Automated essay scoring with e-rater v.2

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Hence, assigning the score of this essay to the tested essay. In fact, the comparison between the manual scored and the tested essays is performed based on specific analysis. The earliest analysis was performed based on the writing style in, which the length of paragraphs, number of sentences and number of words were the core of the comparison 6. This approach has been criticized by many researchers due to the indifference of content analysis, which may lead to some kind of cheating 1.

Therefore, researchers have become more interested in content-based approaches in, which the lexical and semantic analysis could be performed between the manual scored and the tested essays. The LSA is a useful approach that has been commonly used in the field of automated essays assessments. Latent semantic analysis is the process of identifying semantic similarity among text sets 7. It aims to analyse a given text using synonyms and hyponym in order to conclude the meaning of such text.

In fact, several researchers have utilized latent semantic analysis in order to assess essays answers 8 , 9 , 2 , The LSA is a useful approach for identifying similarity among two text set This can be performed by analysing the manually scored answers that have been provided by teachers and comparing it with the automatic answers by the system.


Several issues have been arisen in the field of automated essays answers such as treating complex languages like Arabic language. The complexity of Arabic language lies on the association between its semantic and syntactic In Arabic, in order to identify the actual meaning of certain word, it is necessary to declare its syntactic such as verb, noun, adjective and others.

However, LSA does not has the ability to analyse the syntactic of a given text Therefore, this study aims to address such problem in terms of automated Arabic essay scoring. Automatic Essay Scoring AES is the study that has been proposed to assess the teachers by providing an automatic approach to evaluate the score of an essay In fact, there are several techniques have been used for AES where the writing style, lexical analysis, semantic analysis, syntactic analysis and probabilistic approach have been examined in terms of providing scores Kanejiya et al.

Basically, LSA is concentrated on the semantic side where the syntactic features have been denied. Hence, the performance would significantly affected if the meaning of a given sentence is associated with grammar. Therefore, the authors have proposed syntactic features with LSA including POS tagging and parser which reasonably has contributed toward improve the accuracy.

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  • Presently, a few research efforts have been proposed in terms of assessing essay in many languages such as Loraksa and Peachavanish 10 who proposed an automatic scoring for essay in Thai language. Basically, two vectors have been built in order to represent the term frequency of the essays and the corresponding human scores. These vectors have been combined with LSA in order to enrich the synonyms and hyponym. Temporarily Unavailable

    After that, these vectors will be used as a training set for ANN to classify the semantic. The proposed method has concentrated on the semantic aspect by utilizing two matrices, one for the manually scored answer and the other for the automatic answer. Apparently, the similarity will be measured between the two matrices in order to identify the score.

    Eventually, Arabic essay assessment has been addressed by Reafat et al. The authors have concentrated on identifying a synonym dictionary in order to measure the similarity between the manually scored and automatic answers. Then a similarity measure which is cosine similarity has been used in terms of validating the automatic answer. Gomaa and Fahmy 15 have introduced the first benchmark of Arabic dataset for automatic scoring essays which contains students answers written in Arabic language. The authors have applied several similarity measures including string-based, n-gram and corpus-based similarity measures independently and with combination.

    Then they have applied k-means clustering approach in order to scale the obtained similarity values. Finally, Alghamdi et al. In fact, the proposed method consists of hybrid of Latent Semantic Analysis LSA with specific linguistic features including stemming, number of words and number of spelling mistakes.

    Apparently, the hybrid is performed using semantic and the writing style where, the semantic analysis is being performed by LSA and the writing style is represented by the number of words and spelling mistakes. The research design of this study consists of five main phases as shown in Fig. The 1st phase is the corpus collection, which is associated with the dataset that has been used for processing. Whilst, the 2nd phase is pre-processing, which is associated with the tasks of normalization, tokenization and stemming. The 3rd phase is synonym replacement, which aims to replace each word with its corresponding synonym in order to enhance the process of identifying semanticsimilarity.

    For this purpose, a domain-specific dictionary has been created. In fact, this phase consists of two sub-phases: The 1st is the Latent Semantic Analysis LSA which aims to produce the similarity matrix between the selected answer with the model answer. In fact, this modification represents the contribution of this study where LSA will be modified syntactically using POS tagging. However, cosine similarity, which aims to identify the lexical similarity between the attributes within the matrix and will be applied for both standard LSA and modified LSA.

