Soft-Computing Method for Settling Land Disputes Cases Based on Text Similarity

Obot, Okure U.; Uzoka, Faith Michael; John, Anietie; Udoh, Samuel S.

doi:10.1504/ijbis.2020.10036247

Cited by 2 publications

(2 citation statements)

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“…To aid in the retrieval of similar land dispute cases for easy and fast administration of court cases, Obot et al [9] applied Cosine, Jaccard, Text Semantic Similarity (TSS) and fuzzy logic separately to 205 cases. Results show cosine similarity measure had the strongest correlation, (72%) followed by Jaccard (70%), fuzzy logic (70%) and TSS (63%).…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Marking System for Essay Questions

Obot,

Obike,

James

2024

JERR

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The stress of marking assessment scripts of many candidates often results in fatigue that could lead to low productivity and reduced consistency. In most cases, candidates use words, phrases and sentences that are synonyms or related in meaning to those stated in the marking scheme, however, examiners rely solely on the exact words specified in the marking scheme. This often leads to inconsistent grading and in most cases, candidates are disadvantaged. This study seeks to address these inconsistencies during assessment by evaluating the marked answer scripts and the marking scheme of Introduction to File Processing (CSC 221) from the Department of Computer Science, University of Uyo, Nigeria. These were collected and used with the Microsoft Research Paraphrase (MSRP) corpus. After preprocessing the datasets, they were subjected to Logistic Regression (LR), a machine learning technique where the semantic similarity of the answers of the candidates was measured in relation to the marking scheme of the examiner using the MSRP corpus model earlier trained on the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. Results of the experiment show a strong correlation coefficient of 0.89 and a Mean Relative Error (MRE) of 0.59 compared with the scores awarded by the human marker (examiner). Analysis of the error indicates that block marks were assigned to answers in the marking scheme while the automated marking system breaks the block marks into chunks based on phrases both in the marking scheme and the candidates’ answers. It also shows that some semantically related words were ignored by the examiner.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Marking System for Essay Questions

Obot,

Obike,

James

2024

JERR

View full text Add to dashboard Cite

show abstract

“…The answers of individual student were subjected to comparison with that of the model answers provided by the examiners with a view to finding the degree of similarities between the option chosen by the student and the model answer provided by the examiner. Jaro similarity measure was used in finding the similarity due to its high degree of similarity with manual scoring shown in existing literature (Obot et al, 2020;Obot et al, 2021) Jaro measure is given as:…”

Section: Data Collection and The Experimentsmentioning

confidence: 99%

Grading Multiple Choice Questions Based On Similarity Measure

Obot¹,

Onwodi²,

Attai³

et al. 2023

EJCSIT

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Recent findings show the poor performances of students in Multiple Choice Questions (MCQ) examinations. This could be attributed to the administration of questions on application of knowledge rather than on fundamental of knowledge. Candidates doing these examinations at times choose options that are close to the actual answer but get zero (0) as the reward. The objective of developing a system that rewards a candidate based on the approximation of an option chosen not on the exactness of the option to the answer was therefore formulated in this study. 500 MCQs with their model answers, students’ answers and scores obtained from two universities in Nigeria were collected. Jaro similarity measure was used to compute the degree of similarity between the model answers and the student answers. Results of the experiment show an average deviation of 13.3 marks. The adoption of this method of grading would be very beneficial in evaluating learners on e-learning platforms. The result is encouraging but could be improved upon with semantic similarity measure hybridized with string similarity.

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Soft-Computing Method for Settling Land Disputes Cases Based on Text Similarity

Cited by 2 publications

References 0 publications

Automated Marking System for Essay Questions

Automated Marking System for Essay Questions

Grading Multiple Choice Questions Based On Similarity Measure

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