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Bireysel Farklılıklar Kapsamında Çevrimiçi Öğrenme Araştırmalarına İlişkin Sistematik Bir Derleme

Year 2018, , 1003 - 1018, 24.10.2018
https://doi.org/10.30831/akukeg.407289

Abstract

e-Öğrenme aynı anda büyük kitlelere ulaşmada, eğitim-öğretim faaliyetleri yürütmede etkili, hızlı ve verimli çözümler sunabilmektedir. Birbirinden farklı özelliklere sahip katılımcıların bulunduğu bu ortamlarda bireylere çeşitlendirilmiş olsa da ideal kullanıcı varsayımına göre içerik türleri sunulmaktadır. Oysaki her birey doğuştan getirdiği ve sonradan kazandığı bir takım özelliklere sahiptir. Bu bireysel farklılıklar da öğrenme ortamlarında etkili öğrenme deneyimleri sağlayabilmek adına önemlidir. Bu bağlamda alanyazın incelendiğinde de bireysel farklılıkları odağına alan birçok e-öğrenme çalışması olduğu görülmektedir. Kişilik özellikleri, bilişsel özellikler, geçmiş öğrenme deneyimleri gibi sıralanabilen bu değişkenlerin bireylerin akademik başarılarına, motivasyonlarına, katılım düzeylerine ve sistemde kalmalarına olan etkileri ve aralarındaki ilişkiler incelenmiştir. Bu çalışmanın temel amacı sistematik bir alanyazın taraması gerçekleştirilerek, 2010-2017 yılları arasında yayınlanan çalışmalara konu olan bireysel farklılıkların belirlenmesi ve bu farklılıkların hangi bağımlı değişkenlerle incelendiğinin ortaya konulmasıdır. Bu kapsamda belirlenen alanyazın tarama kriterlerine göre (anahtar kelimeler, seçim kriterleri, yöntem) ISI Web of Knowledge, Ebscohost, Scopus ve JSTOR veritabanlarında taramalar gerçekleştirilmiş olup 38 makale çalışmaya dahil edilmiştir. Öne çıkan bulgular bireysel farklılıklar bağlamında bilişsel özelliklerin diğer değişkenlere oranla daha az incelendiğini gösterirken en fazla incelenen değişkenlerin demografik değişkenler ve kişilik özellikleri oldukları bulunmuştur.

