Abstract
Multimodal learning, as an effective method for helping students to understand complex concepts, has attracted much research interest recently. Using more than one media in the learning process typically makes the study material easier to grasp. In the current study, students annotate linguistic and visual elements in multimodal texts by using geometric shapes and assigning attributes. However, how to effectively evaluate student performance is a challenge. This work proposes to make use of a vector space model to process student-generated multimodal data, with a view to evaluating student performance based on the annotation data. The vector model consists of fuzzy membership functions to model the performance in the various annotation criteria. These vectors are then used as the input to a multi-criteria ranking framework to rank the students.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amelung, M., Krieger, K., Rosner, D.: E-assessment as a service. IEEE Transactions on Learning Technologies 4(2), 162–174 (2011)
Basu, S., Yu, Y., Zimmermann, R.: Student performance evaluation of multimodal learning via a vector space model. In: Proceedings of the 1st ACM International Workshop on Internet-Scale Multimedia Management, ISMM 2014. ACM (2014)
Bhardwaj, B., Paul, S.: Mining Educational Data to Analyze Students’ Performance. International Journal of Advanced Computer Science and Applications 2(6) (2011)
Deng, H.: A similarity-based approach to ranking multicriteria alternatives. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS (LNAI), vol. 4682, pp. 253–262. Springer, Heidelberg (2007)
Friedland, G., HĂ¼rst, W., Knipping, L.: Educational multimedia systems: The past, the present, and a glimpse into the future. In: Proceedings of the International Workshop on Educational Multimedia and Multimedia Education, Emme 2007, pp. 1–4. ACM, New York (2007)
Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: International Conference on Management of Data, pp. 47–57. ACM (1984)
O’Halloran, K.L., Podlasov, A., Chua, A., Marissa, K.L.E.: Interactive Software for Multimodal Analysis. Visual Communication 11, 363–381 (2012)
Phua, Y.C.J., Chew, L.C.: What Do Secondary School Students Think About Multimedia Science Computer Assisted Assessment (CAA). In: Computer Assisted Assessment (2012)
Timmis, P.B.S., Oldfield, A., Sutherland, R.: Where is the cutting edge of research in e-Assessment? Exploring the landscape and potential for wider transformation. In: Computer Assisted Assessment (2012)
Smith, R.: An overview of the tesseract ocr engine. In: Proceedings of the Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, vol. 02, pp. 629–633. IEEE Computer Society, Washington, DC (2007)
Worsley, M.: Multimodal Learning Analytics - Enabling the Future of Learning through Multimodal Data Analysis and Interfaces. In: ICMI 2012, pp. 353–356 (2012)
Yoon, K., Hwang, C.: Multiple attribute decision making: an introduction. In: Quantitative Applications in the Social Sciences, pp. 102–104. Sage Publications (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Basu, S., Zimmermann, R., O’Halloran, K.L., Tan, S., K.L.E., M. (2015). Performance Evaluation of Students Using Multimodal Learning Systems. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8936. Springer, Cham. https://doi.org/10.1007/978-3-319-14442-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-14442-9_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14441-2
Online ISBN: 978-3-319-14442-9
eBook Packages: Computer ScienceComputer Science (R0)