The effect of using negative knowledge based intelligent tutoring system evaluator software to the academic success in English language education

References

• Abushihab, I. (2014). An analysis of grammatical errors in writing made by Turkish learners of English as a foreign language. International Journal of Linguistics, 6(4), 213–223.
   Google Scholar
• Akpınar, B., & Akdoğan, S. (2010). Negative knowledge concept: Learning from mistakes and failures. Western Anatolian Journal of Educational Sciences, 1(1), 14–22.
   Google Scholar
• Alpkan, L., & Dogan, T. (2008). Performance impacts of the strategic planning process components. Kocaeli University Journal of Social Sciences, (16), 21–47.
   Google Scholar
• Alsagoff, L., McKay, S. L., Hu, G., & Renandya, W. A. (Eds). (2012). Principles and practices for teaching English as an international language. New York, NY: Routledge.
   Google Scholar
• An, S., & Wu, Z. (2012). Enhancing mathematics teachers’ knowledge of students’ thinking from assessing and analyzing misconceptions in homework. International Journal of Science and Mathematics Education, 10(3), 717–753.
   Web of Science ®Google Scholar
• Anohina-Naumeca, A. (2015). Justifying the usage of concept mapping as a tool for the formative assessment of the structural knowledge of engineering students. Knowledge Management & E-Learning, 7(1), 56.
   Google Scholar
• Arıcı, N., & Karacı, A. (2013). Intelligent tutoring system for Turkish learning (TÜRKZÖS) and evaluation. Turkish Studies, 8(8), 65–87.
   Google Scholar
• Ates, A., Altunay, U., & Altun, E. (2006). The effects of computer-assisted english instruction on high school preparatory students’ attitudes towards computers and english. Journal of Theory and Practice in Education, 2(2), 97–112.
   Google Scholar
• Azmat, G., & Iriberri, N. (2010). The importance of relative performance feedback information: Evidence from a natural experiment using high school students. Journal of Public Economics, 94(7–8), 435–452.
   Web of Science ®Google Scholar
• Barrick, M. R., Mount, M. K., & Judge, T. A. (2001). Personality and performance at the beginning of the new millennium: What do we know and where do we go next? International Journal of Selection and Assessment, 9(1–2), 9–30.
   Web of Science ®Google Scholar
• Bauer, J., & Mulder, R. H. (2007). Modeling learning from errors in daily work. Learning in Health and Social Care, 6(3), 121–133.
   Google Scholar
• Bickhard, M. H. (2004). The social ontology of persons. In Carpendale, J. I. M., & Müller, U. (Eds.), Social Interaction and the Development of Knowledge(pp. 111–132). New York, NY: Psychology.
   Google Scholar
• Bransford, J. D., Sherwood, R. D., Hasselbring, T. S., Kinzer, C. K., & Williams, S. M. (1990). Anchored instruction: Why we need it and how technology can help. Cognition, Education, and Multimedia: Exploring Ideas in High Technology, 12, 1.
   Google Scholar
• Browning, M. E., & Lehmen, J. D. (1988). Identification of students’ misconception in genetic problem solving via computer program. Journal of Research in Science Teaching, 25(9), 741–761.
   Google Scholar
• Burns, H. L., & Capps, C. G. (2013). Foundations of Intelligent Tutoring Systems: An Introduction. In Polson, M. C., & Richardson, J. J. (Eds.), Foundations of Intelligent Tutoring Systems (pp. 1–19). New Jersey, NJ: Lawrance Erlbaum Associates.
   Google Scholar
• Büyükkasap, E., Düzgün, B., Ertuğrul, M., & Samancı, O. (1998). The impact of computer assisted science teaching on the misconceptions. Kastamonu Education Journal, 6, 59–66.
   Google Scholar
• Büyüköztürk, Ş. (2003). Handbook of data analyses for social sciences. Ankara: Pegema Publishing.
   Google Scholar
• Cannon, M. D., & Edmondson, A. C. (2005). Failing to learn and learning to fail (intelligently): How great organizations put failure to work to innovate and improve. Long Range Planning, 38(3), 299–319.