References

  • Accuray Research LLP. (2017). Global E-Learning Market Analysis & Trends - Industry Forecast to 2025. Retrieved from https://www.researchandmarkets. com/research/qgq5vf/global_elearning.
  • Bear, A. A. G. (2012). Technology, Learning, and Individual Differences. Journal of Adult Education, 41(2), 27-42.
  • Chen, S. Y., & Macredie, R. (2010). Web-based interaction: A review of three important human factors. International Journal of Information Management, 30(5), 379-387. doi:10.1016/j.ijinfomgt.2010.02.009
  • Cho, M.H., Demei, S., & Laffey, J. (2010). Relationships Between Self-Regulation and Social Experiences in Asynchronous Online Learning Environments. Journal of Interactive Learning Research, 21(3), 297-316.
  • Clay, M. N., Rowland, S., & Packard, A. (2009). Improving undergraduate online retention through gated advisement and redundant communication. Journal of College Student Retention: Research, Theory and Practice, 10(1), 93–102.
  • Clewley, N., Chen, S. Y., & Liu, X. (2011). Mining Learning Preferences in Web-based Instruction: Holists vs. Serialists. Educational Technology & Society, 14 (4), 266–277.
  • Deborah, L. J., Baskaran, R., & Kannan, A. (2014). Learning styles assessment and theoretical origin in an e-learning scenario: A survey. Artificial Intelligence Review, 42(4), 801–819.
  • Ekwunife-Orakwue, K. C. V., & Tian-Lih, T. (2014). The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education, 78, 414-427. doi:10.1016/j. compedu.2014.06.011
  • Felder, R. M., & Silverman, L. K. (1988). Learning styles and teaching styles in engineering education. Engineering Education, 78(7), 674–681.
  • Ghorbani, F., & Montazer, G. A. (2015). E-learners' personality identifying using their network behaviors. Computers in Human Behavior, 51(PA), 42-52. doi:10.1016/j.chb.2015.04.043
  • Gökçearslan, Ş., & Alper, A. (2015). The effect of locus of control on learners' sense of community and academic success in the context of online learning communities. Internet and Higher Education, 27, 64-73. doi:10.1016/j.iheduc.2015.06.003
  • Granić, A., & Adams, R. (2011). User sensitive research in e-learning: Exploring the role of individual user characteristics. Universal Access in the Information Society, 10(3), 307-318. doi:10.1007/s10209-010-0207-7
  • Higgins, JPT, & Green, S. (2011). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration. Retrieved from http://handbook.cochrane.org.
  • Hsia, J. W., Chang, C. C., & Tseng, A. H. (2014). Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour and Information Technology, 33(1), 51-64. doi:10.1080/0144929X.2012.702284
  • Ivankova, N. V., & Stick, S. L. (2007). Students’ persistence in a distributed doctoral program in educational leadership in higher education: A mixed methods study. Research in Higher Education, 48(1), 93–135.
  • Jashapara, A., & Tai, W. C. (2011). Knowledge Mobilization Through E-Learning Systems: Understanding the Mediating Roles of Self-Efficacy and Anxiety on Perceptions of Ease of Use. Information Systems Management, 28(1), 71-83. doi:10.1080/10580530.2011.536115
  • Jraidi, I., & Frasson, C. (2013). Student’s Uncertainty Modeling through a Multimodal Sensor-Based Approach. Educational Technology & Society, 16 (1), 219–230.
  • Kanar, A. M., & Bell, B. S. (2013). Guiding Learners through Technology-Based Instruction: The Effects of Adaptive Guidance Design and Individual Differences on
  • Learning over Time. Journal of Educational Psychology, 105(4), 1067-1081.
  • Keller, H., & Karau, S. J. (2013). The importance of personality in students' perceptions of the online learning experience. Computers in Human Behavior, 29(6), 2494-2500. doi:10.1016/j.chb.2013.06.007
  • Kirschner, P. A. & van Merriënboer, J.J.G. (2013). Do Learners Really Know Best? Urban Legends in Education. Educational Psychologist, 48(3), 169-183.doi: 10.1080/00461520.2013.804395
  • Kolb, D. A. (1984). Experiential learning. Englewood Cliffs, NJ: Prentice Hall.
  • Lee, Y., & Choi, J. (2011). A review of online course dropout research: implications for practice and future research. Educational Technology Research and Development, 59(5), 593-618. doi:10.1007/s11423-010-9177-y
  • 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
  • Lu, H.-P., & Chiou, M.-J. (2010). The impact of individual differences on e-learning system satisfaction: A contingency approach. British Journal of Educational Technology, 41(2), 307-323. doi:10.1111/j.1467-8535.2009.00937.x
  • McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52, 81-90.
  • McCrae, R. R. and John, O. P. (1992), An Introduction to the Five-Factor Model and Its Applications. Journal of Personality, 60, 175–215. doi:10.1111/j.1467-6494.1992.tb00970.x
  • Nawrot, I., & Doucet, A. (2014). Building engagement for MOOC students: introducing support for time management on online learning platforms. Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, 1077-1082.
  • Orvis, K. A., Brusso, R. C., Wasserman, M. E., & Fisher, S. L. (2011). E-nabled for E-Learning? The Moderating Role of Personality in Determining the Optimal Degree of Learner Control in an E-Learning Environment. Human Performance, 24(1), 60-78. doi:10.1080/08959285.2010.530633
  • Park, J.Y., & Choi, H.J. (2009). Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning. Journal of Educational Technology & Society, 12(4), 207-217.
  • Poll, K., Widen, J., & Weller, S. (2014). Six instructional best practices for online engagement and retention. Journal of Online Doctoral Education, 1(1), 56- 72
  • Price, L. (2004). Individual Differences in Learning: Cognitive control, cognitive style, and learning style. Educational Psychology, 24(5), 681-698. doi: 10.1080/0144341042000262971
  • Raes, A., Schellens, T., De Wever, B., & Vanderhoven, E. (2012). Scaffolding information problem solving in web-based collaborative inquiry learning. Computers & Education, 59(1), 82-94. doi:10.1016/j.compedu.2011.11.010
  • Randler, C., Horzum, M. B., & Vollmer, C. (2014). The Influence of Personality and Chronotype on Distance Learning Willingness and Anxiety among Vocational High School Students in Turkey. International Review of Research in Open and Distance Learning, 15(6), 93-110.
  • Ren, Y., Dai, Z.-x., Zhao, X.-h., Fei, M.-m., & Gan, W.-t. (2017). Exploring an on-line course applicability assessment to assist learners in course selection and learning effectiveness improving in e-learning. Learning & Individual Differences, 60, 56-62. doi:10.1016/j.lindif.2017.09.002
  • Rovai, A. P. (2001). Classroom community at a distance: A comparative analysis of two ALN-based university programs. The Internet and Higher Education, 4(2), 105-118.
  • Sanchez-Franco, M. J., Peral-Peral, B., & Villarejo-Ramos, A. F. (2014). Users' intrinsic and extrinsic drivers to use a web-based educational environment. Computers & Education, 74, 81-97. doi:10.1016/j.compedu.2014.02.001
  • Schaeffer, C. E., & Konetes, G. D. (2010). Impact of learner engagement on attrition rates and student success in online learning. International Journal of Instructional Technology & Distance Learning, 7(5), 3-9.
  • Sun, J. C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2). 191–204. doi:10.1111/j.1467-8535.2010.01157.x
  • Tempelaar, D. T., Niculescu, A., Rienties, B., Gijselaers, W. H., & Giesbers, B. (2012). How Achievement Emotions Impact Students' Decisions for Online Learning, and What Precedes Those Emotions. Internet and Higher Education, 15(3), 161-169.
  • Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47, 1-64.

