Research Article
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The Effects of Adaptive Educational Web Environment on Students’ Academic Achievement and Motivation

Year 2019, Volume: 27 Issue: 3, 1311 - 1326, 15.05.2019
https://doi.org/10.24106/kefdergi.3079

Abstract

The aim of this research
is to determine whether the effects of adaptive web-based learning(WBL)
environment, non-adaptive WBL environment and adaptive WBL environment
supported by face-to-face learning activities on the students’ achievement and
motivation are different. A 3x2 factorial design was used in this study. The
first factor of the research design is learning environment including
experimental procedures (adaptive WBL environment, non-adaptive WBL environment
and adaptive WBL environment supported by face-to-face learning activities) The
second factor is repeated measures, which revealed the change of achievement
including pre and post measurements. The dependent variables of the study are
academic achievement and motivation. The research was conducted in 2013-2014
spring semester with 72 second-year students, who took the course of Basic
Information Technology at Sakarya University, Education Faculty, Department of
Primary Education, Primary Math Education and Science Education. In such a way
that each group of 24 students, learning environments were formed as peer
groups based on pretest. According to the findings, academic achievement in the
adaptive WBL environment supplemented with face-to-face learning was significantly
determined to be higher. As a result of the examination of the students’
products in different learning environments, it was shown that environment type
did not influence students’ rubrics grade points. Moreover, there was no
significant difference among students’ motivation according to their learning
environment used.

