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Education and Information Technologies

, Volume 24, Issue 1, pp 711–741 | Cite as

Knowledge tracing with an intelligent agent, in an e-learning platform

  • Amal TrifaEmail author
  • Aroua Hedhili
  • Wided Lejouad Chaari
Article

Abstract

E-learning systems have gained nowadays a large student community due to the facility of use and the integration of one-to-one service. Indeed, the personalization of the learning process for every user is needed to increase the student satisfaction and learning efficiency. Nevertheless, the number of students who give up their learning process cannot be neglected. Therefore, it is mandatory to establish an efficient way to assess the level of personalization in such systems. In fact, assessing represents the evolution’s key in every personalized application and especially for the e-learning systems. Besides, when the e-learning system can decipher the student personality, the student learning process will be stabilized, and the dropout rate will be decreased. In this context, we propose to evaluate the personalization process in an e-learning platform using an intelligent referential system based on agents. It evaluates any recommendation made by the e-learning platform based on a comparison. We compare the personalized service of the e-learning system and those provided by our referential system. Therefore, our purpose consists in increasing the efficiency of the proposed system to obtain a significant assessment result; precisely, the aim is to improve the outcomes of every algorithm used in each defined agent. This paper deals with the intelligent agent ‘Mod-Knowledge’ responsible for analyzing the student interaction to trace the student knowledge state. The originality of this agent is that it treats the external and the internal student interactions using machine learning algorithms to obtain a complete view of the student knowledge state. The validation of this contribution is done with experiments showing that the proposed algorithms outperform the existing ones.