A Systematic Review of Online Learning Researches in the Context of Individual Differences

Year 2018, , 1003 - 1018, 24.10.2018
https://doi.org/10.30831/akukeg.407289

Abstract

Online learning has been offering practical,
efficient and effective solutions for reaching out big groups of learners in
terms of learning and education. In these environments, where the participants
have distinct properties, although the contents have been diversified, it has
been represented with the assumption of ideal user. These environments consist
many people who have very distinct properties from each other. Even if the
presented content has been diversified, mostly it is designed for the ideal
user. However, every person has some individual characteristics, which can be
natal and acquired in time. These individual differences are important in order
to provide effective learning experiences in learning environments. In this
context when the e-learning studies analyzed it can be seen that there are
several studies, which focused on individual differences. In these studies, the
effects of the variables such as personal traits, cognitive characteristics,
and prior learning experiences on individuals’ academic success, motivation,
participation levels, and staying on system, and the relationships had been analyzed.
The main objective of this study is conducting a systematic review with the aim
of identifying which individual differences studied and their relationships
with other variables between 2010-2017 years. In this context, researcher
defined review criteria (keywords, selection criteria, method) and conducted
review process. According to search results, 38 research articles have been
included in this study. Prominent results show that cognitive differences have
been worked less in comparison with the other variables in terms of individual
differences perspective, while the most worked variables are demographic
variables and personal traits. 