References

  • Allen, I. E. & Seaman, J. (2006). Making the Grade. Online Education in the United States. Newburyport: Sloan Con-sortium. Retrieved on 11/08/2010 from https://docs.google.com/viewer?url=http%3A%2F%2Ffiles.eric.ed.gov%2Ffulltext%2FED530101.pdf
  • Allen, I. E. & Seaman, J. (2010). Learning on Demand Online Education in the United States, 2009. Newburyport: Sloan Consortium (SLOAN-C). Retrieved on 11/08/2010 from http://sloanconsortium.org/publications/survey/pdf/learningondemand.pdf
  • Allen, I. E. & Seaman, J. (2011). Going the Distance: Online Education in the United States. Newburyport: Sloan Con-sortium. Retrieved on 04/03/2012 from http://sloanconsortium.org/publications/survey/pdf/learningondemand.pdf .
  • Allen, I. E., Seaman, J., Poulin, R., & Straut, T. T. (2016). Online report card: Tracking online education in the United States (Rep.). Babson Survey Research Group. Retrieved on 13/11/2016 from http://onlinelearningsurvey.com/reports/onlinereportcard.pdf.
  • Andrade, H. G. (2001). The Effects of İnstructional Rubrics on Learning to Write. Current Issues in Education, 4(4), 1-28. Retrieved on 12/07/2014 from http://cie.asu.edu/volume4/number4/
  • Brusilovsky, P. (1998). Methods and Techniques of Adaptive Hypermedia. In P. Brusilovsky, A. Kobsa and J. Vassile-va (Eds.), Adaptive Hypertext and Hypermedia. Boston: Kluwer Academic Publishers.
  • Brusilovsky, P., Eklund, J. & Schwarz, E. (1998). Web-based Education for All: A Tool for Development Adaptive Co-urseware. Computer Networks and ISDN Systems (Proceedings of Seventh International World Wide Web Conference, 14-18 April 1998), 30 (1-7), 291-300.
  • Bruskilovsky, P. (2001). Adaptive Hypermedia. User Modelling and User Adapted Insruction, 11(1-2), 87-110
  • Burgos, D., Tattersall, C., & Koper, R. (2007). How to Represent Adaptation in E-learning with IMS Learning De-sign. Interactive Learning Environments, 15(2), 161-170.
  • Cesur, E. G. (2013). Investigate the effects of adaptive learning on disorientation and cognitive load of students in terms of their cognitive styles (Unpublished master dissertation). Ankara University, Institute of Education Sciences, Computer and Instructional Technology Department, Ankara.
  • Çelebi, F. (2014). Effect of navigation strategies on navigation time, navigation path and percieved disorientation in adaptive learning environments (Unpublished master dissertation). Ankara University, Institute of Educa-tion Sciences, Computer and Instructional Technology Department, Ankara.
  • De Bra, P., Houben, G. J. & Wu, H. (1999, February). AHAM: a Dexter-Based Reference Model for Adaptive Hyper-media. Proceedings of the tenth ACM Conference on Hypertext and hypermedia: Returning to Our Diverse Roots. Darmstadt, 147-156. ACM, Germany.
  • De Bra, P., Smits, D., Van Der Sluijs, K., Cristea, A. I., Foss, J., Glahn, C., & Steiner, C. M. (2013). GRAPPLE: Learning Management Systems Meet Adaptive Learning Environments. Intelligent and Adaptive Educational-Learning Systems, 133-160. Berlin Heidelberg: Springer.
  • Despotović-Zrakić, M., Marković, A., Bogdanović, Z., Barać, D., & Krčo, S. (2012). Providing Adaptivity in Moodle LMS Courses. Educational Technology & Society, 15 (1), 326–338.
  • Erdoğan, B. (2013). The Effect Of Adaptive Learning Management System On Student’s Satisfaction, Motivation And Achievement In Online Learning (Unpublished doctoral dissertation). Ankara University, Institute of Educa-tion Sciences, Computer and Instructional Technology Department, Educational Technology Department, Ankara.
  • Eryılmaz, M. (2012). The effect of hyper media on academic achievement satisfaction and cognitive load of students by using adaptive presentation and adaptive navigation (Unpublished doctoral dissertation). Ankara Uni-versity, Institute of Education Sciences, Computer and Instructional Technology Department, Educational Technology Department, Ankara.
  • Gao, T. & Lewandowski, J. (2002). Motivating Students with Interactive Web-based Learning. Society for Information Technology & Teacher Education International Conference, 2002 (1), 166-172. Retrieved on 27/09/2012 from http://editlib.org/d/6710
  • Graf, S. (2007). Adaptivity in Learning Management Systems Focussing on Learning Styles (Unpublished doctoral dissertation). Vienna University of Technology, Austria.
  • Green, S. & Salkind, N. (2005). Using SPSS for Windows and Macintosh: Understanding and Analysing Data. New Jersey: Pearson Prentice Hall.
  • Güler, N. (2012). Eğitimde Ölçme ve Değerlendirme (3th edition). Ankara: Pegem Akademi Yayıncılık.
  • Hopcan, S. (2013). Development, implementation and evaluation of adaptive web-assisted learning system for 1.-3. Grade students with specific learning disabilities (Unpublished master dissertation). Sakarya University, Institute of Education Sciences, Computer and Instructional Technology Department, Sakarya.
  • Horzum, M.B. (2012). The Effect of Web Based Instruction on Students’ Web Pedagogical Content Knowledge, Cour-se Achievement And General Course Satisfaction. Çukurova University Faculty of Education Journal, 41(1), 36-51.
  • Kehoe, J. (1995). Basic Item Analysis for Multiple-Choice Tests. Practical Assessment, Research & Evaluation, 4 (10). Retrieved on 27/07/2014 from http://PAREonline.net/getvn.asp?v=4&n=10
