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2017 | OriginalPaper | Buchkapitel

12. Experimental Evaluation of Protus 2.1

verfasst von : Aleksandra Klašnja-Milićević, Boban Vesin, Mirjana Ivanović, Zoran Budimac, Lakhmi C. Jain

Erschienen in: E-Learning Systems

Verlag: Springer International Publishing

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Abstract

Implemented Protus 2.1 for Java programming language has been used in real-life educational environments. The experiments were realized on an educational dataset, consisting of 440 learners, 3rd year undergraduate students of the Department of Information technology at Higher School of Professional Business Studies, University of Novi Sad. The experiment lasted for two semesters. Involved learners were programming beginners that successfully passed the basic computer literacy course at previous semester. They were divided into two groups: the experimental group and the control group. Learners of the control group learned with the previous version of the system and did not receive any recommendation or guidance through the course, while the learners of the experimental group were required to use Protus 2.1 system. Learners from both groups did not take any parallel traditional course and they were required not to use any additional material or help. This chapter highlights the results of the evaluation and discussion of analysis of the results regarding the validity of the tutoring system presented in the previous chapters.

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Vorheriges Kapitel Personalization in Protus 2.1 System

Nächstes Kapitel Conclusions and Future Directions

Bateman, S., Brooks, C., McCalla, G., & Brusilovsky, P. (2007). Applying collaborative tagging to e-learning. WWW, 1–7. http://doi.org/10.1.1.64.8892.

Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach’s alpha. Bmj, 314(7080), 572.CrossRef

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology. http://doi.org/10.1037/0021-9010.78.1.98.

DeVellis, R. F. (1991). Guidelines in scale development. In Scale Development (pp. 51–60).

Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the goal, user model and conditions of recommender systems for formal and informal learning. Journal of Digital Information, 10(2).

Felder, R. M. (2005). A study of the reliability and validity of the Felder-Soloman index of learning styles. Engineering Education, 113, 77. http://doi.org/10.1109/IIAI-AAI.2015.284.

Friesen, N. (2001). What are educational objects? Interactive Learning Environments, 9(3), 219–230.CrossRef

Glaser, R. (1963). Instructional technology and the measurement of learing outcomes: Some questions. American Psychologist, 18(8), 519.CrossRef

Golder, S. A., & Huberman, B. A. (2006). The structure of collaborative tagging systems. Journal of Information Science, 32(2), 198–208.CrossRef

Halpin, H., Robu, V., & Shepherd, H. (2007). The complex dynamics of collaborative tagging. In Proceedings of the 16th International Conference on World Wide Web (pp. 211–220).

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. In: ACM Transactions on Information Systems. http://doi.org/10.1145/963770.963772.

Hwang, G.-J., Tsai, P.-S., Tsai, C.-C., & Tseng, J. C. R. (2008). A novel approach for assisting teachers in analyzing student web-searching behaviors. Computers & Education, 51(2), 926–938.CrossRef

Jones, N., Macasek, M., Walonoski, J., Rasmussen, K., & Heffernan, N. (2006). Common tutor object platform–an e-learning software development strategy. In Proceedings of the 15th International Conference on World Wide Web, Edinburgh (pp. 307–316). Scotland.

Nunnally, J. C., Bernstein, I. H., & Berge, J. M. F. T. (1967). Psychometric theory (Vol. 226). JSTOR.

Rendle, S., Balby Marinho, L., Nanopoulos, A., & Schmidt-Thieme, L. (2009). Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 727–736).

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (Vol. 1, pp. 285–295). http://doi.org/10.1145/371920.372071.

Sen, S., Lam, S. K., Rashid, A. M., Cosley, D., Frankowski, D., Osterhouse, J.,& Riedl, J. (2006). Tagging, communities, vocabulary, evolution. In Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work (pp. 181–190).

Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2010). A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. Knowledge and Data Engineering, IEEE Transactions on, 22(2), 179–192.CrossRef

Symeonidis, P., Ruxanda, M. M., Nanopoulos, A., & Manolopoulos, Y. (2008). Ternary semantic analysis of social tags for personalized music recommendation. In ISMIR (Vol. 8, pp. 219–224).

Vesin, B., Ivanović, M., & Budimac, Z. (2009). Learning management system for programming in java. Annales Universitatis Scientiarum De Rolando Eötvös Nominatae, Sectio-Computatorica, 31, 75–92.

Wetzker, R., Zimmermann, C., Bauckhage, C., & Albayrak, S. (2010). I tag, you tag: Translating tags for advanced user models. In Proceedings of the Third ACM International Conference on Web Search and Data Mining (pp. 71–80).

Titel: Experimental Evaluation of Protus 2.1
verfasst von: Aleksandra Klašnja-Milićević
Boban Vesin
Mirjana Ivanović
Zoran Budimac
Lakhmi C. Jain
Verlag: Springer International Publishing
Buch: E-Learning Systems
Print ISBN: 978-3-319-41161-3

Electronic ISBN: 978-3-319-41163-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-41163-7_12

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