Machine learning for mathematical question difficulty classification

This project is an experimental study on how machine learning models can be used for classification of GCE ‘A’ Level mathematical questions. Two levels of classification are carried out. First, the classification of questions to their respective topics and second, the classification of the questions...

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Main Author: Pang, Jarald Qi Kai
Other Authors: Hui Siu Cheung
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/76982
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-769822023-03-03T20:38:45Z Machine learning for mathematical question difficulty classification Pang, Jarald Qi Kai Hui Siu Cheung School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering This project is an experimental study on how machine learning models can be used for classification of GCE ‘A’ Level mathematical questions. Two levels of classification are carried out. First, the classification of questions to their respective topics and second, the classification of the questions to their difficulty level. The report will contain detailed explanations of the steps gone through during the experiment. The grading metrics used in this experiment are F1 Score, Precision, Recall and Accuracy. For data pre-processing three text vectorization methods, count vector, word level TF-IDF and N-gram level TF-IDF were used and tested. Four machine learning methods, Support Vector Machines, Naïve Bayes, Random Forest and Extreme Gradient Boosting, were then used to classify the data to their respective topic. Analysis was then done on the models’ performance on each topic. The same 4 machine learning methods were then again used to classify the difficulty of each question using the vectorized question and predicted topic. A final analysis was then done on the performance of the models in difficulty classification. Bachelor of Engineering (Computer Science) 2019-04-28T14:17:17Z 2019-04-28T14:17:17Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/76982 en Nanyang Technological University 45 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Pang, Jarald Qi Kai
Machine learning for mathematical question difficulty classification
description This project is an experimental study on how machine learning models can be used for classification of GCE ‘A’ Level mathematical questions. Two levels of classification are carried out. First, the classification of questions to their respective topics and second, the classification of the questions to their difficulty level. The report will contain detailed explanations of the steps gone through during the experiment. The grading metrics used in this experiment are F1 Score, Precision, Recall and Accuracy. For data pre-processing three text vectorization methods, count vector, word level TF-IDF and N-gram level TF-IDF were used and tested. Four machine learning methods, Support Vector Machines, Naïve Bayes, Random Forest and Extreme Gradient Boosting, were then used to classify the data to their respective topic. Analysis was then done on the models’ performance on each topic. The same 4 machine learning methods were then again used to classify the difficulty of each question using the vectorized question and predicted topic. A final analysis was then done on the performance of the models in difficulty classification.
author2 Hui Siu Cheung
author_facet Hui Siu Cheung
Pang, Jarald Qi Kai
format Final Year Project
author Pang, Jarald Qi Kai
author_sort Pang, Jarald Qi Kai
title Machine learning for mathematical question difficulty classification
title_short Machine learning for mathematical question difficulty classification
title_full Machine learning for mathematical question difficulty classification
title_fullStr Machine learning for mathematical question difficulty classification
title_full_unstemmed Machine learning for mathematical question difficulty classification
title_sort machine learning for mathematical question difficulty classification
publishDate 2019
url http://hdl.handle.net/10356/76982
_version_ 1759858415423717376