  • Kelly, D. (2005). On the Dynamic Multiple Intelligence Informed Personalization of the Learning Environment (Un-published doctoral dissertation). University of Dublin.
  • Khan, B. H. (1997). Web-Based Instruction. Englewood Cliffs, NJ: Educational Technology Publications.
  • Kim, J., Lee, A. & Ryu, H. (2013). Personality and Its Effects on Learning Performance: Design Guidelines for an Adap-tive E-Learning System Based on A User Model. International Journal of Industrial Ergonomics, 43(5), 450-461.
  • Limongelli, C., Sciarrone, F. & Vaste, G. (2011), Personalized E-Learning in Moodle: the Moodle_LS System, Journal of E-Learning and Knowledge Society, 7(1), English Edition, 49-58. ISSN: 1826-6223, e-ISSN:1971-8829
  • Lo, J. J., Chan, Y. C. & Yeh, S. W. (2012). Designing an Adaptive Web-Based Learning System Based on Students’ Cognitive Styles İdentified Online. Computers & Education, 58(1), 209-222.
  • Lynch, M. M. (2002). The Online Educator: A Guide to Creating the Virtual Classroom. London: Routledge Falmer Taylor & Francis Group.
  • Magoulas, G. D., Papanikolaou, Y. & Grigoriadou, M. (2003). Adaptive Web‐Based Learning: Accommodating Indivi-dual Differences Through System's Adaptation. British Journal of Educational Technology, 34(4), 511-527.
  • Matthews, G., Zeidner, M. & Roberts, R.D. (2004). Emotional Intelligence: Science and Myth. MIT Press.
  • Meccawy, M., Blanchfield, P., Ashman, H., Brailsford, T. & Moore, A. (2008). Whurle 2.0: Adaptive Learning Meets Web 2.0. In P. Dillenbourg and M. Specht (Eds.), EC-TEL 2008 (pp. 274-279).Berlin Heidelberg: Springer.
  • New York Times. (4 November 2012). The Year of the MOOC.
  • Önder, İ. & Beşoluk, Ş. (2010). Adaptation of Revised Two Factor Study Process Questionnaire (R-SPQ-2F) to Turkish. Education and Science, 35(157), 55-67.
  • Özçelik, D.A. (2010). Test Hazırlama Klavuzu (4th edition). Ankara: Pegem Akademi Yayıncılık.
  • Özyurt, Ö. (2013). The development and evaluation of a web based adaptive testing system: the case of probability unit (Unpublished doctoral dissertation). Karadeniz Technical University, Institute of Education Sciences, Department of Secondary Science and Mathematics Education, Mathematics Education Department, Trab-zon.
  • Park, O. & Lee, J. (2004). Adaptive Instructional Systems. In D.H. Jonnasen (Ed.), Handbook Of Research On Educati-onal Communications and Technology. Lawrence Erlbaum Associates.
  • Reigeluth, C. M. (1996). A new paradigm of ISD? Educational Technology and Society, 36(3), 13-20.
  • Riding, R. & Rayner, S. (1998). Cognitive Styles and Learning Strategies. London: David Fulton Publishers.
  • Rosenthal, R., Hall, J. A., DiMatteo, M. R., Rogers, P. L. & Archer, D. (1979). Sensitivity to Nonverbal Communicati-ons: The PONS Test. Baltimore, MD: The Johns Hopkins University Press.
  • Sang, S. & Keller, J. M. (2001). Effectiveness of Motivationally Adaptive Computer-Assisted Instruction on the Dyna-mic Aspects of Motivation. ETR&D, 49 (2), 5–22.
  • Šimko, M., Barla, M. & Bieliková, M. (2010). ALEF: A Framework for Adaptive Web-Based Learning 2.0. Key Compe-tencies in the Knowledge Society, 367-378. Springer Berlin Heidelberg.
  • Somyürek, S. (2008). The effects of adaptive educational web environments to learners academic achievement and navigation (Unpublished doctoral dissertation). Gazi University, Ankara.
  • Stash, N., Cristea, A. & De Bra, P. (2006, January). Learning Styles Adaptation Language for Adaptive Hypermedia. Adaptive Hypermedia and Adaptive Web-Based Systems. s. 323-327. Berlin Heidelberg: Springer.
  • Şimşek, N. (2002). BIG16 Learning Modality Inventory. Educational Sciences and Practice, 1(1). Triantafillou, E., Pomportsis, A. & Georgiadou, E. (2002). AES-CS: Adaptive Educational System Based on Cognitive Styles. Adaptive Hypermedia 2002 Workshop on Adaptive Systems for Web-based Education. Universidad de Malaga, Malaga, Spain.
  • Tseng, J., Chu, H., Hwang, G. & Tsai, C. (2008). Development of an Adaptive Learning System with Two Sources of Personalization İnformation. Computers & Education, 51(2), 776–786.
  • Uysal, M.P. (2008). The effects of instructional software designed in accordance with instructional transaction theory and the adaptive drill software on achievements of students (Unpublished doctoral dissertation). Gazi Uni-versity, Institute of Education Sciences, Department of Education Sciences, Ankara.
  • Weber, G. & Brusilovsky, P. (2001). ELM-ART: An Adaptive Versatile System for Web-based Instruction. Internatio-nal Journal of Artificial Intelligence in Education (2001), 12, 351-384.
  • Weber, G. (1999). Adaptive Learning Systems in the World Wide Web (pp. 371-377). Vienna: Springer.
  • Weibelzahl, S. (2005). Problems and Pitfalls in the Evaluation of Adaptive Systems. In S. Chen and G. Magoulas (Eds.), Adaptable and Adaptive Hypermedia Systems (pp. 285-299). Hershey, PA: IRM Press.
  • Yang, T. C., Hwang, G. J. & Yang, S. J. H. (2013). Development of an Adaptive Learning System with Multiple Perspec-tives based on Students? Learning Styles and Cognitive Styles. Educational Technology & Society, 16(4), 185-200.

