Publication Type : Conference Paper
Thematic Areas : Amrita e-Learning Research Lab
Publisher : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Source : Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, Volume 10404 LNCS, p.543-552 (2017)
ISBN : 9783319623917
Keywords : Accurate mapping, Cheating detection, Difficulty levels, E assessments, Learning systems, Personalized learning, Question banks, University levels, Unsupervised learning, Unsupervised machine learning
Campus : Amritapuri
School : School of Arts and Sciences
Center : E-Learning
Department : E-Learning
Year : 2017
Abstract : Question Difficulty Level is an important factor in determining assessment outcome. Accurate mapping of the difficulty levels in question banks offers a wide range of benefits apart from higher assessment quality: improved personalized learning, adaptive testing, automated question generation, and cheating detection. Adopting unsupervised machine learning techniques, we propose an efficient method derived from assessment responses to enhance consistency and accuracy in the assignment of question difficulty levels. We show effective feature extraction is achieved by partitioning test takers based on their test-scores. We validate our model using a large dataset collected from a two thousand student university-level proctored assessment. Preliminary results show our model is effective, achieving mean accuracy of 84% using instructor validation. We also show the model’s effectiveness in flagging mis-calibrated questions. Our approach can easily be adapted for a wide range of applications in e-learning and e-assessments.
Cite this Research Publication : S. Narayanan, Kommuri, V. S., Subramanian, N. S., Kamal Bijlani, Nair, N. C., and , “Unsupervised Learning of Question Difficulty Levels using Assessment Responses”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10404 LNCS, pp. 543-552.