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International Conference on AI and Mobile Services

AIMS 2020: Artificial Intelligence and Mobile Services – AIMS 2020 pp 45–59Cite as

A Novel Method to Estimate Students’ Knowledge Assessment

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12401))

Abstract

Performance of learning can be enriched with proper and timely feedback. This paper proposes a solution based on a Bayesian network in machine learning that can examine and judge students’ written response to identify evidences that students fully comprehend concepts being considered in a certain knowledge domain. In particular, it can estimate probabilities that a student has known concepts in computer science at different cognitive ability levels in a sense. Thus, the method can offer learners personalized feedbacks on their strengths and shortcomings, as well as advising them and instructors of supplementary education actions that may help students to resolve any lacks to improve their knowledge and exam score.

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References

  1. Novak, J.D., Cañas, A.J.: The theory underlying concept maps and how to construct and use them, Technical report IHMC CmapTools 2006-01 Rev 2008-01, Florida Institute for Human and Machine Cognition, USA, February 2008

    Google Scholar 

  2. Anderson, L.W., et al.: A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives, USA (2001)

    Google Scholar 

  3. Khan, J.I., Hardas, M.S, Ma, Y.: A study of problem difficulty evaluation for semantic network ontology based intelligent courseware sharing. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 426–429 (2005)

    Google Scholar 

  4. Khan, J.I., Hardas, M.S.: Does sequence of presentation matter in reading comprehension? A model based analysis of semantic concept network growth during reading. In: IEEE Seventh International Conference on Semantic Computing (ICSC), 16–18 September 2013, pp. 444–452 (2013)

    Google Scholar 

  5. Khan J.I., Yongbin M., Hardas M.: Course composition based on semantic topical dependency. In: International Conference on Web Intelligence, pp. 502–505, December 2006

    Google Scholar 

  6. Falmagne, J.C., Cosyn, E., Doignon, J.-P., Thiery, N.: The assessment of knowledge, in theory and in practice. In: International Conference on Integration of Knowledge Intensive Multi-Agent Systems, pp. 609–615 (2003)

    Google Scholar 

  7. Falmagne, J.C., Doignon, J.P.: Knowledge Spaces. Springer, Berlin (1999). https://doi.org/10.1007/978-3-642-58625-5

    Book  MATH  Google Scholar 

  8. Dowling, C.E., Hockemeyer, C., Ludwig, A.H.: Adaptive assessment and training using the neighbourhood of knowledge states. In: Frasson, C., Gauthier, G., Lesgold, A. (eds.) ITS 1996. LNCS, vol. 1086, pp. 578–586. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61327-7_157

    Chapter  Google Scholar 

  9. Vodovozov, V., Raud, Z.: Concept maps for teaching, learning, and assessment in electronics. Educ. Res. Int. J. 2015 (2015)

    Google Scholar 

  10. Shieh, J.C., Yang, Y.T.: Concept maps construction based on student-problem chart. In: Proceedings of the IIAI 3rd International Conference on Advanced Applied Informatics (IIAIAAI 2014), Kokura Kita-ku, Japan 2014, pp. 324–327 (2014)

    Google Scholar 

  11. Rudraraju, R., Najim, L., Gurupur, V.P., Tanik, M.M.: A learning environment based on knowledge storage and retrieval using concept maps. In: Proceedings of the IEEE Southeastcon 2014, Lexington, Ky, USA, March 2014, pp. 1–6 (2014)

    Google Scholar 

  12. Yusuf, K., Alev, M., Tolga, E.: Graph-based concept discovery in multi relational data. In: 6th International Conference - Cloud System and Big Data Engineering, India (2016)

    Google Scholar 

  13. Bayes network. http://homepages.wmich.edu/~mcgrew/Bayes8.pdf

  14. Directed acyclic graph. https://en.wikipedia.org/wiki/Directed_acyclic_graph

  15. Chain rule. https://en.wikipedia.org/wiki/Chain_rule_%28probability%29

  16. Ontology. https://en.wikipedia.org/wiki/Ontology

  17. Fatema, N., Khan Javed, I.: Discovering hidden cognitive skill dependencies between knowledge units using Markov cognitive knowledge state network (MCKSN). PhD dissertation (2018)

    Google Scholar 

  18. Rania, A., Khan, J.: Model of learning assessment to measure student learning: inferring of concept state of cognitive skill level in concept space. In: 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) (2016)

    Google Scholar 

  19. Wordnet. https://en.wikipedia.org/wiki/WordNet

  20. Conditional probability. https://en.wikipedia.org/wiki/Independence_(probability_theory)

  21. Rania, A., Khan, J.: Computation of assessing the knowledge in one domain by using cognitive skills levels. Int. J. Comput. Appl. 180(12) (2018)

    Google Scholar 

  22. Rania, A., Khan, J.: Are we asking the right questions to grade our students in a knowledge-state space analysis. In: IEEE 8th International Conference on Technology for Education (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Kent State University, Kent, OH, USA

    Phuoc Hung Pham & Javed I. Khan

Authors
  1. Phuoc Hung Pham
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  2. Javed I. Khan
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Corresponding author

Correspondence to Javed I. Khan .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Ruifeng Xu

  2. Sunmi US Inc., Pleasanton, CA, USA

    Wang De

  3. University of South Carolina Upstate, Spartanburg, SC, USA

    Wei Zhong

  4. University of Electronic Science and Technology of China, Chengdu, China

    Ling Tian

  5. Eastern Michigan University, Ypsilanti, MI, USA

    Yongsheng Bai

  6. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

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Cite this paper

Pham, P.H., Khan, J.I. (2020). A Novel Method to Estimate Students’ Knowledge Assessment. In: Xu, R., De, W., Zhong, W., Tian, L., Bai, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2020. AIMS 2020. Lecture Notes in Computer Science(), vol 12401. Springer, Cham. https://doi.org/10.1007/978-3-030-59605-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-59605-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59604-0

  • Online ISBN: 978-3-030-59605-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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