Uyarlanabilir Eğitsel Web Ortamlarının Öğrencilerin Akademik Başarılarına ve Motivasyonlarına Etkisi

Year 2019, Volume: 27 Issue: 3, 1311 - 1326, 15.05.2019
https://doi.org/10.24106/kefdergi.3079

Abstract

Bu çalışmanın amacı uyarlanabilir olan, uyarlanabilir
olmayan ve yüz yüze öğrenme etkinlikleri ile desteklenmiş uyarlanabilir web
temelli öğrenme ortamlarının, öğrencilerin başarıları ve motivasyonları
üzerindeki etkilerinin farklı olup olmadığını belirlemektir. Araştırmada iki
faktörlü 3x2’lik faktöriyel desen kullanılmıştır. Araştırma deseninin birinci
faktörü deneysel işlemleri içeren öğrenme ortamı (uyarlamaların bulunduğu web
temelli öğrenme ortamı, uyarlamaların bulunmadığı web temelli öğrenme ortamı ve
yüzyüze öğrenme etkinlikleri ile desteklenmiş uyarlamaların olduğu web temelli
ortamı), ikinci faktörü ise öntest ve sontest ölçümlerini içeren ve başarının
değişimini ortaya koyan tekrarlı ölçümlerdir. Araştırmanın bağımlı değişkenleri
başarı ve motivasyondur. Araştırma 2013-2014 öğretim yılı bahar döneminde
Sakarya Üniversitesi Eğitim Fakültesi Sınıf Öğretmenliği, İlköğretim Matematik
Öğretmenliği ve Fen Bilgisi Öğretmenliği bölümlerinin 2. sınıfında öğrenim
görmekte olan ve Temel Bilgi Teknolojisi Kullanımı dersini alan 72 öğrenci ile
yürütülmüştür. Elde edilen bulgulara göre yüzyüze öğrenme etkinlikleri ile desteklenmiş
uyarlamaların olduğu web temelli ortamdaki başarı anlamlı olarak daha
yüksektir.
Farklı öğrenme ortamlarındaki öğrencilerin ürünlerinin incelenmesi
sonucunda kullanılan ortam türlerinin öğrencilerin rubrik başarı puanlarını
etkilemediği ortaya konmuştur. Ayrıca kullanılan öğrenme ortamlarına göre
öğrencilerin motivasyonları arasında farklılık olmadığı tespit edilmiştir.

