Electronic essay grader online6/4/2023 ![]() ![]() In the field of education, assessment (summative or formative) is often categorized as either objective or subjective. Objective assessment is a form of questioning that has a single correct answer. Subjective assessment is a form of questioning that may have more than one correct answers or more than one way of expressing the correct answer. There are various types of objective and subjective questions. Objective questions include true/false, multiple choice, multiple response and matching questions. Subjective questions include extended-response questions. Objective assessment is well suited for the increasingly popular computerized or online assessment formats. However, concern has been expressed that such formats may not always be assessing what the teacher believes they are, partly because they require “the recognition of the answer, rather than the construction of a response”. The intellectual procedures assess how very different students respond when they are asked to build a response from their own with no prompts from the question. Since traditional paper-based assessment attributes higher value to short answer items, so research in Computer Assisted Assessment (CAA) systems has resulted in sophisticated response-matching techniques. Such recent developments enable automatic marking of longer free text-answers. The proposed model is applied to the field of education for essay scoring and to the field of bibliometric study for research article relevancy or classification of articles. Our research work is closely related to the work of. In Tandalla’s approach, multi-features, including RE from text, are extracted and trained on RF and GBM. The proposed approach uses the same model for training but is different in feature extractions. ![]() Similarly, in Ramachandran’s approach, the authors select two types of features: text patterns containing content tokens, and text patterns containing sentence-structure information. Then, these features are used in RF and GBM training models. This approach works slightly better, compared to Tandalla's method. ![]() For research article relevancy, “Proving Ground for Social Network analysis in the emerging research area “Internet of Things” (IoT)” (PGSN-IoT) uses only features of Word2vec for training purposes, and the model is trained only on GBM. This part describes the background of the model proposed in this article. The system is developed in different modules. Therefore, each module including the background is discussed in this section. Feature extraction and selection are different approaches that have been considered with multiple aspects. The Bag Of Words (BOW) approach is the most commonly used approach for feature extraction. In this technique, the order of terms is not considered. Text documents are represented with weighted frequencies (i.e., the term frequency-inverse document frequency of the sole terms in the collection). As each unique term is used in the construction of the feature set, even a collection including a small number of documents may be expressed with thousands of features. ![]()
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