TopicResponse ========================================= TopicResponse discovers topics with difficulty levels inferred by combining NMF-based topic modelling and Rasch modelling. TopicResponse is distributed as open source under GPL license. ####Relevant Publications TopicResponse implements the TopicResponse algorithm introduced in the following [paper]( TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan arXiv:1607.08720, 2016 #### Requirements [Python]( 2.7 The implementation is based on the [Nimfa]( python module for nonnegative matrix factorisation. Our implementation of TopicResponse algorithm lies in ```nimfa/methods/factorization/``` which is a new file added to the original nimfa module. You can either install [Nimfa 1.0]( and copy the above file to corresponding directory or use our local repository of nimfa. #### Preparing input data The input 'data' dictionary includes: * data['V']: word-student matrix with normalised tfidf * data['Hideal']: the ideal number of topics per student participated, computed using formula in our paper for H_ideal * data['words']: word list, used for printing topics Notes: The data directory contains only toy data as we cannot make our data public due to confidential and ethical issue. #### Running Topicresponse Start running a single test by ``` python ``` Options: -d, --dataset, default='do001' : course name -t, --num_topic, default=10 : the number of topics -wi, --lambda_w_init, default=0.1 : regularisation parameter for ||W|| -hi, --lambda_h_init, default=1.0, : regularisation parameter for H binary constraint -hideali, --lambda_h_ideal_init, default=1.0 : regularisation parameter for H_ideal constraint -rasch, --lambda_rasch, default=0.1 : regularisation parameter for rasch #### Results * *result.txt: contains the values of each part of the objective function, item difficulty (beta), and item infit. * *topic.txt: contains the generated topics. #### License TopicResponse is based on the following package. The modifications are Copyright (C) 2016-17 Jiazhen He nimfa - A Python Library for Nonnegative Matrix Factorization Techniques Copyright (C) 2011-2012 Marinka Zitnik and Blaz Zupan This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.