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WEB-TABANLI ÖZ-DEĞERLENDİRME SİSTEMİNDE ÖĞRENCİ UYARI İNDEKSİNİ TEMEL ALAN ÖĞRENME ANALİTİĞİ MODÜLÜNÜN TASARLANMASI

Year 2016, Volume: 6 Issue: 2, 85 - 99, 27.07.2016
https://doi.org/10.17943/etku.59549

Abstract

Çalışmanın amacı öğrenenlerin kendini test edebilmeleri için hazırlanan web tabanlı öz-değerlendirme sistemi için mikro düzeyde bir öğrenme analitiği modülünün tasarlanmasıdır. Web-tabanlı öz-değerlendirme sistemi öğrenci merkezli bir sistemdir ve öğrenenlerin kendilerini test ettikten sonra öğrenenlere var olan durumları hakkında bilgi (dönüt) sağlayan öğrenme analitiği modülü sistemin en önemli bileşenidir. Buna bağlı olarak bu modülün tasarlanmasında madde tepki kuramının aksine, gözlem sayısı az olan sınıf içi değerlendirme uygulamalarında öğrencinin var olan durumunu tanılamak için kullanılması önerilen SATO öğrenci uyarı indeksleri temel alınmıştır. Öğrenci uyarı indeksi, öğrencinin sınıf içi durumuna ve maddelerin güçlük indekslerine bağlı olarak hesaplanmaktadır. Öğrenci uyarı indeksi ve cevaplama oranına bağlı olarak öğrencinin sınıflandırması yapılabilmektedir. Bu sınıflamada altı kategori bulunmaktadır. Bu kategorilere bağlı olarak öğrencinin etkili bir öğrenme gerçekleştirip gerçekleştirmediği, dikkatsizlik sonucu yanlış cevaplandırdığı soruların olup olmadığı, eksik olduğu konuların olup olmadığını belirlenebilmektedir. Araştırma kapsamında bu kategorinin gösterge panelinde nasıl gösterileceği tasarlanacaktır.  

References

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  • Kumar, V.S., Kinshuk, Clemens, C., & Harris, S. (2015). Causal models and big data learning analytics. In Kinshuk, & R. Huang (Eds.), Ubiquitous learning environments and technologies (pp. 31–53). Berlin, Germany: Springer Berlin Heidelberg. doi: 10.1007/978-3-662-44659-1_3
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  • Molloy, E.K., Borrell-Carrio, F., & Epstein, R., (2013), The impact of emotions in feedback, in D. Boud & E. Molly (Eds.) Feedback in Higher and Professional Education - Understanding It and Doing It Well, 50-71. London: Routledge.
  • Mottus, A., Graf, S., & Chen, N. S. (2015). Use of Dashboards and Visualization Techniques to Support Teacher Decision Making. In Kinshuk, & R. Huang (Eds.), Ubiquitous learning environments and technologies (pp. 181-199). Berlin, Germany: Springer Berlin Heidelberg. doi: 10.1007/978-3-662-44659-1_3
  • Nicol, D. (2007). E-assessment by design: using multiple choice tests to good effect. Journal of Further and Higher Education, 31(1), 53-64, doi: 10.1080/03098770601167922
  • Shum, B. (2012). Learning Analytics. UNESCO Policy Brief. Retrieved from http://iite.unesco.org/files/policy_briefs/pdf/en/learning_analytics.pdf.
  • Styll, R. (2013). Fast Dashboards Anywhere with SAS® Visual Analytics. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.381.918&rep=rep1&type=pdf.
  • Yigitbasioglu, O. M., & Velcu, O. (2012). A review of dashboards in performance management: Implications for design and research. International Journal of Accounting Information Systems, 13(1), 41-59.
  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 0002764213479363.
  • Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032-1044. doi:http://dx.doi.org/10.1016/j.compedu.2010.11.017
  • Wang, T. H., Wang, K. H., Wang, W. L., Huang, S. C., & Chen, S. Y. (2004). Web-based Assessment and Test Analyses (WATA) system: development and evaluation. Journal of Computer Assisted Learning, 20(1), 59-71.
  • Wu, H. (1998). Software based on S-P chart analysis and its applications. Proc. Natl. Sci. Counc. ROC (D), 8(3), 108-120.
Year 2016, Volume: 6 Issue: 2, 85 - 99, 27.07.2016
https://doi.org/10.17943/etku.59549

