research-article

Learning to Grade Short Answers using Machine Learning Techniques

Authors:

Jayasree NarayananAuthors Info & Claims

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Pages 262 - 271

https://doi.org/10.1145/2791405.2791508

Published: 10 August 2015 Publication History

Abstract

In this work, we are attempting to grade short answer automatically which can be efficient and helpful to both students and teachers. It uses a combination of many semantic and graph alignment features and is implemented in the Microsoft Azure Machine Learning using Two-class Averaged Perceptron, Linear and Isotonic Regression. We also provide first attempt to use graph alignment features at sentence level. We compare the results of two machine learning algorithms like Two-class Averaged Perceptron and Two-class Support Vector Machine in the results of grading short answers. We have devised novel techniques to apply the concept of Random Projection for grading 150 algorithmic answers on a coding question using our own domain specific corpus which gives precise classification of right and wrong answers.

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S. Basu, C. Jacobs, and L. Vanderwende. Powergrading:a clustering approach to amplify human effort for short answer grading. Transactions of the Association for Computational Linguistics, 2013.

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Microsoft. Microsoft azure machine learning studio, 2014.

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M. Mohler, R. Bunescu, and R. Mihalcea. Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments. Association for Computatonal Linguistics, 2011.

Digital Library

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M. Mohler and R. Mihalcea. Text-to-text Semantic Similarity for Automatic Short Answer Grading. Association of Computational Linguistics, 2009.

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N. Pharm, Y. Zhong Zhang, R. Bernardi, and M. Baroni. Sentence paraphrase detection: when determiners and word order make the difference.

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Cited By

Akhilesh PK ABharadwaj SVenugopalan M(2024)Automated Short Answer Grading With Word Embedding-Based Semantic Similarity Using PySpark2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS)10.1109/RAICS61201.2024.10690067(1-6)Online publication date: 16-May-2024
https://doi.org/10.1109/RAICS61201.2024.10690067
Mahajan DChanne PDiwate SKharate SPatil R(2024)Smart Grading System Using Bi LSTM with Attention MechanismAdvances in Communication and Applications10.1007/978-981-99-7633-1_18(247-260)Online publication date: 7-Jan-2024
https://doi.org/10.1007/978-981-99-7633-1_18
Divya AHaridas VNarayanan J(2023)Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT56650.2023.10179759(1-7)Online publication date: 22-Feb-2023
https://doi.org/10.1109/ICECCT56650.2023.10179759
Show More Cited By

Index Terms

Learning to Grade Short Answers using Machine Learning Techniques
1. Applied computing
  1. Education
  2. Enterprise computing
2. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Other conferences

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

August 2015

763 pages

ISBN:9781450333610

DOI:10.1145/2791405

Editor:
Indu Nair
SCMS, Kochi, India
,
General Chairs:
Sushmita Mitra
Indian Statistical Institute, Kolkata, India
,
Ljiljana Trajković
Simon Fraser University, Canada
,
Program Chairs:
Punam Bedi
University of Delhi, India
,
Suzanne McIntosh
New York University and Cloudera Inc., USA
,
M. S. Rajasree
IIITM-K, Trivandrum, India

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

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WCI '15

WCI '15: Third International Symposium on Women in Computing and Informatics

August 10 - 13, 2015

Kochi, India

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WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;

Overall Acceptance Rate 98 of 452 submissions, 22%

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Cited By

Akhilesh PK ABharadwaj SVenugopalan M(2024)Automated Short Answer Grading With Word Embedding-Based Semantic Similarity Using PySpark2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS)10.1109/RAICS61201.2024.10690067(1-6)Online publication date: 16-May-2024
https://doi.org/10.1109/RAICS61201.2024.10690067
Mahajan DChanne PDiwate SKharate SPatil R(2024)Smart Grading System Using Bi LSTM with Attention MechanismAdvances in Communication and Applications10.1007/978-981-99-7633-1_18(247-260)Online publication date: 7-Jan-2024
https://doi.org/10.1007/978-981-99-7633-1_18
Divya AHaridas VNarayanan J(2023)Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT56650.2023.10179759(1-7)Online publication date: 22-Feb-2023
https://doi.org/10.1109/ICECCT56650.2023.10179759
Sreevidhya VNarayanan J(2021)Short descriptive answer evaluation using word-embedding techniques2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT51525.2021.9579636(1-4)Online publication date: 6-Jul-2021
https://doi.org/10.1109/ICCCNT51525.2021.9579636
Forsyth SMavridis N(2021)Short Answer Marking Agent for GCSE Computer Science2021 IEEE World Conference on Engineering Education (EDUNINE)10.1109/EDUNINE51952.2021.9429163(1-6)Online publication date: 14-Mar-2021
https://doi.org/10.1109/EDUNINE51952.2021.9429163
Abu Romman LSyed SAlshmari MHasan M(2021)Improving Sentiment Classification for Large-Scale Social Reviews Using Stack GeneralizationProceedings of International Conference on Emerging Technologies and Intelligent Systems10.1007/978-3-030-85990-9_11(117-130)Online publication date: 3-Dec-2021
https://doi.org/10.1007/978-3-030-85990-9_11
Sung SLi CChen GHuang XXie CMassicotte JShen J(2020)How Does Augmented Observation Facilitate Multimodal Representational Thinking? Applying Deep Learning to Decode Complex Student ConstructJournal of Science Education and Technology10.1007/s10956-020-09856-2Online publication date: 16-Sep-2020
https://doi.org/10.1007/s10956-020-09856-2
Vinothina VPrathap G(2020)EVaClassifier Using Linear SVM Machine Learning AlgorithmIntelligent Computing and Communication10.1007/978-981-15-1084-7_48(503-509)Online publication date: 18-Feb-2020
https://doi.org/10.1007/978-981-15-1084-7_48
Sanjay ANair HMurali SKrishnaveni K(2018)A Data Mining Model To Predict Breast Cancer Using Improved Feature Selection Method On Real Time Data2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2018.8554450(2437-2440)Online publication date: Sep-2018
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Krishnaveni KPai RIyer V(2017)Faculty rating system based on student feedbacks using sentimental analysis2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2017.8126079(1648-1653)Online publication date: Sep-2017
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