Abstract
Evaluating descriptive answer scripts is one of the challenging tasks for academicians along with their routine works and increase in the number of students enrolling in educational institution. It involves various factors such as man power, time, cost, and mental health. These factors are directly proportional to students’ strengths. Hence, evaluation scheme needs to be automated to ease the work of staff. Many research activities have been carried out to automate the evaluation process and easier the work of staff. In this paper, an attempt is made to propose two classes Eva classifier using Support Vector Machine Supervised Machine Learning algorithm for auto evaluating short answers and performance of the classifier is evaluated using accuracy of answer classification.
Please note that the LNCS Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Protest Over Delay in Evaluation Work. http://www.thehindu.com/news/cities/
80 out of 83 Score More After Revaluation. http://articles.timesofindia.indiatimes.com
Revaluation Fails 100 ‘Passed’ PU Students. http://www.bangaloremirror.com/index.aspx
Siddhartha, G.: e-Examiner: A System for Online Evaluation and Grading of Essay Questions. http://elearn.cdac.in/eSikshak/eleltechIndia05/PDF
Ramamurthy, M., Krishnamurthi, I., Ilango, S., Palaniappan, S.: Discrete Model Based Answer Script Evaluation Using Decision Tree Rule Classifier, pp. 1–12 (2019)
Alrehily, A.D., Siddiqui, M.A., Buhari, S.M.: Intelligent electronic assessment for subjective exam, ACSIT, ICITE, SIPM, pp. 47–63 (2018)
Kirithika, R., Jayashree, N.: Learning to Grade Short Answers using Machine Learning Techniques, WCI ’15, August 10–13, 2015
Basu, S., Jacobs, C. and Vanderwende, L.: Powergrading: a clustering approach to amplify human effort for short answer grading. Trans. Associat. Computat. Linguistics (2013)
Nlp.stanford.edu. The stanford nlp (natural language processing) group (2015)
Mohler, M., Bunescu, R. and Mihalcea, R.: Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments. Association for Computational Linguistics (2011)
Mohler, M., Mihalcea, R.: Text-to-text Semantic Similarity for Automatic Short Answer Grading. Association of Computational Linguistics (2009)
Shermis, M.D., Hamner, B.: Contrasting State-of-the-Art Automated Scoring of Essays: Analysis. Contrasting Essay Scoring, pp. 1–54 (2012)
Latifi, S.M.F., Guo, Q., Gierl, M.J., Mousavi, A., Fung, K., Lacroix, D.: Towards Automated Scoring using Open-Source Technologies. Annual Meeting of the Canadian Society for the Study of Education, pp. 13–14 (2013)
Kumar, S. and Sree, R.R.: Experiments towards determining best training sample size for automated evaluation of descriptive answers through sequential minimal optimization. ICTACT J. Soft Comput. 4(2), 710 –714 (2014)
Text Categorization with Support Vector Machines. http://www.cs.cornell.edu/people/tj/publications/joachims_98a.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vinothina, V., Prathap, G. (2020). EVaClassifier Using Linear SVM Machine Learning Algorithm. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_48
Download citation
DOI: https://doi.org/10.1007/978-981-15-1084-7_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1083-0
Online ISBN: 978-981-15-1084-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)