Metrika članka

  • citati u SCindeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[=>]
  • posete u prethodnih 30 dana:16
  • preuzimanja u prethodnih 30 dana:7
članak: 1 od 1  
International Journal of Cognitive Research in Science, Engineering and Education / IJCRSEE
2018, vol. 6, br. 1, str. 45-52
jezik rada: engleski
vrsta rada: originalan članak

Creative Commons License 4.0
Cognitive dialog games as cognitive assistants: Tracking and adapting knowledge and interactions in student's dialogs
(naslov ne postoji na srpskom)
aBahcesehir University, Engineering Faculty, Bahcesehir, Turkey
bInstitute of High Performance Computing, A*STAR, Turkey

e-adresa:, yengini@ihpc.a-star.


(ne postoji na srpskom)
This study introduces a system design in a form of cognitive dialog game (DiaCog) to support pedagogical factors and student learning model ideas. The purpose of the study is to describe how such a design achieves tracking and adapting students' knowledge and mastery learning levels as a cognitive assistant. Also, this study shows alternative ways for supporting intelligent personal learning, tutoring systems, and MOOCS. This paper explains method called DiaCog that uses structure for students' thinking in an online dialog by tracking student's level of learning/knowledge status. The methodology of computing is the semantic that match between students' interactions in a dialog. By this way it informs DiaCog's learner model to inform the pedagogical model. Semantic fingerprint matching method of DiaCog allows making comparisons with expert knowledge to detect students' mastery levels in learning. The paper concludes with the DiaCog tool and methodologies that used for intelligent cognitive assistant design to implement pedagogical and learner model to track and adapt students' learning. Finally, this paper discusses future improvements and planned experimental set up to advance the techniques introduced in DiaCog design.

Ključne reči

student modeling; cognitive modeling; cognitive assistants; cognitive dialog game; MOOCS