Keywords

Personalization Knowledge analysis Student learning Data mining 

Abbreviations

RPMAS

Referential Personalized Multi Agent Systems

SOSC

Stack Overflow for Semantic Correlation

MAE

Mean Absolute Error

KT

Knowledge Tracing

ML

Machine Learning

BKT

Bayesian Knowledge Tracing

DKT

Deep Knowledge tracing

LSTM

Long Short-Term Memory

PFA

Performance Factor Analysis

DKVMN

Dynamic Key-Value Memory Net

AIDKVMN

Augmented Input Dynamic Key-Value Memory Net

LO

Learning Object

NLP

Natural Language Processing

IT

Information Technology

RCNN

Reccurent Convolutional Neural Network

CNN

Convolutional Neural Network

SOCSW

Stack Overflow for Semantic Correlation With verbs

AUC

Area Under the Curve

Notes

References

  1. Bollegala, D., Matsuo, Y., Ishizuka, M. (2011). A web search engine-based approach to measure semantic similarity between words. IEEE Transactions on Knowledge and Data Engineering, 23(7), 977–990.CrossRefGoogle Scholar
  2. Bourkoukou, O., El Bachari, E., El Adnani, M. (2016). A personalized e-learning based on recommender system. International Journal of Learning and Teaching, 2(2), 99–103.Google Scholar
  3. Church, K. W., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.Google Scholar
  4. Cilibrasi, R.L., & Vitanyi, P.M.B. (2007). The google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370–383.CrossRefGoogle Scholar
  5. Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4 (4), 253–278.CrossRefGoogle Scholar
  6. d Baker, R. S. J., Corbett, A. T., Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In International conference on intelligent tutoring systems. (pp. 406–415). Springer.Google Scholar
  7. Eirinaki, M., & Vazirgiannis, M. (2003). Web mining for web personalization. ACM Transactions on Internet Technology (TOIT), 3(1), 1–27.CrossRefGoogle Scholar
  8. Gong, Y., Beck, J. E., Heffernan, N. T. (2010). Comparing knowledge tracing and performance factor analysis by using multiple model fitting procedures. In International conference on intelligent tutoring systems (pp. 35–44). Springer.Google Scholar
  9. Guo, Q., & Zhang, M. (2009). Implement web learning environment based on data mining. Knowledge-Based Systems, 22(6), 439–442.CrossRefGoogle Scholar
  10. Islam, A., Milios, E. E., Keselj, V. (2012). Text similarity using google tri-grams. In Canadian conference on AI (Vol. 7310, pp. 312–317). Springer.Google Scholar
  11. Jonassen, D. H., & Grabowski, B. L. (2012). Handbook of individual differences, learning, and instruction. New York: Routledge.Google Scholar
  12. Kerns, J. G., Cohen, J. D., MacDonald, A. W., Cho, R. Y., Stenger, V. A., Carter, C. S. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science, 303(5660), 1023–1026.CrossRefGoogle Scholar
  13. Khamparia, A., & Pandey, B. (2015). Knowledge and intelligent computing methods in e-learning. International Journal of Technology Enhanced Learning, 7(3), 221–242.CrossRefGoogle Scholar
  14. Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv:http://arXiv.org/abs/1408.5882.
  15. Lai, S., Xu, L., Liu, K., Zhao, J. (2015). Recurrent convolutional neural networks for text classification. In AAAI (Vol. 333, pp. 2267–2273).Google Scholar
  16. Lin, D. (1998). Others an information-theoretic definition of similarity. In Icml (Vol. 98, pp. 296–304).Google Scholar
  17. Liu, B., Feng, J., Liu, M., Liu, F., Wang, X., Li, P. (2014). Computing semantic relatedness using a word-text mutual guidance model. In Natural language processing and Chinese computing (pp. 67–78). Springer.Google Scholar
  18. Pascanu, R., Mikolov, T., Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In International conference on machine learning (pp. 1310–1318).Google Scholar
  19. Pavlik, Jr, P. I., Cen, H., Koedinger, K. R. (2009). Performance factors analysis–a new alternative to knowledge tracing. Online Submission.Google Scholar
  20. Pekrun, R., Elliot, A. J., Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101(1), 115.CrossRefGoogle Scholar
  21. Pérez Martínez, C., López Morteo, G., Martínez Reyes, M., Gelbukh, A. (2015). Wikipedia-based learning path generation. Computación y Sistemas, 19 (3), 589–600.CrossRefGoogle Scholar
  22. Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., Sohl-Dickstein, J. (2015). Deep knowledge tracing. In Advances in neural information processing systems (pp. 505–513).Google Scholar
  23. Salonen, V., & Karjaluoto, H. (2016). Web personalization: the state of the art and future avenues for research and practice. Telematics and Informatics, 33(4), 1088–1104.CrossRefGoogle Scholar
  24. Senach, B. (1990). Evaluation ergonomique des interfaces homme-machine: une revue de la littėrature. PhD thesis, INRIA.Google Scholar
  25. Trifa, A., Sbai, A. H., Chaari, W. L. (2017a). Enhancing assessment of personalized multi-agent system through convLSTM. Procedia Computer Science, 112, 249–259.Google Scholar
  26. Trifa, A., Sbai, A. H., Chaari, W. L. (2017b). Evaluate a personalized multi agent system through social networks: web scraping. In 2017 IEEE 26th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE) (pp. 18–20). IEEE.Google Scholar
  27. Tu̇rker, A., Gȯrgu̇n, B., Conlan, O. (2006). The challenge of content creation to facilitate personalized e-learning experiences. International Journal on ELearning, 5(1), 11.Google Scholar
  28. Wu, Z., & Palmer, M. (1994). Verbs semantics and lexical selection . In Proceedings of the 32nd annual meeting on association for computational linguistics (pp. 133–138). Association for Computational Linguistics.Google Scholar
  29. Xiong, X., Zhao, S., Van Inwegen, E., Beck, J. (2016). Going deeper with deep knowledge tracing. In EDM (pp. 545–550).Google Scholar
  30. Zack, M. H. (2009). Knowledge and strategy. New York: Routledge.CrossRefGoogle Scholar
  31. Zapata, A., Menėndez, V. H., Prieto, M. E., Romero, C. (2015). Evaluation and selection of group recommendation strategies for collaborative searching of learning objects. International Journal of Human-Computer Studies, 76, 22–39.CrossRefGoogle Scholar
  32. Zhang, J., Shi, X., King, I., Yeung, D. -Y. (2017). Dynamic key-value memory networks for knowledge tracing. In Proceedings of the 26th international conference on World Wide Web (pp. 765–774). International World Wide Web Conferences Steering Committee.Google Scholar
  33. Zheng, Y., Li, L., Zheng, F. (2010). An analysis framework of activity context in e-learning environments. Entertainment for Education. Digital Techniques and Systems, 6249, 258–268.Google Scholar
  34. Zhuhadar, L., & Nasraoui, O. (2008). Semantic information retrieval for personalized e-learning. In 2008. ICTAI’08. 20th IEEE international conference on tools with artificial intelligence (Vol. 1, pp. 364–368). IEEE.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Artificial Intelligence, Cosmos LabNational School of Computer SciencesManoubaTunisia

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