   Web of Science ®Google Scholar
• Casakin, H. (2011). Metaphorical reasoning and design expertise: A perspective for design education. Journal of Learning Design, 4(2), 29–38.
   Google Scholar
• Çetin Köroğlu, Z. (2014). An analysis of grammatical errors of Turkish EFL students’ written texts. Turkish Studies, 9(12), 101–111.
   Google Scholar
• Chambers, J. A., & Sprecher, J. W. (1980). Computer-assisted instruction: Current trends and critical issues. Communications of the ACM, 23(6), 332–342.
   Web of Science ®Google Scholar
• Chapelle, C. A. (2008). Computer-Assisted Language Learning. In Spolsky B. & Hult, H. M. (Eds.), The Handbook of Educational Linguistics, (pp.585–595). Malden, MA: Blackwell Publishing Ltd.
   Google Scholar
• Chen, C. M., & Duh, L. J. (2008). Personalized web-based tutoring system based on fuzzy item response theory. Expert Systems with Applications, 34(4), 2298–2315.
   Web of Science ®Google Scholar
• Chowdhury, G. G. (2003). Natural language processing. Annual Review of Information Science and Technology, 37(1), 51–89.
   Google Scholar
• Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. New Jersey, NJ: John Wiley & Sons.
   Google Scholar
• Dağ, F., & Erkan, K. (2004). Prolog based an intelligent tutoring system. Denizli. Journal of Engineering Sciences, Information Technology Congress, 47–55.
   Google Scholar
• Davidovic, A., Warren, J., & Trichina, E. (2003). Learning benefits of structural example-based adaptive tutoring systems. IEEE Transactions on Education, 46(2), 241–251.
   Web of Science ®Google Scholar
• Deeks, J. J. (2001). Systematic reviews in health care: Systematic reviews of evaluations of diagnostic and screening tests. BMJ, 323(7305), 157–162.
   PubMed Web of Science ®Google Scholar
• Ekiz, D. (2003). Introduction to research methods and methods in education: Qualitative, quantitative and critical theory methodologies. Ankara: Anı Yayıncılık.
   Google Scholar
• Er, K. O. (2006). Evaluation of English curricula in 4th and 5th-grade primary schools. Ankara University, Journal of Faculty of Educational Sciences, 39(2), 1–25.
   Google Scholar
• Erdoğan, V. (2005). Contribution of error analysis to foreign language teaching. Mersin Üniversitesi Egitim Fakültesi Dergisi, 1(2), 261–270.
   Google Scholar
• Galvez, J., Guzman, E., Conejo, R., & Millan, E. (2009). Student knowledge diagnosis using item response theory and constraint-based modeling. In Proceeding of the 2009 Conference on Artificial intelligence in Education: Building Learning Systems that Care: From Knowledge Representation To Affective Modelling (Vol. 200., pp. 291–298). Amsterdam: IOS Press
   Google Scholar
• Gartmeier, M., Bauer, J., Gruber, H., & Heid, H. (2008). Negative knowledge: Understanding professional learning and expertise. Vocations and Learning, 1(2), 87–103.
   Web of Science ®Google Scholar
• Gömleksiz ve Serdemir Düşmez. (2005). A comparison of computer assisted language learning and traditional method on student’s achievement in teaching relative clauses. The Journal of Turkish Educational Sciences, 3(2), 163–179.
   Google Scholar
• Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education, 48(4), 612–618.
   Web of Science ®Google Scholar
• Gündüz, N. (2005). Computer-assisted language learning. Journal of Language and Linguistic Studies, 1(2), 193–214.
   Google Scholar
• Heilman, M., Collins-Thompson, K., Callan, J., & Eskenazi, M. (2006). Classroom success of an intelligent tutoring system for lexical practice and reading comprehension. In Ninth International Conference on Spoken Language Processing. Pittsburgh, PA: ISCA.
   Google Scholar
• Heinze, A. (2005). Mistake-handling activities in the mathematics classroom. International Group for the Psychology of Mathematics Education, 3, 105–112.
   Google Scholar
• Hotomaroglu, T. A. (2002). Development of an expert system based program for computer-assisted instruction and assessment of its effectiveness (Doctoral dissertation).