    Finally, the 5th phase is Evaluation, which aims to evaluate the automatic scoring compared to the manual scores. Corpus collection: The dataset used in this study is the same that introduced by Gomaa and Fahmy 15 which consists of 61 questions related to environmental science with 10 answers for each question provided by 10 students in which the total number of answers is Such, questions consist of four types including "Define, explain, what are the consequences and why".

    Table 1 shows the details of such questions. However, each question contains model answer that would be compared with the given answer by the students. In addition, the manual scores by the teachers have been identified in order to be compared with the automatic generated scores. Pre-processing: This phase aims to turn the data into a suitable form by normalizing, tokenizing and stemming the data. Normalization aims to eliminate the unwanted data such as: Numbers, special characters and stopwords. According to Isa et al.

    This is due to the false indication that could be obtained by such words for example, the stop-word "The" could occur frequently but at the same time, such frequency does not yield valuable indication Arabic stop-words are various and can be formed with many variations.

    Alajmi et al. Hence, this study aims to utilize the list of Arabic stop-words with its forms that have been generated by such study. Synonym replacement: This phase aims to replace all the words with their corresponding synonyms.

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    • Therefore, this study aims to construct a domain-specific dictionary in order to list all the words with its potential synonyms. Hence, replacing each word with its existing synonyms in order to unify the corresponding words. Such approach has been introduced by Runeson et al.

      Topic outline

      This can significantly provide accurate results of matching among answers where students frequently use alternative words. Latent semantic analysis: Latent semantic analysis is an approach that widely used in Natural Language Processing NLP for identifying the similarity between two groups of text It aims to analyse the relationships among two set of documents by creating a vector space for the semantic of words, terms and concepts that occurred in both documents.

      This can be performed by vectoring the words into two rows and columns where the words are represented in the rows and the documents represented in the columns. Then using the theory of words frequency, LSA can identify important relationship by counting the frequency of words Figure 2 shows the framework of such standard LSA. Consider three answers and A 1 , A 2 and A 3 , which have sentences as shown in Table 2. In order to represents the mentioned answers via LSA, a matrix X is being constructed in which the unique words of the three answers are represented as rows and the answers represented as columns as shown in Table 3.

      Table 3 shows that the matrix will be populated based on TF-IDF in which the word frequencies will be assigned using 1 or 0 where 1 indicates the presence and 0 indicates the absence in accordance to the corresponding answer. Considering the high dimensionality of given words, a post-processing task called Singular Value Decomposition SVD will be applied in order to reduce the dimensionality of the words matrix.

      Automatic essay scoring with recurrent neural network

      In particular, SVD aims to decrease the number of rows without losing the similarity structure among the columns. The SVD can be calculated using the following Eq. After applying SVD, the cosine similarity will be computed between each pair of answers in order to identify the similarity among them. The cosine is calculated as Eq. Hence, LSA has a main drawback which lies in denying the syntactic aspect of the words Each meaning cannot be determined without using the syntactic aspect. Therefore, this study aims to propose a modified LSA using a syntactic feature.

      Such syntactic feature is part-of-speech POS tagging. The following sub-section describes it in further details. Sign in. E-rater[R] has been used by the Educational Testing Service for automated essay scoring since This paper describes a new version of e-rater V. The main innovations of e-rater V. The paper describes this new system and presents evidence on the validity and reliability of its scores. Contains 2 endnotes, 9 tables, and 2 figures. Attali, Y.

      Journal of Technology, Learning, and Assessment, 4 3 ,. Department of Education.

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      Copyright for this record is held by the content creator. For more details see ERIC's copyright policy. An evaluation of computerised essay marking for national curriculum assessment in the UK for year-olds. British Journal of Educational Technology 38 6 , To determine the reason for the discrepancies, the markers discussed the texts that had received discrepant scores and the researcher identified three reasons for the discrepancies that he termed Human Friendly, Neutral, and Computer Friendly.

      James, Cindy L. Validating a computerized scoring system for assessing writing and placing students in composition courses. Assessing Writing 11 3 , Correlations between machine and human scores ranging from. IntelliMetric picked only one of the 18 nonsuccessful students, and humans picked only 6 of them. Addresses many issues related to the machine scoring of writing: historical understandings of the technology Ken S. Ericsson; Chris M.

      Matzen, Jr. Ziegler; Teri T. Includes a item bibliography of machine scoring of student writing spanning the years Richard Haswell , and a glossary of terms and products. Wilson, Maja. Rethinking Schools 20 3. Critique found problems in repetition, sentence syntax, sentence length, organization, and development. Part II: Online writing assessment. NCES — Washington, DC: U.