An efficient automated answer scoring system for Punjabi language

Walia, Tarandeep Singh; Josan, Gurpreet Singh; Singh, Amarpal

doi:10.1016/j.eij.2018.11.001

Cited by 15 publications

(3 citation statements)

References 5 publications

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“…e keyword search method has developed a lot in resource search and is constantly improving, but inevitably, some problems are overlooked, such as students' screening and sorting of search resources in the search process, which takes a lot of time and seriously affects students' learning efficiency. At the same time, because most online education platforms do not pay attention to individual characteristics, the resources searched are also uniform [8]. To enable students to use their time effectively, learn and expert knowledge efficiently, and improve their experience, online education platforms necessarily need personalized recommendation algorithms to provide personalized recommendation services.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

Lian

2022

Mathematical Problems in Engineering

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This paper puts forward a method of similarity distribution and applies it to music teaching management system. We use Java language, using JavaME development platform to complete the basic information management, music homework management, music practice management, online classroom management, information notification management, and other modules of the technical implementation. The system uses mobile learning system to provide a convenient learning platform for learners. However, student users can only view the course information in the system. To allow students to know the course change information more quickly when teachers update course information, the system implements the course information update reminder function. Compared with the existing music teaching system, the music teaching system based on Android adopts wireless Internet technology, which is more suitable for the daily habits of today’s college students and makes full use of the fragmentary time, which is conducive to improving their learning efficiency and stimulating students’ interest in learning.

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Section: Introductionmentioning

confidence: 99%

Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

Lian

2022

Mathematical Problems in Engineering

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“…They used NLP to extract language-based features. Walia, Josan, and Singh (2019) developed grading of answers in the Punjabi language using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). Agung Putri Ratna et al ( 2018) developed a grading system for the Japanese language examinations.…”

Section: Related Workmentioning

confidence: 99%

Automatic Grading of Student’s Presentation Skills based on PowerPoint Presentation and Audio

Borade

Kiwelekar

Netak

2022

UPjeng

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Creating and delivering a presentation for Project, Seminar, and Course work is an important academic activity included in the curricula of undergraduate engineering studies. The presentation should be graded based on the presentation skills, accuracy, and authenticity of the contents covered in the presentation. Educational institutes use rubrics to assess the presentation skills on different grounds, which is a cumbersome task for the teacher when the strength of students is significant. Our main objective is to automatically grade the students' presentation skills in terms of PowerPoint presentations and the student's confidence. The proposed system describes a method and dataset designed to automate grading students' presentation skills. Our research study is divided into two parts. In the first part, the PowerPoint presentation features corresponding to text appearance, tables, charts, images, footer, and hyperlinks are extracted to grade PowerPoint presentations. At the same time, Mel-frequency Cepstral Coefficients, Mel Spectrogram, and Chroma features are extracted from the students' audio to identify confidence in the second part of the study. The audio is recorded at the presentation time. Feature extraction programs are implemented in python using Python-pptx and Librosa library. The tree-based feature selection method is used to remove the irrelevant features. Random Forest Ensemble model gives 100 % accuracy while predicting the grade of PowerPoint presentations. Multilayer Perceptron model gives 88% accuracy while predicting the confidence level of the students. The output of both models is combined to grade the students' presentation skills. The quadratic Weighted Kappa (QWK) score is 0.82, which indicates a significant similarity between automated and human-rated scores.

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“…Besides English, many overseas languages have endeavored to develop AES systems including Chinese [31], Punjabi [32], Swedish [33], Bahasa [34], Korean [35], and Arabic [36]. As for Arabic language, diverse approaches and techniques have been examined including a hybrid approach combining string-based, corpus-based and knowledge-based of text similarity measures [37], hybrid approach of LSA and POS tagging of syntactic analysis [38], cosine similarity [39], string-based (N-gram and Damera-levenshtein) and corpusbased (LSA and DISCO2) of text similarity measures [36], vector space model and latent semantic indexing [40], and a combination of LSA, writing style and spelling errors [41].…”

Section: Related Workmentioning

confidence: 99%

Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

Abdeljaber

2021

IEEE Access

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The manual process of scoring short answers of Arabic essay questions is exhaustive, susceptible to error and consumes instructor's time and resources. This paper explores longest common subsequence (LCS) algorithm as a string-based text similarity measure for effectively scoring short answers of Arabic essay questions. To achieve this effectiveness, the longest common subsequence is modified by developing weight-based measurement techniques and implemented along with using Arabic WordNet for scoring Arabic short answers. The experiments conducted on a dataset of 330 students' answers reported Root Mean Square Error (RMSE) value of 0.81 and Pearson correlation r value of 0.94. Findings based on experiments have shown improvements in the accuracy of performance of the proposed approach compared to similar studies. Moreover, the statistical analysis has shown that the proposed method scores students' answers similar to that of human estimator.

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An efficient automated answer scoring system for Punjabi language

Cited by 15 publications

References 5 publications

Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

Optimization of Music Teaching Management System for College Students Based on Similarity Distribution Method

Automatic Grading of Student’s Presentation Skills based on PowerPoint Presentation and Audio

Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

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