons about dropping out education will provide important information for the solution of these problems. There isn’t any comprehensive study about dropping out education in the open and distance education system in Turkey. In this study, the literature about dropping out education were examined and the reasons for dropping out were tried to be determined according to these researches. The reasons for dropping out in the literature were discussed within the developed theories on this subject and the factors causing drop out in open and distance learning were examined. It is very important to identify the reasons for dropping out the education and to develop solutions. The aim of this study is to provide a theoretical background to the studies to be done about dropping out in open and distance learning. In addition, suggestions have been made to solve the reasons about dropping out education are given in the literature

Kaynakça

  • Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States. Newburyport, MA: Sloan Consortium.
  • Arthur, C. (2006). What is the 1% rule? The Guardian. Retrieved from 20/guardianweeklytechnologysection2
  • Battin-Pearson, S., Newcomb, M. D., Abbott, R. D., Hill, K. G., Catalano, R. F., & Hawkins, J. D. (2000). Predictors of early high school dropout: A test of five theories. Journal of Educational Psychology, 92(3), 568–582. doi:10.1037/0022-0663.92.3.568
  • Bean, J. P., & Eaton, S. (2000). A psychological model of college student retention. In J. M. Braxton (Ed.), Reworking the departure puzzle: New theory and research on college student retention. Nashville: University of Vanberbilt Press.
  • Beekhoven, S., & Dekkers, H. (2005). Early school leaving in the lower vocational track: triangulation of qualitative and quantitative data. Adolescence, 40(157), 197–213.
  • Belloc, F., Maruotti, A., & Petrella, L. (2010). University drop- out: An Italian experience. Higher Education, 60(2), 127–138. doi:10.1007/s10734-009-9290-1
  • Bennett, R. (2003). Determinants of undergraduate student drop out rates in a university business studies department. Journal of Further and Higher Education, 27(2), 123–141. doi:10.1080/030987703200065154
  • Bezerra, L., & Silva, M. (2017). A review of literature on the reasons that cause the high dropout rates in the MOOCS. Revista Espacios, 38(5), 11. Retrieved from http://www. revistaespacios.com/a17v38n05/a17v38n05p11.pdf adresinden erişildi.
  • Bocchi, J., Eastman, J. K., & Swift, C. O. (2004). Retaining the online learner: Profile of students in an online mba program and ımplications for teaching them. Journal of Education for Business, 79(4), 245–253. doi:10.3200/JOEB.79.4.245-253
  • Bruinsma, M. (2003). Effectiveness of higher education: Factors that determine outcomes of university education. Groningen: s.n.
  • Çakmak, Ö. (2018). Eğitimin ekonomiye etkisi ve kalkınmaya etkisi. Dicle Üniversitesi Ziya Gökalp Eğitim Fakültesi Dergisi, 11, 33–41.
  • Castles, J. (2004). Persistence and the adult learner. Active Learning in Higher Education, 5(2), 166–179. doi:10.1177/ 1469787404043813
  • Chen, R. (2008). Financial aid and student dropout in higher education: A heterogeneous research approach. In J. C. Smart (Ed.), Higher education: Handbook of theory and research, Vol. XXIII (pp. 209–239). Dordrecht: Springer.
  • Cheung, L. L. W., & Kan, A. C. N. (2002). Evaluation of factors related to student performance in a distance-learning business communication course. Journal of Education for Business, 77(5), 257–263. doi:10.1080/08832320209599674
  • Christenson, S. L., & Thurlow, M. L. (2004). School dropouts prevention considerations, ınterventions, and challenges. Current Directions in Psychological Science, 13(1), 36–39. doi:10.1111/j.0963-7214.2004.01301010.x
  • Levy, Y., & Murphy, K. E. (2002). Toward a value framework for online learning systems. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences (pp. 40–48). Big Island, Hawaii: IEEE Comput. Soc. doi:10.1109/ HICSS.2002.993854
  • Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48(2), 185–204. doi:10.1016/J.COMPEDU.2004.12.004
  • Li, H. (2002). Distance education: Pros, cons, and the future. In The annual meeting of the Western States Communication Association (p. 37). Long Beach, CA.
  • Li, W., Gao, M., Li, H., Xiong, Q., Wen, J., & Wu, Z. (2016). Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning. In Proceedings of the International Joint Conference on Neural Networks (pp. 3130–3137). IEEE. doi:10.1109/IJCNN.2016.7727598
  • Liu, S. Y., Gomez, J., & Cherng-Jyh, Y. (2009). Community college online course retention and final grade: Predictability of social presence repository citation original publication citation. Journal of Interactive Online Learning, 8(2), 165–182.
  • Lu, X., Wang, S., Huang, J., Chen, W., & Yan, Z. (2017). What decides the dropout in MOOCS? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 316–327). Springer, Cham. doi:10.1007/978-3-319-55705-2_25
  • Mau, W. C. (1995). Educational planning and academic achievement of middle school students: A racial and cultural comparison. Journal of Counseling & Development, 73(5), 518–526. doi:10.1002/j.1556-6676.1995.tb01788.x
  • McMillan, W. J. (2010). Teaching for clinical reasoning – helping students make the conceptual links. Medical Teacher, 32(10), e436–e442. doi:10.3109/01421591003695303
  • Meister, J. (2002). Pillars of e-learning success. New York: Corporate University Exchange.
  • Moore, M., & Kearsley, G. (2005). Distance education a systems view. Canada: Thomson Wadsworth.
  • Morris, L. V., Wu, S.-S., & Finnegan, C. L. (2005). Predicting retention in online general education courses. American Journal of Distance Education, 19(1), 23–36. doi:10.1207/ s15389286ajde1901_3
  • Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29–48. doi:10.1080/01587910500081269
  • Nielson, J. (2006). The 90-9-1 rule for participation inequality in social media and online communities. Nielsen Norman Group. Retrieved from https://www.nngroup.com/articles/ participation-inequality/
  • Osborn, V. (2001). Identifying at-risk students in videoconferencing and web-based distance education. American Journal of Distance Education, 15(1), 41–54. doi:10.1080/08923640109527073
  • Özer, A., Gençtanırım, D., & Ergene, T. (2011). Türk lise öğrencilerinde okul terkinin yordanması: Aracı ve etkileşim değişkenleri ile bir model testi. Eğitim ve Bilim, 36(161), 302– 317.
  • Spady, W. G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64– 85. doi:10.1007/BF02214313
  • Suh, S., Suh, J., & Houston, I. (2007). Predictors of categorical at-risk high school dropouts. Journal of Counseling & Development, 85(2), 196–203. doi:10.1002/j.1556-6678.2007.tb00463.x
  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. doi:10.3102/00346543045001089
  • Wilkowski, J., Deutsch, A., & Russell, D. M. (2014). Student skill and goal achievement in the mapping with google MOOC. In Proceedings of the first ACM conference on Learning (pp. 3–10). New York, New York, USA: ACM Press. doi:10.1145/2556325.2566240
  • Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39(4), 361–377.
  • Xing, W., & Du, D. (2018). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547–570. doi:10.1177/0735633118757015
  • Yuan, L., & Powell, S. (2013). MOOCs and disruptive innovation: Implications for higher education. eLearning Papers, 33(2), 1–8.
  • Yukselturk, E., & Inan, F. A. (2004). Factors affecting online certificate program dropouts. In J. Nall & R. Robson (Eds.), Proceedings of E-Learn 2004--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2253–2273). Association for the Advancement of Computing in Education (AACE).
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