References

  • Allen, I. E. & Seaman, J. (2006). Making the Grade. Online Education in the United States. Newburyport: Sloan Con-sortium. Retrieved on 11/08/2010 from https://docs.google.com/viewer?url=http%3A%2F%2Ffiles.eric.ed.gov%2Ffulltext%2FED530101.pdf
  • Allen, I. E. & Seaman, J. (2010). Learning on Demand Online Education in the United States, 2009. Newburyport: Sloan Consortium (SLOAN-C). Retrieved on 11/08/2010 from http://sloanconsortium.org/publications/survey/pdf/learningondemand.pdf
  • Allen, I. E. & Seaman, J. (2011). Going the Distance: Online Education in the United States. Newburyport: Sloan Con-sortium. Retrieved on 04/03/2012 from http://sloanconsortium.org/publications/survey/pdf/learningondemand.pdf .
  • Allen, I. E., Seaman, J., Poulin, R., & Straut, T. T. (2016). Online report card: Tracking online education in the United States (Rep.). Babson Survey Research Group. Retrieved on 13/11/2016 from http://onlinelearningsurvey.com/reports/onlinereportcard.pdf.
  • Andrade, H. G. (2001). The Effects of İnstructional Rubrics on Learning to Write. Current Issues in Education, 4(4), 1-28. Retrieved on 12/07/2014 from http://cie.asu.edu/volume4/number4/
  • Brusilovsky, P. (1998). Methods and Techniques of Adaptive Hypermedia. In P. Brusilovsky, A. Kobsa and J. Vassile-va (Eds.), Adaptive Hypertext and Hypermedia. Boston: Kluwer Academic Publishers.
  • Brusilovsky, P., Eklund, J. & Schwarz, E. (1998). Web-based Education for All: A Tool for Development Adaptive Co-urseware. Computer Networks and ISDN Systems (Proceedings of Seventh International World Wide Web Conference, 14-18 April 1998), 30 (1-7), 291-300.
  • Bruskilovsky, P. (2001). Adaptive Hypermedia. User Modelling and User Adapted Insruction, 11(1-2), 87-110
  • Burgos, D., Tattersall, C., & Koper, R. (2007). How to Represent Adaptation in E-learning with IMS Learning De-sign. Interactive Learning Environments, 15(2), 161-170.
  • Cesur, E. G. (2013). Investigate the effects of adaptive learning on disorientation and cognitive load of students in terms of their cognitive styles (Unpublished master dissertation). Ankara University, Institute of Education Sciences, Computer and Instructional Technology Department, Ankara.
  • Çelebi, F. (2014). Effect of navigation strategies on navigation time, navigation path and percieved disorientation in adaptive learning environments (Unpublished master dissertation). Ankara University, Institute of Educa-tion Sciences, Computer and Instructional Technology Department, Ankara.
  • De Bra, P., Houben, G. J. & Wu, H. (1999, February). AHAM: a Dexter-Based Reference Model for Adaptive Hyper-media. Proceedings of the tenth ACM Conference on Hypertext and hypermedia: Returning to Our Diverse Roots. Darmstadt, 147-156. ACM, Germany.
  • De Bra, P., Smits, D., Van Der Sluijs, K., Cristea, A. I., Foss, J., Glahn, C., & Steiner, C. M. (2013). GRAPPLE: Learning Management Systems Meet Adaptive Learning Environments. Intelligent and Adaptive Educational-Learning Systems, 133-160. Berlin Heidelberg: Springer.
  • Despotović-Zrakić, M., Marković, A., Bogdanović, Z., Barać, D., & Krčo, S. (2012). Providing Adaptivity in Moodle LMS Courses. Educational Technology & Society, 15 (1), 326–338.
  • Erdoğan, B. (2013). The Effect Of Adaptive Learning Management System On Student’s Satisfaction, Motivation And Achievement In Online Learning (Unpublished doctoral dissertation). Ankara University, Institute of Educa-tion Sciences, Computer and Instructional Technology Department, Educational Technology Department, Ankara.
