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

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.

Developing an Image-Based Deep Learning Framework for Automatic Scoring of The Pentagon Drawing Test

Background: The Pentagon Drawing Test (PDT) is a common assessment for visuospatial function. Evaluating the PDT by artificial intelligence can improve efficiency and reliability in the big data era. This study aimed to develop a deep learning (DL) framework for automatic scoring of the PDT based on image data. Methods: A total of 823 PDT photos were retrospectively collected and preprocessed into black-and-white, square-shape images. Stratified fivefold cross-validation was applied for training and testing. Two strategies based on convolutional neural networks were compared. The first strategy was to perform an image classification task using supervised transfer learning. The second strategy was designed with an object detection model for recognizing the geometric shapes in the figure, followed by a predetermined algorithm to score based on their classes and positions. Results: On average, the first framework demonstrated 62%accuracy, 62%recall, 65%precision, 63%specificity, and 0.72 area under the receiver operating characteristic curve. This performance was substantially outperformed by the second framework, with averages of 94%, 95%, 93%, 93%, and 0.95, respectively. Conclusion: An image-based DL framework based on the object detection approach may be clinically applicable for automatic scoring of the PDT with high efficiency and reliability. With a limited sample size, transfer learning should be used with caution if the new images are distinct from the previous training data. Partitioning the problem-solving workflow into multiple simple tasks should facilitate model selection, improve performance, and allow comprehensible logic of the DL framework.

Off-Topic Detection of Business English Essay Based on Deep Learning Model

The automatic scoring system of business English essay has been widely used in the field of education, and it is indispensable for the task of off-topic detection of essay. Most of the traditional off-topic detection methods convert text into vector representation of vector space and then calculate the similarity between the text and the correct text to get the off-topic result. However, those methods only focus on the structure of the text, but ignore the semantic association. In addition, the traditional detection method has a low off-topic detection effect for essays with high divergence. In view of the above problems, this paper proposes an off-topic detection method for business English essay based on the deep learning model. Firstly, the word2vec model is used to represent words in sentences as word vectors. And, LDA is used to extract the vector of topic and text, respectively. Then, word vector and topic word vector are spliced together as the input of the convolutional neural network (CNN). CNN is used to extract and screen the features of sentences and perform similarity calculation. When the similarity is less than the threshold, the paper also maps the topic and the subject words in the coupling space and calculates their relevance. Finally, unsupervised off-topic detection is realized by the clustering method. The experimental results show that the off-topic detection method based on the deep learning model can improve the detection accuracy of both the essays with low divergence and the essays with high divergence to a certain extent, especially the essays with high divergence.

Design of Automatic Scoring System for Oral English Test Based on Sequence Matching and Big Data Analysis

With the application of an automatic scoring system to all kinds of oral English tests at all levels, the efficiency of test implementation has been greatly improved. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. Aiming at the reliability of the automatic scoring system, based on the principle of sequence matching, this paper adopts the spoken speech feature extraction method to extract the features of spoken English test pronunciation and establishes a dynamic optimized spoken English pronunciation signal model based on sequence matching, which could maintain good dynamic selection and clustering ability in a strong interference environment. According to the comprehensive experiment, the automatic scoring result of the system is much higher than that of the traditional method, which greatly improves the recognition ability of oral pronunciation, solves the difference between the automatic scoring of the system and the manual scoring, and promotes the computer automatic scoring system to replace or partially replace the manual marking.

Interpreting quality assessment re-imagined: The synergy between human and machine scoring

Assessment of interpreting quality is a ubiquitous social practice in the interpreting industry and academia. In this article, we focus on both psychometric and social dimensions of assessment practice, and analyse two major assessment paradigms, namely, human rater scoring and automatic machine scoring. Regarding human scoring, we describe five specific methods, including atomistic scoring, questionnaire-based scoring, multi-methods scoring, rubric scoring, and ranking, and critically analyse their respective strengths and weaknesses. In terms of automatic scoring, we highlight four assessment approaches that have been researched and operationalised in cognate disciplines and interpreting studies, including automatic assessment based on temporal variables, linguistic/surface features, machine translation metrics, and quality estimation methodology. Finally, we problematise the socio-technological tension between these two paradigms and envisage human–machine collaboration to produce psychometrically sound and socially responsible assessment. We hope that this article sparks more scholarly discussion of rater-mediated and automatic assessment of interpreting quality from a psychometric-social perspective.