Muhammet Recep Okur

Didem Paşaoğlu Baş Bu kişi benim

Esra Pınar Uça Güneş Bu kişi benim

Yayımlanma Tarihi 1 Ağustos 2019
Yayımlandığı Sayı Yıl 2019 Sayı: 2

Kaynak Göster

APA Okur, M. R., Paşaoğlu Baş, D., & Uça Güneş, E. P. (2019). Açık ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi. Yükseköğretim Ve Bilim Dergisi(2), 225-235.
AMA Okur MR, Paşaoğlu Baş D, Uça Güneş EP. Açık ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi. J Higher Edu Sci. Ağustos 2019;(2):225-235.
Chicago Okur, Muhammet Recep, Didem Paşaoğlu Baş, ve Esra Pınar Uça Güneş. “Açık Ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi”. Yükseköğretim Ve Bilim Dergisi, sy. 2 (Ağustos 2019): 225-35.
EndNote Okur MR, Paşaoğlu Baş D, Uça Güneş EP (01 Ağustos 2019) Açık ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi. Yükseköğretim ve Bilim Dergisi 2 225–235.
IEEE M. R. Okur, D. Paşaoğlu Baş, ve E. P. Uça Güneş, “Açık ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi”, J Higher Edu Sci, sy. 2, ss. 225–235, Ağustos 2019.
ISNAD Okur, Muhammet Recep vd. “Açık Ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi”. Yükseköğretim ve Bilim Dergisi 2 (Ağustos 2019), 225-235.
JAMA Okur MR, Paşaoğlu Baş D, Uça Güneş EP. Açık ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi. J Higher Edu Sci. 2019;:225–235.
MLA Okur, Muhammet Recep vd. “Açık Ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi”. Yükseköğretim Ve Bilim Dergisi, sy. 2, 2019, ss. 225-3.
Vancouver Okur MR, Paşaoğlu Baş D, Uça Güneş EP. Açık ve Uzaktan Öğrenmede Öğrenimi Bırakma Sebeplerinin İncelenmesi. J Higher Edu Sci. 2019(2):225-3.