References

  • Accuray Research LLP. (2017). Global E-Learning Market Analysis & Trends - Industry Forecast to 2025. Retrieved from https://www.researchandmarkets. com/research/qgq5vf/global_elearning.
  • Bear, A. A. G. (2012). Technology, Learning, and Individual Differences. Journal of Adult Education, 41(2), 27-42.
  • Chen, S. Y., & Macredie, R. (2010). Web-based interaction: A review of three important human factors. International Journal of Information Management, 30(5), 379-387. doi:10.1016/j.ijinfomgt.2010.02.009
  • Cho, M.H., Demei, S., & Laffey, J. (2010). Relationships Between Self-Regulation and Social Experiences in Asynchronous Online Learning Environments. Journal of Interactive Learning Research, 21(3), 297-316.
  • Clay, M. N., Rowland, S., & Packard, A. (2009). Improving undergraduate online retention through gated advisement and redundant communication. Journal of College Student Retention: Research, Theory and Practice, 10(1), 93–102.
  • Clewley, N., Chen, S. Y., & Liu, X. (2011). Mining Learning Preferences in Web-based Instruction: Holists vs. Serialists. Educational Technology & Society, 14 (4), 266–277.
  • Deborah, L. J., Baskaran, R., & Kannan, A. (2014). Learning styles assessment and theoretical origin in an e-learning scenario: A survey. Artificial Intelligence Review, 42(4), 801–819.
  • Ekwunife-Orakwue, K. C. V., & Tian-Lih, T. (2014). The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education, 78, 414-427. doi:10.1016/j. compedu.2014.06.011
  • Felder, R. M., & Silverman, L. K. (1988). Learning styles and teaching styles in engineering education. Engineering Education, 78(7), 674–681.
  • Ghorbani, F., & Montazer, G. A. (2015). E-learners' personality identifying using their network behaviors. Computers in Human Behavior, 51(PA), 42-52. doi:10.1016/j.chb.2015.04.043
  • Gökçearslan, Ş., & Alper, A. (2015). The effect of locus of control on learners' sense of community and academic success in the context of online learning communities. Internet and Higher Education, 27, 64-73. doi:10.1016/j.iheduc.2015.06.003
  • Granić, A., & Adams, R. (2011). User sensitive research in e-learning: Exploring the role of individual user characteristics. Universal Access in the Information Society, 10(3), 307-318. doi:10.1007/s10209-010-0207-7
  • Higgins, JPT, & Green, S. (2011). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration. Retrieved from http://handbook.cochrane.org.
  • Hsia, J. W., Chang, C. C., & Tseng, A. H. (2014). Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour and Information Technology, 33(1), 51-64. doi:10.1080/0144929X.2012.702284
  • Ivankova, N. V., & Stick, S. L. (2007). Students’ persistence in a distributed doctoral program in educational leadership in higher education: A mixed methods study. Research in Higher Education, 48(1), 93–135.
  • Jashapara, A., & Tai, W. C. (2011). Knowledge Mobilization Through E-Learning Systems: Understanding the Mediating Roles of Self-Efficacy and Anxiety on Perceptions of Ease of Use. Information Systems Management, 28(1), 71-83. doi:10.1080/10580530.2011.536115
  • Jraidi, I., & Frasson, C. (2013). Student’s Uncertainty Modeling through a Multimodal Sensor-Based Approach. Educational Technology & Society, 16 (1), 219–230.
  • Kanar, A. M., & Bell, B. S. (2013). Guiding Learners through Technology-Based Instruction: The Effects of Adaptive Guidance Design and Individual Differences on
  • Learning over Time. Journal of Educational Psychology, 105(4), 1067-1081.
  • Keller, H., & Karau, S. J. (2013). The importance of personality in students' perceptions of the online learning experience. Computers in Human Behavior, 29(6), 2494-2500. doi:10.1016/j.chb.2013.06.007
  • Kirschner, P. A. & van Merriënboer, J.J.G. (2013). Do Learners Really Know Best? Urban Legends in Education. Educational Psychologist, 48(3), 169-183.doi: 10.1080/00461520.2013.804395
  • Kolb, D. A. (1984). Experiential learning. Englewood Cliffs, NJ: Prentice Hall.
