Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS)

Pribadi, Feddy Setio; Permanasari, Adhistya Erna; Adji, Teguh Bharata

doi:10.1007/s10639-018-9745-z

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…Secondly, we applied a pre-processing method, which consisted of seven steps: case folding, line break removal, punctuation removal, numbering removal, stop-word removal, stemming, and the last pre-processing step was applying the proposed method called the abbreviations checker marked by the blue square in Figure 1. In the pre-processing step, we added a new approach different from the original one by using stop-word removal and stemming, marked in the gray square on Figure 1, in which these steps were not applied to the original method proposed by Pribadi [20].…”

Section: Methodsmentioning

confidence: 99%

“…The third and fourth steps were adopted from Pribadi's proposed method [20] by using MMR to generate variant reference answers and using the GAN-LCS method to calculate the final score. The last step was performance evaluation to measure the similarity between the score generated by the system and the score manually calculated by the teacher.…”

Section: Methodsmentioning

confidence: 99%

“…Pribadi [20] proposed the GAN-LCS method to deal with the problem of measuring the similarity of sentences with significantly different character lengths. This method uses the Longest Common Subsequence (LCS) method, which measures the length of the intersection of characters between the sentence of the reference answer (R) and the sentence of the student answer (S).…”

Section: Mmr = Argmaxmentioning

confidence: 99%

“…Furthermore, String-Based Similarity methods have great difficulty to accurately measure the similarity of two sentences that consist of different words or character lengths. Pribadi [20] proposed a new method named Geometric Average Normalized-Longest Common Subsequence (GAN-LCS). It is a type of Character-Based Similarity approach that can be used to handle the variety of student answers, which still have the same meaning but different character lengths.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring

2022

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Automatic short answer scoring methods have been developed with various algorithms over the decades. In the Indonesian language, the string-based similarity is more commonly used. This method is difficult to accurately measure the similarity of two sentences with significantly different word lengths. This problem has been handled by the Geometric Average Normalized-Longest Common Subsequence (GAN-LCS) method by eliminating non-contributive words utilizing the Longest Common Subsequence method. However, students’ answers may vary not only in character length but also in the words they choose. For instance, some students tend only to write the abbreviations or acronyms of the phrase instead of writing meaningful words. As a result, it will reduce the intersection character between the reference answer and the student answer. Moreover, it can change the sentence structure even though it has the same meaning by definition. Therefore, this study aims to improve GAN-LCS method performance by incorporating the abbreviation checker to handle the abbreviations or acronyms found in the reference answer or student answer. The dataset used in this study consisted of 10 questions with 1 reference answer for each question and 585 student answers. The experimental results show an improvement in GAN-LCS performance that could run 34.43% faster. Meanwhile, the Root Mean Square Error (RSME) value became lower by 7.65% and the correlation value was increased by 8%. Looking forward, future studies may continue to investigate a method for automatically generate the abbreviations dictionary.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Mmr = Argmaxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring

2022

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“…This means determining the most extended sequence obtained by removing some items from the first original sequence and removing other items from the second original sequence. This Problem also has a different application in text editing and data compression [20].…”

Section: The Longest Common Subsequence (Lcs)mentioning

confidence: 99%

Improving the Effectiveness of e-Learning Processes through Dynamic Programming: A Survey

Alqahtani¹,

Nadeem²

2021

IJACSA

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E-learning has been widely adopted as an important tool for distance education, especially in these days of pandemic Covid-19. However, several problems/challenges have been reported in different processes of e-learning that need to be addressed for effective use of e-learning. These problems/challenges include development of student focused contents, giving learner partial control, addressing different learning styles, etc. Recently, several efforts have been made to solve e-learning process problems using dynamic programming techniques. Dynamic programming techniques divide a problem situation into several subproblems and dynamically solves each sub-problem based on student needs. Thus it allows student focused customization at each step and provides adaptive e-learning to support students with different capabilities. The objective of this study is to review different e-learning problems and challenges and how those can be addressed using dynamic programming techniques. We conclude by highlighting the importance of different dynamic programming techniques for different processes of e-learning.

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Development of Prototype of Natural Language Answer Processor for e-Learning

Suleymanov

Prokopyev

2020

Artificial Intelligence

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Short answer scoring system using automatic reference answer generation and geometric average normalized-longest common subsequence (GAN-LCS)

Cited by 13 publications

References 12 publications

Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring

Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring

Improving the Effectiveness of e-Learning Processes through Dynamic Programming: A Survey

Development of Prototype of Natural Language Answer Processor for e-Learning

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