Towards Designing an Intelligent Educational Assessment Tool

Abstract

Assessment is an important part of learning process. It can be defined as the process of gathering information for the purpose of making judgments about a current state of affairs presumably for the purpose of enhancing future outcomes [1]. It determines whether or not the goals of education are being met. Typically, most assessment tools give a numerical score as the result of the assessment. This may not be enough to improve the student’s progress. In this paper we defined main problems in current assessment tools and proposed a new assessment model that uses notions in knowledge space theory to overcome the shortage of the current assessment models. The experiment result showed that this new prototype made the assessment process easier and more effective. However, assessment affects decisions about grades, instructional needs and curriculum. This is an important phase of the learning process being showed in this paper in knowledge states framework. Future research will focus on making the tool behave intelligently to improve students’ learning momentum.

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Hamtini, T. , Albasha, S. and Varoca, M. (2015) Towards Designing an Intelligent Educational Assessment Tool. Journal of Software Engineering and Applications, 8, 35-42. doi: 10.4236/jsea.2015.82005.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Glaser, R., Chudowsky, N. and Pellegrino, J.W., Eds. (2001) Knowing What Students Know. The Science and Design of Educational Assessment. National Academies Press, Washington DC.
[2] Camacho, D., Ortigosa, A., Pulido, E. and RMoreno, M. (2008) AI Techniques for Monitoring Student Learning Process. In: Garcia-Peñalbo, F.J., Ed., Advances in E-Learning: Experiences and Meth-odologies, Information Science Reference-IGI Global, Hershey, 149-172.
[3] Falmagne, J.-C. and Doignon, J.-P. (2011) Learning Spaces. Springer-Verlag, Berlin.
http://dx.doi.org/10.1007/978-3-642-01039-2
[4] Gouli, E., Gogoulou, A., Papanikolaou, K. and Grigoriadou, M. (2004) Compass: An Adaptive Web-Based Concept Map Assessment Tool. Proceedings of the 1st International Conference on Concept Mapping, Pamplona, 14-17 September 2004, 295-302.
[5] Scalise, K., Bernbaum, D.J., Timms, M., Harrell, S.V., Burmester, K., Kennedy, C.A. and Wilson, M. (2007) Adaptive Technology for E-Learning: Principles and Case Studies of an Emerging Field. Journal of the American Society for Information Science and Technology, 58, 2295-2309.
http://dx.doi.org/10.1002/asi.20701
[6] Clements, D.H. and Sarama, J. (2004) Learning Trajectories in Mathematics Education. Mathematical Thinking and Learning, 6, 81-89. http://dx.doi.org/10.1207/s15327833mtl0602_1
[7] Albert, D. and Lukas, J., Eds. (1999) Knowledge Spaces: Theories, Empirical Research, and Appli-cations. Psychology Press, London.
[8] Tatsuoka, K.K. (1991) Boolean Algebra Applied to Determination of Universal Set of Knowledge States. ETS Research Report Series, 1991, i-36.
http://dx.doi.org/10.1002/j.2333-8504.1991.tb01411.x
[9] Gediga, G. and Düntsch, I. (2002) Skill Set Analysis in Knowledge Structures. British Journal of Mathematical and Statistical Psychology, 55, 361-384.
http://dx.doi.org/10.1348/000711002760554516
[10] Heller, J., Steiner, C., Hockemeyer, C. and Albert, D. (2006) Competence-Based Knowledge Stru-ctures for Personalised Learning. International Journal on E-Learning, 5, 75-88.
[11] Falmagne, J.C., Cosyn, E., Doignon, J.P. and Thiéry, N. (2006) The Assessment of Knowledge, in Theory and in Practice. In: Missaoui, R. and Schmidt, J., Eds., Formal Concept Analysis, Springer, Heidelberg, 61-79. http://dx.doi.org/10.1007/11671404_4
[12] Anghel, C., Godja, C., Dinsoreanu, M. and Salomie, I. (2003) JADE Based Solutions for Knowledge Assessment in E-Learning Environments. University of Limerick, Limerick.
[13] Stahl, C. (2011) Knowledge Space Theory.
http://cran.r-project.org/web/packages/kst/vignettes/kst.pdf
[14] Albert, D. and Hockemeyer, C. (1997) Adaptive and Dynamic Hypertext Tutoring Systems Based on Knowledge Space Theory. In: du Boulay, B. and Mizoguchi, R., Eds., Artificial Intelligence in Education: Knowledge and Media in Learning Systems, IOS Press, Amsterdam, 553-555.
[15] Nwaogu, E. (2012) The Effect of Aleks on Students’ Mathematics Achievement in an Online Learning Environment and the Cognitive Complexity of the Initial and Final Assessments. Ph.D. Thesis, Georgia State University, Atlanta.

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