  • Lee, Y., & Choi, J. (2011). A review of online course dropout research: implications for practice and future research. Educational Technology Research and Development, 59(5), 593-618. doi:10.1007/s11423-010-9177-y
  • 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
  • Lu, H.-P., & Chiou, M.-J. (2010). The impact of individual differences on e-learning system satisfaction: A contingency approach. British Journal of Educational Technology, 41(2), 307-323. doi:10.1111/j.1467-8535.2009.00937.x
  • McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52, 81-90.
  • McCrae, R. R. and John, O. P. (1992), An Introduction to the Five-Factor Model and Its Applications. Journal of Personality, 60, 175–215. doi:10.1111/j.1467-6494.1992.tb00970.x
  • Nawrot, I., & Doucet, A. (2014). Building engagement for MOOC students: introducing support for time management on online learning platforms. Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, 1077-1082.
  • Orvis, K. A., Brusso, R. C., Wasserman, M. E., & Fisher, S. L. (2011). E-nabled for E-Learning? The Moderating Role of Personality in Determining the Optimal Degree of Learner Control in an E-Learning Environment. Human Performance, 24(1), 60-78. doi:10.1080/08959285.2010.530633
  • Park, J.Y., & Choi, H.J. (2009). Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning. Journal of Educational Technology & Society, 12(4), 207-217.
  • Poll, K., Widen, J., & Weller, S. (2014). Six instructional best practices for online engagement and retention. Journal of Online Doctoral Education, 1(1), 56- 72
  • Price, L. (2004). Individual Differences in Learning: Cognitive control, cognitive style, and learning style. Educational Psychology, 24(5), 681-698. doi: 10.1080/0144341042000262971
  • Raes, A., Schellens, T., De Wever, B., & Vanderhoven, E. (2012). Scaffolding information problem solving in web-based collaborative inquiry learning. Computers & Education, 59(1), 82-94. doi:10.1016/j.compedu.2011.11.010
  • Randler, C., Horzum, M. B., & Vollmer, C. (2014). The Influence of Personality and Chronotype on Distance Learning Willingness and Anxiety among Vocational High School Students in Turkey. International Review of Research in Open and Distance Learning, 15(6), 93-110.
  • Ren, Y., Dai, Z.-x., Zhao, X.-h., Fei, M.-m., & Gan, W.-t. (2017). Exploring an on-line course applicability assessment to assist learners in course selection and learning effectiveness improving in e-learning. Learning & Individual Differences, 60, 56-62. doi:10.1016/j.lindif.2017.09.002
  • Rovai, A. P. (2001). Classroom community at a distance: A comparative analysis of two ALN-based university programs. The Internet and Higher Education, 4(2), 105-118.
  • Sanchez-Franco, M. J., Peral-Peral, B., & Villarejo-Ramos, A. F. (2014). Users' intrinsic and extrinsic drivers to use a web-based educational environment. Computers & Education, 74, 81-97. doi:10.1016/j.compedu.2014.02.001
  • Schaeffer, C. E., & Konetes, G. D. (2010). Impact of learner engagement on attrition rates and student success in online learning. International Journal of Instructional Technology & Distance Learning, 7(5), 3-9.
  • Sun, J. C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2). 191–204. doi:10.1111/j.1467-8535.2010.01157.x
  • Tempelaar, D. T., Niculescu, A., Rienties, B., Gijselaers, W. H., & Giesbers, B. (2012). How Achievement Emotions Impact Students' Decisions for Online Learning, and What Precedes Those Emotions. Internet and Higher Education, 15(3), 161-169.
  • Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research, 47, 1-64.
There are 41 citations in total.

Details

Primary Language Turkish
Subjects Studies on Education
Journal Section Articles
Authors

Hale Ilgaz 0000-0001-7011-5354

Publication Date October 24, 2018
Submission Date March 17, 2018
Published in Issue Year 2018

Cite

APA Ilgaz, H. (2018). Bireysel Farklılıklar Kapsamında Çevrimiçi Öğrenme Araştırmalarına İlişkin Sistematik Bir Derleme. Journal of Theoretical Educational Science, 11(4), 1003-1018. https://doi.org/10.30831/akukeg.407289

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