Abstract

References

  • Acar, T. (2006). Sato uyarı indeksleri ile madde ve başarı analizleri. [Çevrim-içi: http://www.parantezegitim.net/hakkimizda/Sato-TulinACAR.pdf], Erişim Tarihi: 07.06.2012.
  • Ahokas, T. (2008). Information Visualization in a Business Decision Support System, Master Thesis, University of Helsinki, Finland.
  • Andrade, H., & Valtcheva, A. (2009). Promoting learning and achievement through self-assessment. Theory into Practice, 48(1), 12-19.
  • Bajzek, D., Brown, W., Lovett, M. & Rule, G. (2007). Inventing the Digital Dashboard for Learning. In C. Montgomerie & J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications (pp. 1084-1092). Chesapeake, VA: AACE.
  • Brew, A. (1999) Towards autonomous assessment: using self-assessment and peer assessment, in: S. Brown & A. Glasner (Eds) Assessment matters in higher education: choosing and using diverse assessment, 159–171. Buckingham, Open University Press/SRHE.
  • Boud, D. (2013). Enhancing learning through self-assessment. Routledge.
  • Chang, W. C., Yang, H. C., Shih, T. K., & Chao, L. R. (2009). Using S-P chart and Bloom taxonomy to develop intelligent formative assessment tool. International Journal of Distance Education Technologies (IJDET),7(4), 1-16.
  • Chen, D., Lai, A., & Liu, I. (2005). The design and implementation of a diagnostic test system based on the enhanced S-P model. Journal of Information Science and Engineering, 21, 1007-1030.
  • Clow , D. ( 2012). “ The Learning Analytics Cycle: Closing the Loop Effectively .” In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK12), Vancouver, BC, Canada, April 29–May 02, 2012 , Simon Buckingham
  • Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695.
  • Few, S. (2006). Information dashboard design: The Effective Visual Communication of Data. O'Reilly.
  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77, 81-112
  • Irons, A., (2008). Enhancing learning through formative assessment and feedback. Routledge, New York
  • Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., and Ludgate, H. (2013). NMC Horizon Report: 2013 Higher Education Edition. Austin, Texas: The New Media Consortium.
  • Johnson, L., Adams Becker, S., Estrada, V., Freeman, A. (2014). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium.
  • Kumar, V.S., Kinshuk, Clemens, C., & Harris, S. (2015). Causal models and big data learning analytics. In Kinshuk, & R. Huang (Eds.), Ubiquitous learning environments and technologies (pp. 31–53). Berlin, Germany: Springer Berlin Heidelberg. doi: 10.1007/978-3-662-44659-1_3
  • Lal, P. (2014). Designing Online Learning Strategies through Analytics. In F. J. García-Peñalvo & A. M. Seoane Pardo (Eds.), Online Tutor 2.0: Methodologies and Case Studies for Successful Learning: Methodologies and Case Studies for Successful Learning, (pp. 1-15). Hershey, PA: IGI Global.
  • Mok, M. M. C., Lam, S. M., Ngan, M. Y., Yao, J. J., Wong, M. Y. W., Xu, J. K., & Ting, S. Y. C. (2012). Student-problem chart: An essential tool for SLOA. In M. M. C. Mok (Eds), Self-directed Learning Oriented Assessments in the Asia-Pacific, (pp. 18, 203). Springer Netherlands
  • Molloy, E.K., Borrell-Carrio, F., & Epstein, R., (2013), The impact of emotions in feedback, in D. Boud & E. Molly (Eds.) Feedback in Higher and Professional Education - Understanding It and Doing It Well, 50-71. London: Routledge.
  • Mottus, A., Graf, S., & Chen, N. S. (2015). Use of Dashboards and Visualization Techniques to Support Teacher Decision Making. In Kinshuk, & R. Huang (Eds.), Ubiquitous learning environments and technologies (pp. 181-199). Berlin, Germany: Springer Berlin Heidelberg. doi: 10.1007/978-3-662-44659-1_3
  • Nicol, D. (2007). E-assessment by design: using multiple choice tests to good effect. Journal of Further and Higher Education, 31(1), 53-64, doi: 10.1080/03098770601167922
  • Shum, B. (2012). Learning Analytics. UNESCO Policy Brief. Retrieved from http://iite.unesco.org/files/policy_briefs/pdf/en/learning_analytics.pdf.
  • Styll, R. (2013). Fast Dashboards Anywhere with SAS® Visual Analytics. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.381.918&rep=rep1&type=pdf.
  • Yigitbasioglu, O. M., & Velcu, O. (2012). A review of dashboards in performance management: Implications for design and research. International Journal of Accounting Information Systems, 13(1), 41-59.
  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 0002764213479363.
  • Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032-1044. doi:http://dx.doi.org/10.1016/j.compedu.2010.11.017
  • Wang, T. H., Wang, K. H., Wang, W. L., Huang, S. C., & Chen, S. Y. (2004). Web-based Assessment and Test Analyses (WATA) system: development and evaluation. Journal of Computer Assisted Learning, 20(1), 59-71.
  • Wu, H. (1998). Software based on S-P chart analysis and its applications. Proc. Natl. Sci. Counc. ROC (D), 8(3), 108-120.
There are 28 citations in total.

Details

Journal Section Articles
Authors

Fatma Bayrak

Halil Yurdugül

Publication Date July 27, 2016
Published in Issue Year 2016 Volume: 6 Issue: 2

Cite

APA Bayrak, F., & Yurdugül, H. (2016). WEB-TABANLI ÖZ-DEĞERLENDİRME SİSTEMİNDE ÖĞRENCİ UYARI İNDEKSİNİ TEMEL ALAN ÖĞRENME ANALİTİĞİ MODÜLÜNÜN TASARLANMASI. Eğitim Teknolojisi Kuram Ve Uygulama, 6(2), 85-99. https://doi.org/10.17943/etku.59549