Prediction of Success or Failure for Examination using Nearest Neighbor Method to the Trend of Weekly Online Testing
Abstract
Using trends of estimated abilities in terms of item response theory for online testing, we can predict success/failure for term-end examinations for each student at early stages in courses. We applied the newly developed nearest neighbor method for determining the similarity of learning skills in the trends of estimated abilities, resulting in better prediction accuracy for success or failure. This paper shows that the use of the learning analytics incorporating trends for abilities is effective. ROC curve and recall precision curve are also utilized in the proposed method.
References
R. de Ayala, The Theory and Practice of Item Response Theory. Guilford Press, 2009.
R.S.J.D. Baker, Data mining for education. In B. McGaw, P. Peterson, E. Baker, (Eds.) International Encyclopedia of Education (3rd edition), Elsevier, 2010.
R.S.J.D. Baker, K. Yacef, The State of Educational Data Mining in 2009: A Review and Future Visions, Journal of Educational Data Mining, 1(1), 2009. pp.1-16.
J.P. Egan, Signal detection theory and ROC analysis, Series in Cognition and Perception. Academic Press, New York, 1975.
N. Elouazizi, Critical Factors in Data Governance for Learning Analytics, Journal of Learning Analytics, 1, 2014, pp. 211-222.
D. Gasevic, S. Dawson, and G. Siemens, Let’s not forget: Learning analytics are about learning, TechTrends, 59, 2015, pp. 64-71.
R. Hambleton, H. Swaminathan, and H. J. Rogers, Fundamentals of Item Response Theory. Sage Publications, 1991.
H. Hirose, Y. Soejima, K. Hirose, NNRMLR: A Combined Method of Nearest Neighbor Regression and Multiple Linear Regression, 6th International Workshop on eActivity, pp.351-356, 2012.
H. Hirose, NNRMLR2: An Improved Combined Method of Nearest Neighbor Regression and Multiple Linear Regression, 3rd IMS Asia Pacific Rim Meetings, Paper: 220534, 2014.
H. Hirose, NNRMLR3: Further Improved Combination Method of Nearest Neighbor Regression and Multiple Linear Regression, 2nd International Symposium on Applied Engineering and Sciences, 2014.
H. Hirose and T. Sakumura, Test evaluation system via the web using the item response theory, in Computer and Advanced Technology in Education, 2010, pp.152-158.
H. Hirose, T. Sakumura, Item Response Prediction for Incomplete Response Matrix Using the EM-type Item Response Theory with Application to Adaptive Online Ability Evaluation System, IEEE International Conference on Teaching, Assessment, and Learning for Engineering, 2012, pp.8-12.
H. Hirose, Yu Aizawa, Automatically Growing Dually Adaptive Online IRT Testing System, IEEE International Conference on Teaching, Assessment, and Learning for Engineering, 2014, pp.528-533.
H. Hirose, Y. Tokusada, K. Noguchi, Dually Adaptive Online IRT Testing System with Application to High-School Mathematics Testing Case, IEEE International Conference on Teaching, Assessment, and Learning for Engineering, 2014, pp.447-452.
H. Hirose, Y. Tokusada, A Simulation Study to the Dually Adaptive Online IRT Testing System, IEEE International Conference on Teaching, Assessment, and Learning for Engineering, 2014, pp.97-102.
H. Hirose, Meticulous Learning Follow-up Systems for Undergraduate Students Using the Online Item Response Theory, 5th International Conference on Learning Technologies and Learning Environments, 2016, pp.427-432.
H. Hirose, M. Takatou, Y. Yamauchi, T. Taniguchi, T. Honda, F. Kubo, M. Imaoka, T. Koyama, Questions and Answers Database Construction for Adaptive Online IRT Testing Systems: Analysis Course and Linear Algebra Course, 5th International Conference on Learning Technologies and Learning Environments, 2016, pp.433-438.
H. Hirose, Learning Analytics to Adaptive Online IRT Testing Systems “Ai Arutte” Harmonized with University Textbooks, 5th International Conference on Learning Technologies and Learning Environments, 2016, pp.439-444.
H. Hirose, M. Takatou, Y. Yamauchi, T. Taniguchi, F. Kubo, M. Imaoka, T. Koyama, Rediscovery of Initial Habituation Importance Learned from Analytics of Learning Check Testing in Mathematics for Undergraduate Students, 6th International Conference on Learning Technologies and Learning Environments, 2017, pp.482-486.
H. Hirose, Dually Adaptive Online IRT Testing System, Bulletin of Informatics and Cybernetics Research Association of Statistical Sciences, 48, 2016, pp.1-17.
H. Hirose, Difference Between Successful and Failed Students Learned from Analytics of Weekly Learning Check Testing, Information Engineering Express, Vol 4, No 1, 2018, pp.11-21.
H. Hirose, A Large Scale Testing System for Learning Assistance and Its Learning Analytics, Proceedings of the Institute of Statistical Mathematics, Vol.66, No.1, 2018, pp.79-96.
W.J.D. Linden and R.K. Hambleton, Handbook of Modern Item Response Theory. Springer, 1996.
C. Romero, S. Ventura, P.G. Espejo, C. Hervas, Data Mining Algorithms to Classify Students. Proceedings of the First International Conference on Educational Data Mining, 2008, pp.8-17.
T. Sakumura and H. Hirose, Making up the Complete Matrix from the Incomplete Matrix Using the EM-type IRT and Its Application, Transactions on Information Processing Society of Japan (TOM), 72, 2014, pp.17-26.
T. Sakumura, H. Hirose, Bias Reduction of Abilities for Adaptive Online IRT Testing Systems, International Journal of Smart Computing and Artificial Intelligence (IJSCAI), 1, 2017, pp.57-70.
G. Siemens and D. Gasevic, Guest Editorial - Learning and Knowledge Analytics, Educational Technology & Society, 15, 2012, pp.1-2.
K, Spoon, J. Beemer, J.C. Whitmer, J.F. J.P. Frazee, J. Stronach, A.J. Bohonak, R.A. Levine, Random Forests for Evaluating Pedagogy and Informing Personalized Learning, Journal of Educational Data Mining, 8(2), 2016. pp.20-50.
M. Sweeney, H. Rangwala, J. Lester, A. Johri, Next-Term Student Performance Prediction: A Recommender Systems Approach, Journal of Educational Data Mining, 8(1), 2016. pp.22-50.
Y. Tokusada, H. Hirose, Evaluation of Abilities by Grouping for Small IRT Testing Systems, 5th International Conference on Learning Technologies and Learning Environments, 2016, pp.445-449.
R. J. Waddington, S. Nam, S. Lonn, S.D. Teasley, , Improving Early Warning Systems with Categorized Course Resource Usage, Journal of Learning Analytics, 3, 2016, 263-290.
A.F. Wise and D.W. Shaffer, Why Theory Matters More than Ever in the Age of Big Data, Journal of Learning Analytics, 2, pp. 5-13, 2015.
