scholarly journals Exploring the Feasibility of an Automated Essay Scoring Model Based on LSTM

2021 ◽  
Vol 24 (4) ◽  
pp. 223-238
Author(s):  
Kangyun Park ◽  
Yongsang Lee ◽  
Dongkwang Shin
Keyword(s):  
Automated Essay Scoring ◽  
Scoring Model ◽  
Model Based ◽  
Essay Scoring

Comparison ofe-rater® Automated Essay Scoring Model Calibration Methods Based on Distributional Targets

2012 ◽  
Vol 12 (4) ◽  
pp. 345-364 ◽  
Author(s):  
Mo Zhang ◽  
David M. Williamson ◽  
F. Jay Breyer ◽  
Catherine Trapani
Keyword(s):  
Model Calibration ◽  
Automated Essay Scoring ◽  
Scoring Model ◽  
Calibration Methods ◽  
Essay Scoring

Automated Essay Scoring: A Human's Review

PsycCRITIQUES ◽  
2004 ◽  
Vol 49 (Supplement 14) ◽  
Author(s):  
Steven E. Stemler
Keyword(s):  
Automated Essay Scoring ◽  
Essay Scoring

Learning writing skills using feedback from automated essay scoring

2009 ◽  
Author(s):  
Ronald T. Kellogg ◽  
Alison P. Whiteford ◽  
Thomas Quinlan
Keyword(s):  
Writing Skills ◽  
Automated Essay Scoring ◽  
Essay Scoring

EVALD – a Pioneer Application for Automated Essay Scoring in Czech

2019 ◽  
Vol 113 (1) ◽  
pp. 9-30
Author(s):  
Kateřina Rysová ◽  
Magdaléna Rysová ◽  
Michal Novák ◽  
Jiří Mírovský ◽  
Eva Hajičová
Keyword(s):  
Czech Republic ◽  
Native Speakers ◽  
Threshold Level ◽  
Automated Essay Scoring ◽  
The Czech Republic ◽  
Linguistic Differences ◽  
Supervised Training ◽  
Language Data ◽  
Overall Performance ◽  
Essay Scoring

Abstract In the paper, we present EVALD applications (Evaluator of Discourse) for automated essay scoring. EVALD is the first tool of this type for Czech. It evaluates texts written by both native and non-native speakers of Czech. We describe first the history and the present in the automatic essay scoring, which is illustrated by examples of systems for other languages, mainly for English. Then we focus on the methodology of creating the EVALD applications and describe datasets used for testing as well as supervised training that EVALD builds on. Furthermore, we analyze in detail a sample of newly acquired language data – texts written by non-native speakers reaching the threshold level of the Czech language acquisition required e.g. for the permanent residence in the Czech Republic – and we focus on linguistic differences between the available text levels. We present the feature set used by EVALD and – based on the analysis – we extend it with new spelling features. Finally, we evaluate the overall performance of various variants of EVALD and provide the analysis of collected results.

Marketing customer response scoring model based on machine learning data analysis

2020 ◽  
pp. 1-11
Author(s):  
Tang Yan ◽  
Li Pengfei
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Extraction ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Customer Data ◽  
Modeling And Analysis ◽  
Scoring Model ◽  
Model Based ◽  
Learning Data

In marketing, problems such as the increase in customer data, the increase in the difficulty of data extraction and access, the lack of reliability and accuracy of data analysis, the slow efficiency of data processing, and the inability to effectively transform massive amounts of data into valuable information have become increasingly prominent. In order to study the effect of customer response, based on machine learning algorithms, this paper constructs a marketing customer response scoring model based on machine learning data analysis. In the context of supplier customer relationship management, this article analyzes the supplier’s precision marketing status and existing problems and uses its own development and management characteristics to improve marketing strategies. Moreover, this article uses a combination of database and statistical modeling and analysis to try to establish a customer response scoring model suitable for supplier precision marketing. In addition, this article conducts research and analysis with examples. From the research results, it can be seen that the performance of the model constructed in this article is good.

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