Learning Methods for Rating the Difficulty of Reading Comprehension Questions

This work deals with an Intelligent Tutoring System (ITS) for reading comprehension. Such a system could promote reading comprehension skills. An important step towards building a full ITS for reading comprehension is to build an automated ranking system that will assign a hardness level to questions used by the ITS. This is the main concern of this work. For this purpose we, first, had to define the set of criteria that determines the rate of difficulty of a question. Second, we prepared a bank of questions that were rated by a panel of experts using the set of criteria defined above. Third, we developed an automated rating software based on the criteria defined above. In particular, we considered and compared different machine learning techniques for the ranking system of the third part of the process: Artificial Neural Network (ANN), Support Vector Machine (SVM), decision tree and naïve Bayesian network. The definition of the criteria set for rating a question's difficulty, and the development of an automated software for rating a questions' difficulty, contribute to a tremendous advancement in the ITS domain for reading comprehension by providing a uniform, objective and automated system for determining a question's difficulty.

[1]  D. Krathwohl.  A Taxonomy for Learning, Teaching and Assessing: , 2008 .

[2]  L. Fuchs,et al.  Teaching Reading Comprehension Strategies to Students With Learning Disabilities: A Review of Research , 2001 .

[3]  Jane Oakhill,et al.  Reading Comprehension Difficulties , 2004 .

[4]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[5]  S. Kugblenu,et al.  Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm , 1999 .

[6]  Dorit Hutzler,et al.  Adaptation Schemes for Question's Level to be Proposed by Intelligent Tutoring Systems , 2014, ICAART.

[7]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[8]  Al-Azhar University-Gaza,et al.  PREDICTING LEARNERS PERFORMANCE USING ARTIFICIAL NEURAL NETWORKS IN LINEAR PROGRAMMING INTELLIGENT TUTORING SYSTEM , 2012 .

[9]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  Russell S. Ende.  Reading for Understanding in Grades 7, 8, and 9. , 1971 .

[12]  Frederick B. Davis,et al.  Fundamental factors of comprehension in reading , 1944 .

[13]  N. Uma Maheswari,et al.  Intelligent Tutoring System Using Hybrid Expert System With Speech Model in Neural Networks , 2010 .

[14]  Michihiro Namba.  Intelligent Tutoring System with Associative Cellular Neural Network , 2012 .

[15]  Benjamin S. Bloom,et al.  Taxonomy of Educational Objectives: The Classification of Educational Goals. , 1957 .

[16]  Lucila Ohno-Machado,et al.  Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.

[17]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[18]  Carisma Dreyer,et al.  Teaching reading strategies and reading comprehension within a technology-enhanced learning environment , 2003 .

[19]  Anwar Ali Yahya,et al.  Automatic Classification of Questions into Bloom's Cognitive Levels Using Support Vector Machines , 2011 .

[20]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[21]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.