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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (8): 113-121    DOI: 10.11925/infotech.2096-3467.2020.1193
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Automatic Scoring for Subjective Questions in Maritime Competency Assessment
Han Hui(),Liu Xiuwen
Navigation College, Dalian Maritime University, Dalian 116000, China
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[Objective] This paper builds an automatic scoring system for subjective questions in the maritime competency assessment, aiming to reduce the heavy workload and human factors of subjective question scoring. [Methods] Firstly, we used the weighted TextRank algorithm of dependency syntax analysis to extract keywords. Then, we integrated sentence vectors, core words, syntactic components, and dependent structures to judge the similarity between student answers and the standard ones. Third, we constructed a set of special negative words for maritime affairs to judge the semantic opposition between the student’s answer and the standard answer. Finally, we gave each answer an objective score. [Results] We examined our method with multiple sets of different subjective questions, and found the average score difference between the automatic score and the manual scoring was 0.21, with a deviation rate of 4.20%. [Limitations] More research is needed to improve the processing of long and complex sentences. [Conclusions] The proposed algorithm could effectively evaluate subjective questions in the maritime competency assessment.

Key wordsAutomatic Scoring of Subjective Questions      Extraction      Similarity Calculation      Opposition Judgment      Maritime Field     
Received: 30 November 2020      Published: 15 September 2021
ZTFLH:  TP391  
Fund:Ministry of Industry and Information Technology([2018]No.473);Fundamental Research Funds for the Central Universities(3132019312)
Corresponding Authors: Han Hui ORCID:0000-0002-3053-0614     E-mail:

Cite this article:

Han Hui, Liu Xiuwen. Automatic Scoring for Subjective Questions in Maritime Competency Assessment. Data Analysis and Knowledge Discovery, 2021, 5(8): 113-121.

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Flow Chart of Automatic Scoring Algorithm
词性 概率分布
nz 40.67%
v 23.46%
n 20.21%
m 12.20%
b 3.46%
Keyword Part of Speech Distribution
Keyword Extraction Flowchart
说明 对立度判断
A类 标准答案与学生答案均不可切分 直接计算标准答案与学生答案中否定词的数量差
B类 标准答案不可切分,学生答案可切分为多句 计算学生答案所有分句中否定词个数之和与标准答案中否定词的数量差
C类 标准答案可切分为多句,学生答案不可切分 计算学生答案中否定词与标准答案第一分句中否定词的数量差
D类 标准答案与学生答案均可切分为多句 先计算学生答案第一分句中否定词与标准答案第一分句中否定词的数量差,然后计算学生答案和标准答案其余分句否定词个数和的差
Classification of the Degree of Opposition
文本切分结果 说明 评分公式
A,B,C类 学生答案与标准答案至少其一不可切分 M = 5 × ( ω × Co n Keywords + ( 1 - ω ) × Si m sen ) × O
D类 学生答案与标准答案第一分句对立,其余分句不对立 M = 2.5 × ( ω × Co n Keywords + ( 1 - ω ) × Si m sen )
学生答案与标准答案第一分句对立,其余分句也对立 M = 0
学生答案与标准答案第一分句不对立,其余分句对立 M = 2.5 × ( ω × Co n Keywords + ( 1 - ω ) × Si m sen )
学生答案与标准答案第一分句不对立,其余分句也不对立 M = 5 × ( ω × Co n Keywords + ( 1 - ω ) × Si m sen )
Scoring Formula in Different Situations
The Average Score Difference of the System Score under Different Values of ω
Distribution of Similarity Between System and Manual Scoring
项目 标准答案 学生答案
答案内容 潮汐主要是由天体引潮力作为原动力,由于其距离的不同形成了潮汐椭圆体,再结合月球公转以及地球自转、公转,其相对位置不断变换,造成了潮汐一天之内有高低潮现象。 潮汐主要是由天体引潮力作为原动力,海水随地球自转的同时,受到了月球和太阳的引力,在日,月引潮力的作用下引起的海面周期性的升降,称为海洋潮汐。
关键词 ‘天体引潮力’,‘高低潮’,‘潮汐椭球体’,‘位置’ ‘天体引潮力’,‘引力’,‘升降’
Example Analysis of Manual Scoring and System Scoring
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