  • Eryılmaz, M. (2012). The effect of hyper media on academic achievement satisfaction and cognitive load of students by using adaptive presentation and adaptive navigation (Unpublished doctoral dissertation). Ankara Uni-versity, Institute of Education Sciences, Computer and Instructional Technology Department, Educational Technology Department, Ankara.
  • Gao, T. & Lewandowski, J. (2002). Motivating Students with Interactive Web-based Learning. Society for Information Technology & Teacher Education International Conference, 2002 (1), 166-172. Retrieved on 27/09/2012 from http://editlib.org/d/6710
  • Graf, S. (2007). Adaptivity in Learning Management Systems Focussing on Learning Styles (Unpublished doctoral dissertation). Vienna University of Technology, Austria.
  • Green, S. & Salkind, N. (2005). Using SPSS for Windows and Macintosh: Understanding and Analysing Data. New Jersey: Pearson Prentice Hall.
  • Güler, N. (2012). Eğitimde Ölçme ve Değerlendirme (3th edition). Ankara: Pegem Akademi Yayıncılık.
  • Hopcan, S. (2013). Development, implementation and evaluation of adaptive web-assisted learning system for 1.-3. Grade students with specific learning disabilities (Unpublished master dissertation). Sakarya University, Institute of Education Sciences, Computer and Instructional Technology Department, Sakarya.
  • Horzum, M.B. (2012). The Effect of Web Based Instruction on Students’ Web Pedagogical Content Knowledge, Cour-se Achievement And General Course Satisfaction. Çukurova University Faculty of Education Journal, 41(1), 36-51.
  • Kehoe, J. (1995). Basic Item Analysis for Multiple-Choice Tests. Practical Assessment, Research & Evaluation, 4 (10). Retrieved on 27/07/2014 from http://PAREonline.net/getvn.asp?v=4&n=10
  • Kelly, D. (2005). On the Dynamic Multiple Intelligence Informed Personalization of the Learning Environment (Un-published doctoral dissertation). University of Dublin.
  • Khan, B. H. (1997). Web-Based Instruction. Englewood Cliffs, NJ: Educational Technology Publications.
  • Kim, J., Lee, A. & Ryu, H. (2013). Personality and Its Effects on Learning Performance: Design Guidelines for an Adap-tive E-Learning System Based on A User Model. International Journal of Industrial Ergonomics, 43(5), 450-461.
  • Limongelli, C., Sciarrone, F. & Vaste, G. (2011), Personalized E-Learning in Moodle: the Moodle_LS System, Journal of E-Learning and Knowledge Society, 7(1), English Edition, 49-58. ISSN: 1826-6223, e-ISSN:1971-8829
  • Lo, J. J., Chan, Y. C. & Yeh, S. W. (2012). Designing an Adaptive Web-Based Learning System Based on Students’ Cognitive Styles İdentified Online. Computers & Education, 58(1), 209-222.
  • Lynch, M. M. (2002). The Online Educator: A Guide to Creating the Virtual Classroom. London: Routledge Falmer Taylor & Francis Group.
  • Magoulas, G. D., Papanikolaou, Y. & Grigoriadou, M. (2003). Adaptive Web‐Based Learning: Accommodating Indivi-dual Differences Through System's Adaptation. British Journal of Educational Technology, 34(4), 511-527.
  • Matthews, G., Zeidner, M. & Roberts, R.D. (2004). Emotional Intelligence: Science and Myth. MIT Press.
  • Meccawy, M., Blanchfield, P., Ashman, H., Brailsford, T. & Moore, A. (2008). Whurle 2.0: Adaptive Learning Meets Web 2.0. In P. Dillenbourg and M. Specht (Eds.), EC-TEL 2008 (pp. 274-279).Berlin Heidelberg: Springer.
  • New York Times. (4 November 2012). The Year of the MOOC.
  • Önder, İ. & Beşoluk, Ş. (2010). Adaptation of Revised Two Factor Study Process Questionnaire (R-SPQ-2F) to Turkish. Education and Science, 35(157), 55-67.
  • Özçelik, D.A. (2010). Test Hazırlama Klavuzu (4th edition). Ankara: Pegem Akademi Yayıncılık.
