Assessment Of Descriptive Answers in Moodle-based E-learning using Winnowing Algorithm
Journal of Contemporary Issues in Business and Government,
2021, Volume 27, Issue 3, Pages 2759-2769
AbstractAssessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. An automatic evaluation tool is proposed that allows the assessor to evaluate descriptive answer at any time and receive instant feedback of the students. Due to the lack of descriptive answer grading in Moodle-based E-learning system, there is a need to build a model and also add this feature as a plug-in for the E-learning system .Up until today, most assessors still choose to examine descriptive document manually for each student document. This method takes time where assessor needs to be focused and thorough while examining a number of descriptive documents. This reason often affects essay examinations to be less objective and not optimal. To improve objectivity, time efficiency and fair correction in descriptive answers assessment process, a system is needed that can automatically assess student documents, or in other words a descriptive answer evaluating system. An evaluation of descriptive answer system, works by analysing student answer document with model answer document. The higher the semantic similarity, the higher the score obtained.The purpose of this paper is to check the similarity between the teacher’s answer and the student’s answer using Winnowing algorithm. Winnowing algorithm is one of the document fingerprinting algorithms that can be used to detect document similarity by using hashing technique. The fingerprint document itself is a method used to detect document similarities with other documents. The Winnowing algorithm has fulfilled one of the requirements of the plagiarism algorithm, which is whitespace insensitivity, disposing of irrelevant characters such as punctuation. The similarity value is calculated using Jaccard Coefficient. Later this assessment is used for grading the student’s performance.
- Attali, Y. and Burstein. J, “Automated essay Scoring with e-rater V.2”, The Journal of Technology, Learning and Assessment, 2019.
- Amit Rokade, Bhushan Patil, Sana Rajani, Surabhi Revandkar, Rajashree Shedge, “Automated Grading System using Natural Language Processing”, 2nd International Conference of Inventive Communication & Computational Technologies (ICICCT) 2018.
- Praful Mishra, Anmol Mishra, Aniket Bharati, Prof. Serta Ambadekar, “Theoretical Answer Evaluation Using LSA, BLEU, WMD and Fuzzy Logic”, International Journal of Advanced Research in Computer Science, Vol 9, March-April 2018.
- Sijimol P J, Surekha Mariam Varghese, “Handwritten Short Answer Evaluation System (HSAES)”, International Journal of Scientific Research in Science and Technology, 2018, Volume 4, Issue 2.
- Mitchell, T. Russell, P.Broomhead, N. Aldridge “Towards Robust Computerised Marking of Free-text responses”, Proceedings of the 6th computer assisted assessment conference, Loughborough, 2017.
- Gregory K.W.K. Chung, Harold F.O’Neil, “Methodological Approaches to Online Scoring of Essays”, The Regents of the University of California, December 1997.
- Raheel Siddiqi, Christopher J. Harrison, and Rosheena Siddiqi, “Improving Teaching and Learning through Automated Short-Answer Marking”, IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, VOL. 3, NO. 3, JULY-SEPTEMBER 2010.
- Md. Monjurul Islam, A. S. M. Latiful Hoque, “Automated Essay Scoring Using Generalized Latent Semantic Analysis”, JOURNAL OF COMPUTERS, VOL. 7, NO. 3, MARCH 2012.
- Eric Ganiwijaya Hasan, Arya Wicaksana, Seng Hansun, “The Implementation of Winnowing Algorithm for Plagiarism Detection in Moodle-based E-learning”, 2018.
- Srujana Inturi,”A Survey on the Assessment of Models towards Automated Free-Text Marking Engine”, JETIR May 2018, Volume 5, Issue 5, 2018
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