   Google Scholar
• Hwang, G. J., Kuo, F. R., Chen, N. S., & Ho, H. J. (2014). Effects of an integrated concept mapping and web-based problem-solving approach on students’ learning achievements, perceptions and cognitive loads. Computers & Education, 71, 77–86.
   Web of Science ®Google Scholar
• Işık, A. (2008). Where’s wrong in our foreign language education? Journal of Language and Linguistic Studies, 4(2), pp–15.
   Google Scholar
• John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and think. International Journal of Advertising, 33(1), 181–183.
   Google Scholar
• Karacı, A. (2014). Intelligent tutoring system model proposal for teaching the use of punctuation marks and capital letters in Turkish. J Res Educ Teach, 3(1), 18–24.
   Google Scholar
• Karasar, N. (2006). Scientific research method. Ankara: Nobel Yayınları.
   Google Scholar
• Kargın, T. (2007). Educational evaluation and individualized education program preparation process. Ankara Üniversitesi Egitim Bilimleri Fakültesi Özel Egitim Dergisi, 8(1), 1–13.
   Google Scholar
• Kaya, S., & Korkmaz, O. (2007). The effects of exceltutor software desing an intelligent tutoring system on learning. Ahi Evran University Journal of Kırşehir Faculty of Education, 8(2), 171–187.
   Google Scholar
• Keleş, A., Ocak, R., Keleş, A., & Gülcü, A. (2009). ZOSMAT: Web-based intelligent tutoring system for the teaching-learning process. Expert Systems with Applications, 36(2), 1229–1239.
   Web of Science ®Google Scholar
• Kırkgöz, Y. (2010). An analysis of written errors of Turkish adult learners of English. Procedia-Social and Behavioral Sciences, 2(2), 4352–4358.
   Google Scholar
• Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885–899.
   Web of Science ®Google Scholar
• Kolb, D. A., Boyatzis, R. E., & Mainemelis, C. (2001). Experiential learning theory: Previous research and new directions. Perspectives on Thinking, Learning, and Cognitive Styles, 1(8), 227–247.
   Google Scholar
• Koru, S., & Akesson, J. (2011). Turkey’s English deficit. Ankara: Economic Policy Research Foundation of Turkey.
   Google Scholar
• Kurilovas, E., & Dagiene, V. (2010). Multiple criteria evaluation of quality and optimisation of e-learning system components. Electronic Journal of e-Learning, 8(2), 141–151.
   Google Scholar
• Latham, A., Crockett, K., McLean, D., & Edmonds, B. (2012). A conversational intelligent tutoring system to automatically predict learning styles. Computers & Education, 59(1), 95–109.
   Web of Science ®Google Scholar
• Liu, P. L., Chen, C. J., & Chang, Y. J. (2010). Effects of a computer-assisted concept mapping learning strategy on EFL college students’ English reading comprehension. Computers & Education, 54(2), 436–445.
   Web of Science ®Google Scholar
• Martin, B., & Mitrovic, A. (2002). Authoring web-based tutoring systems with WETAS. In Computers in Education, 2002. Proceedings. International Conference on (pp. 183–187). Los Alamitos, CA: IEEE Computer Society.
   Google Scholar
• Milrad, M. (2002). Using construction kits, modeling tools and system dynamics simulations to support collaborative discovery learning. Educational Technology & Society, 5, 4.
   Google Scholar
• Minsky, M. (1994). Negative expertise. International Journal of Expert Systems, 7(1), 13–19.
   Google Scholar
• Mitrovic, A., Koedinger, K. R., & Martin, B. (2003). A comparative analysis of cognitive tutoring and constraint-based modeling. In International Conference on User Modeling (pp. 313–322). Berlin, Heidelberg: Springer.
   Google Scholar
• Mitrovic, A., Ohlsson, S., & Barrow, D. K. (2013). The effect of positive feedback in a constraint-based intelligent tutoring system. Computers & Education, 60(1), 264–272.