  • Özyurt, Ö. (2013). The development and evaluation of a web based adaptive testing system: the case of probability unit (Unpublished doctoral dissertation). Karadeniz Technical University, Institute of Education Sciences, Department of Secondary Science and Mathematics Education, Mathematics Education Department, Trab-zon.
  • Park, O. & Lee, J. (2004). Adaptive Instructional Systems. In D.H. Jonnasen (Ed.), Handbook Of Research On Educati-onal Communications and Technology. Lawrence Erlbaum Associates.
  • Reigeluth, C. M. (1996). A new paradigm of ISD? Educational Technology and Society, 36(3), 13-20.
  • Riding, R. & Rayner, S. (1998). Cognitive Styles and Learning Strategies. London: David Fulton Publishers.
  • Rosenthal, R., Hall, J. A., DiMatteo, M. R., Rogers, P. L. & Archer, D. (1979). Sensitivity to Nonverbal Communicati-ons: The PONS Test. Baltimore, MD: The Johns Hopkins University Press.
  • Sang, S. & Keller, J. M. (2001). Effectiveness of Motivationally Adaptive Computer-Assisted Instruction on the Dyna-mic Aspects of Motivation. ETR&D, 49 (2), 5–22.
  • Šimko, M., Barla, M. & Bieliková, M. (2010). ALEF: A Framework for Adaptive Web-Based Learning 2.0. Key Compe-tencies in the Knowledge Society, 367-378. Springer Berlin Heidelberg.
  • Somyürek, S. (2008). The effects of adaptive educational web environments to learners academic achievement and navigation (Unpublished doctoral dissertation). Gazi University, Ankara.
  • Stash, N., Cristea, A. & De Bra, P. (2006, January). Learning Styles Adaptation Language for Adaptive Hypermedia. Adaptive Hypermedia and Adaptive Web-Based Systems. s. 323-327. Berlin Heidelberg: Springer.
  • Şimşek, N. (2002). BIG16 Learning Modality Inventory. Educational Sciences and Practice, 1(1). Triantafillou, E., Pomportsis, A. & Georgiadou, E. (2002). AES-CS: Adaptive Educational System Based on Cognitive Styles. Adaptive Hypermedia 2002 Workshop on Adaptive Systems for Web-based Education. Universidad de Malaga, Malaga, Spain.
  • Tseng, J., Chu, H., Hwang, G. & Tsai, C. (2008). Development of an Adaptive Learning System with Two Sources of Personalization İnformation. Computers & Education, 51(2), 776–786.
  • Uysal, M.P. (2008). The effects of instructional software designed in accordance with instructional transaction theory and the adaptive drill software on achievements of students (Unpublished doctoral dissertation). Gazi Uni-versity, Institute of Education Sciences, Department of Education Sciences, Ankara.
  • Weber, G. & Brusilovsky, P. (2001). ELM-ART: An Adaptive Versatile System for Web-based Instruction. Internatio-nal Journal of Artificial Intelligence in Education (2001), 12, 351-384.
  • Weber, G. (1999). Adaptive Learning Systems in the World Wide Web (pp. 371-377). Vienna: Springer.
  • Weibelzahl, S. (2005). Problems and Pitfalls in the Evaluation of Adaptive Systems. In S. Chen and G. Magoulas (Eds.), Adaptable and Adaptive Hypermedia Systems (pp. 285-299). Hershey, PA: IRM Press.
  • Yang, T. C., Hwang, G. J. & Yang, S. J. H. (2013). Development of an Adaptive Learning System with Multiple Perspec-tives based on Students? Learning Styles and Cognitive Styles. Educational Technology & Society, 16(4), 185-200.
There are 51 citations in total.

Details

Primary Language Turkish
Subjects Studies on Education
Journal Section Review Article
Authors

Özlem Canan Güngören 0000-0002-9184-6110

Publication Date May 15, 2019
Acceptance Date August 7, 2018
Published in Issue Year 2019 Volume: 27 Issue: 3

Cite

APA Canan Güngören, Ö. (2019). Uyarlanabilir Eğitsel Web Ortamlarının Öğrencilerin Akademik Başarılarına ve Motivasyonlarına Etkisi. Kastamonu Education Journal, 27(3), 1311-1326. https://doi.org/10.24106/kefdergi.3079

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