Bain, Y.C. (2011) Learning through online discussion: a framework evidenced in learners' interactions. Research in Learning Technology, 19(sup1): 7779
Bernsen, N.O., Dybkjær, H., Dybkjær, L. (2012) Designing interactive speech systems: From first ideas to user testing. Springer Science and Business Media
Bull, S. (2004) Supporting learning with open learner models. Planning, 29(14), 1. links/0c96052b4861a5d449000000/Supporting-learning-with-open-learner-m
Chi, M.T.H. (2009) Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities. Topics in Cognitive Science, 1(1): 73-105
Conati, C., Gertner, A., Vanlehn, K. (2002) Using Bayesian networks to manage uncertainty in student modelin. User Modeling and User-Adapted Interaction, 12(4): 371-417
Core, M.G., Moore, J.D., Zinn, C. (2003) The role of initiative in tutorial dialogue. u: Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - EACL '03, Morristown, NJ, USA: Association for Computational Linguistics (ACL), str. 67
Corticalio (2015) Sparse distributed representations.
Embretson, S.E., Reise, S.P. (2013) Item response theory. Psychology Press
Ezen-Can, A., Boyer, K.E., Kellogg, S., Booth, S. (2015) Unsupervised modeling for understanding MOOC discussion forums. u: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge - LAK '15, New York, New York, USA: Association for Computing Machinery (ACM), str. 146-150
Fereday, J., Muir-Cochrane, E. (2006) Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods, 5(1): 80-92
Forbes-Riley, K., Litman, D.J., Purandare, A., Rotaru, M., Tetreault, J. (2007) Comparing linguistic features for modeling learning in computer tutoring. Aied, June, (Vol. 158, pp. 270-277). ios2-final.pdf
Gagne, M., Deci, E.L. (2005) Self-determination theory and work motivation. Journal of Organizational Behavior, 26(4): 331
Hawkey, K. (2003) Social constructivism and asynchronous text-based discussion: A case study with trainee teachers. Education and Information Technologies, 8(2): 165-177
Huang, Y., Liang, T. (2014) A technique for tracking the reading rate to identify the e-book reading behaviors and comprehension outcomes of elementary school students. British Journal of Educational Technology, 46(4): 864-876
Johnson, A., Taatgen, N. (2005) User modeling. u: The handbook of human factors in web design, 424- 438.
Kolb, D.A. (1985) Learning-style inventory: Self-scoring inventory and interpretation book-let. Boston: McBer and Co, 2nd ed
Kop, R., Fournier, H., Mak, J.S.F. (2011) A pedagogy of abundance or a pedagogy to support human beings? Participant support on massive open online courses. International Review of Research in Open and Distributed Learning, 12(7): 74
Lee, Y., Sawaki, Y. (2009) Cognitive Diagnosis and Q-Matrices in Language Assessment. Language Assessment Quarterly, 6(3): 169-171
Li, N., Cohen, W.W., Koedinger, K.R., Matsuda, N. (2011) A machine learning approach for automatic student model discovery. EDM, July, (pp. 31-40). ED537453.pdf
Nkuyubwatsi, B. (2013) Evaluation of massive open online courses (MOOCs) from the learner’s perspective. u: European Conference on e-Learning, Academic Conferences International Limited, October, (p. 340). https://search.proquest. com/openview/2616f896b4618dba39f72b248bd6d47a/1?pq-origsite=gscholar and cbl=1796419
Palmer, S., Holt, D., Bray, S. (2008) Does the discussion help? The impact of a formally assessed online discussion on final student results. British Journal of Educational Technology, 39(5): 847-858
Prylipko, D., Rösner, D., Siegert, I., Günther, S., Friesen, R., Haase, M., Vlasenko, B., Wendemuth, A. (2014) Analysis of significant dialog events in realistic human-computer interaction. Journal on Multimodal User Interfaces, 8(1): 75-86
Rosé, C.P., Bhembe, D., Siler, S., Srivastava, R., VanLehn, K. (2003) The role of why questions in effective human tutoring. u: The 11th International Conference on AI in Education, Proceedings of, (pp. 55-62).
Rosenberg, M., Burkert, A. (2015) Learning styles and their effect on learning and teaching. u: Forschende Fachdidaktik, 103-130.
Rotaru, M., Litman, D.J. (2006) Exploiting discourse structure for spoken dialogue performance analysis. u: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06, Morristown, NJ, USA: Association for Computational Linguistics (ACL), str. 85
Sardareh, S.A., Aghabozorgi, S., Dutt, A. (2014) Reflective dialogues and students' problem solving ability analysis using clustering. u: The 3rd International Conference on Computer Engineering and Mathematical Sciences (ICCEMS 2014), 04-05 Dec 2014, Langkawi, Malaysia, Proceedings of,
Soloman, B.A., Felder, R.M. (2014) Index of learning styles questionnaire. North Carolina State University, [Internet].
Sottilare, R.A., Graesser, A., Hu, X., Holden, H., Eds. (2013) Design recommendations for intelligent tutoring systems: Volume 1-learner modeling. US Army Research Laboratory. https: //goo. gl/8HGjp4, Vol. 1
Thomas, M.J.W. (2002) Learning within incoherent structures: the space of online discussion forums. Journal of Computer Assisted Learning, 18(3): 351-366
Vail, A.K., Boyer, K.E. (2014) Identifying Effective Moves in Tutoring: On the Refinement of Dialogue Act Annotation Schemes. u: Hutchison, David; Kanade, Takeo; Kittler, Josef; Kleinberg, Jon M.; Kobsa, Alfred; Mattern, Friedemann; Mitchell, John C.; Naor, Moni; Nierstrasz, Oscar; Pandu Rangan, C.; Steffen, Bernhard; Terzopoul [ur.] In International Conference on Intelligent Tutoring Syste, Cham: Springer Nature, str. 199-209
Webb, G.I., Pazzani, M.J., Billsus, D. (2001) Machine learning for user modeling. User Modeling and User-Adapted Interaction, 11(1/2): 19-29
Yengin, I., Lazarevic, B. (2014) The DiaCog: A prototype tool for visualizing online dialog games' interactions. Research in Higher Education Journal, 25.
Zapata, M. (2010) Estrategias de evaluación de competencias en entornos virtuales de aprendi-zaje. RED: Revista de Educación a Distancia, Sección de Docencia Universitaria en la Sociedad del Conocimiento. Número 1. Consultado