   Web of Science ®Google Scholar
• Murray, M. C., & Pérez, J. (2015). Informing and performing: A study comparing adaptive learning to traditional learning. Informing Science: the International Journal of an Emerging Transdicipline, 18, 111.
   Google Scholar
• Murray, T. (2003). An overview of intelligent tutoring system authoring tools: Updated analysis of the state of the art. Murray, T., Blessing, S. B., & Ainsworth, S. (Eds.), Authoring tools for advanced technology learning environments (pp. 491–544). Dordrecht: Springer.
   Google Scholar
• Nwana, H. S. (1990). Intelligent tutoring systems: An overview. Artificial Intelligence Review, 4(4), 251–277.
   Web of Science ®Google Scholar
• Ohlsson, S. (1994). Constraint-based student modeling. Greer, J. E., & McCalla, G. I. (Eds.), Student modeling: The key to individualized knowledge-based instruction (pp. 167–189). Berlin, Heidelberg: Springer.
   Google Scholar
• Ohlsson, S. (1996). Learning from performance mistakes. Psychological Review, 103(2), 241.
   Web of Science ®Google Scholar
• Onder, H. H. (2003). The use of computer on distance education and expert systems. The Turkish Online Journal of Educational Technology, 2(3), 142–6.
   Google Scholar
• Parviainen, J., & Eriksson, M. (2006). Negative knowledge, expertise, and organizations. International Journal of Management Concepts and Philosophy, 2(2), 140–153.
   Google Scholar
• Rickel, J. W. (1989). Intelligent computer-aided instruction: A survey organized around system components. IEEE Transactions on Systems, Man, and Cybernetics, 19(1), 40–57.
   Web of Science ®Google Scholar
• Rosic, M., Glavinic, V., & Stankov, S. (2006). Intelligent tutoring systems for the new learning infrastructure. Intelligent Learning Infrastructure for Knowledge Intensive Organizations: A Semantic Web Perspective (pp. 225–250). doi:10.1093/alcalc/agl002
   Google Scholar
• Salvia, J., & Ysseldyke, E. J. (2001). Assessment (8th ed.). Boston: Houghton Mifflin.
   Google Scholar
• Shams, L., & Seitz, A. R. (2008). Benefits of multisensory learning. Trends in Cognitive Sciences, 12(11), 411–417.
   PubMed Web of Science ®Google Scholar
• Šimić, G., Gašević, D., & Devedžić, V. (2004). Semantic web and intelligent learning management systems. In Workshop on Applications of Semantic Web Technologies for e-Learning.Amsterdam, The Netherlands.
   Google Scholar
• Tauber, Y. (2009). The elusive horse. Retrieved from http://www.chabad.org/library/article_cdo/aid/63025/jewish/The-Elusive-Horse.htm
   Google Scholar
• Turnbull, A. P. (1995). Exceptional lives: Special education in today’s schools. Order Department, 200 Old Tappan Rd., Old Tappan, NJ 07675: Merrill/Prentice Hall.
   Google Scholar
• Virvou, M., & Alepis, E. (2005). Mobile educational features in authoring tools for personalized tutoring. Computers & Education, 44(1), 53–68.
   Web of Science ®Google Scholar
• Vosniadou, S., Ioannides, C., Dimitrakopoulou, A., & Papademetriou, E. (2001). Designing learning environments to promote conceptual change in science. Learning and Instruction, 11(4–5), 381–419.
   Web of Science ®Google Scholar
• Wang, D., Han, H., Zhan, Z., Xu, J., Liu, Q., & Ren, G. (2015). A problem solving oriented intelligent tutoring system to improve students’ acquisition of basic computer skills. Computers & Education, 81, 102–112.
   Web of Science ®Google Scholar
• Wenger, E. (2014). Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge. California, CA: Morgan Kaufmann Publishers.
   Google Scholar
• Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. California, CA: Morgan Kaufmann Publishers.
   Google Scholar
• Zhiping, L., Yu, S., Tianwei, X., & Yang, L. (2012). The research of classical learner models in intelligent tutoring systems. In Computer Science & Education (ICCSE), 2012 7th International Conference on (pp. 1204–1207). Melbourne, Australia.
   Google Scholar