scholarly journals The Design and Implemention of Subjective Questions Automatic Scoring Algorithm in Intelligent Turtoring System

2013 ◽  
Author(s):  
Yaowen Xia ◽  
Zhiping Li ◽  
Saidong Lv ◽  
Guohua Tang
Keyword(s):  
Scoring Algorithm ◽  
Automatic Scoring

Automatic Scoring Algorithm of Chinese Subjective Questions Based on Domain Ontology and Sentence Framework

2013 ◽  
Vol 373-375 ◽  
pp. 1780-1783 ◽  
Author(s):  
Hong Chao Chen ◽  
Jin Jin Wang ◽  
Xin Hua Zhu
Keyword(s):  
Semantic Information ◽  
Domain Ontology ◽  
New Approach ◽  
Word Similarity ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Scoring Algorithm ◽  
Scoring Accuracy ◽  
Automatic Scoring ◽  
Speech Tagging

This paper constructs domain ontology for Data Structure Course and standard (student) answer sentence framework, then proposes a new approach to automatic marking Chinese subjective questions based on them. This method deals with the standard (student) answer in word segmentation, part-of-speech tagging, pronouns digestion, extracting framework, calculating word similarity. Compared with the traditional ones, this means allows the computer to understand the semantic information as much as possible, keeps the semantic relations between standard answer and the students, improves scoring accuracy.

scholarly journals Research and Design of Automatic Scoring Algorithm for English Composition Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Zhao
Keyword(s):  
Machine Learning ◽  
Development Trend ◽  
English Composition ◽  
Classroom Teaching ◽  
English Writing ◽  
Scoring Algorithm ◽  
Design Idea ◽  
Deep Integration ◽  
Automatic Scoring ◽  
Research And Design

With the development of artificial intelligence and big data, the concept of “Internet plus education” has gradually become popular, including automatic scoring system based on machine learning. Countries all over the world vigorously promote the deep integration of information technology and discipline teaching in various fields. English is a medium of communication in the current era of education information development trend. English composition automatic scoring mode is gradually accepted by the majority of educators and applied in the actual classroom teaching. However, the research of English composition automatic grading in teaching space is not perfect. Most systems have used traditional algorithms. Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm on English writing teaching, and proves the correctness of the design idea and algorithm of this paper through a lot of experiments.

Research on Automatic Scoring for Subjective Item of Database Domain Based on Database Synonyms Ontology

2014 ◽  
Vol 543-547 ◽  
pp. 3079-3082
Author(s):  
Ying Liu ◽  
Xiao Ran Zhang ◽  
Ying Zhang ◽  
De Peng Dang
Keyword(s):  
Hamming Distance ◽  
Text Similarity ◽  
Calculation Algorithm ◽  
Specific Subject ◽  
Similarity Calculation ◽  
Scoring Algorithm ◽  
Automatic Scoring ◽  
At Home

Nowadays, automatic scoring is an important way of teaching and examinations. However, there is no existing research on automatic scoring for subjective item of database domain both at home and abroad. According to the characteristics of database domain, we construct database domain synonyms ontology and proposed a text similarity calculation algorithm based on Hamming distance. Then we implement the automatic scoring for subjective item of database domain on the basis of ontology. In addition, in order to verify the accuracy and rationality of the algorithm, we take a specific subject as an example. The experiment results further illustrate the accuracy and efficiency of the proposed automatic scoring algorithm.

Research on Automatic Scoring Algorithm of Chinese Subjective Questions Based on Text Mining

2021 ◽  
Vol 105 ◽  
pp. 377-383
Author(s):  
Bo Yang ◽  
Yu Qi Yao
Keyword(s):  
Text Mining ◽  
Language Processing ◽  
Good Accuracy ◽  
Examination System ◽  
Review System ◽  
Sentence Similarity ◽  
Similarity Calculation ◽  
Scoring Algorithm ◽  
Online Examination ◽  
Automatic Scoring

At present, the research on automatic evaluation of computer online examination system has become a hot issue. Natural language processing technology based on text mining has unique advantages in text similarity calculation. This paper designs the TR-BFS-WE-WMD integrated algorithm for automatic review of Chinese subjective questions based on text mining, uses the word database to integrate the BFS algorithm, realizes the calculation of the text full sentence similarity and keyword matching, and solves the problem of text semantic similarity. Experimental results prove that this algorithm has good accuracy and effectiveness. The TR-BFS-WE-WMD algorithm provides a useful attempt for the intelligent research of the computer automatic review system and has good practical value.

The Design and Implemention of Subjective Questions Automatic Scoring Algorithm in Intelligent Turtoring System

2013 ◽  
Vol 347-350 ◽  
pp. 2647-2650
Author(s):  
Yao Wen Xia ◽  
Zhi Ping Li ◽  
Sai Dong Lv ◽  
Guo Hua Tang
Keyword(s):  
Reference Value ◽  
Test System ◽  
Fuzzy Mathematics ◽  
Key Technology ◽  
Scoring Algorithm ◽  
Automatic Scoring ◽  
Network Test System

Automatic subjective question of marking is a key technology in the network test system. In order to solve this problem, this paper analyzes the grading teachers thinking reviewers subjective questions. Then introduce the concept of a one-way approach degree based on the nearness theory of fuzzy mathematics. Finally design a subjective question automatic scoring algorithm and give a specific algorithm achievement. It provide certain reference value of automatic scoring of subjective questions.

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Absolute Point ◽  
Language Analysis ◽  
Search Patterns ◽  
Language Data ◽  
Report Accuracy ◽  
Point To Point ◽  
Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

Automatic Scoring of Shadowing Speech Based on DNN Posteriors and Their DTW

2017 ◽  
Author(s):  
Junwei Yue ◽  
Fumiya Shiozawa ◽  
Shohei Toyama ◽  
Yutaka Yamauchi ◽  
Kayoko Ito ◽  
...  
Keyword(s):  
Automatic Scoring

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

Sign in / Sign up

Export Citation Format

Share Document