Manish V. Patel’s research while affiliated with Sankalchand Patel College of Engineering and other places

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Publications (2)


Deep Learning in Automated Short Answer Grading: A Comprehensive Review
  • Article
  • Full-text available

July 2024

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33 Reads

ITM Web of Conferences

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Manish Patel

Automated Short Answer Grading (ASAG), more generally referred to as ASAG, is a method that evaluates the written short answers provided by students through the use of certain computer algorithms. This particular component of ASAG has been the subject of study for a considerable amount of time [4]. A significant obstacle in ASAG is the low availability of relevant training data inside the domain. This is one of the most significant obstacles. There are a number of different approaches that may be taken to address this problem. These approaches can be broadly classified into two categories: traditional methods that rely on handcrafted characteristics and Deep Learning-based approaches [22]. Over the course of the past five years, there has been a significant increase in the number of researchers in this field that have adopted Deep Learning techniques in order to address the ASAG challenge [6]. The purpose of this research is to determine whether or whether strategies based on Deep Learning are superior to traditional methods across 38 different publications. Additionally, the study intends to provide a full review of the many deep learning methodology that have been investigated by academics in order to address this issue [19]. In addition to this, the study provides an analysis of a number of state-of-the-art datasets that are ideal for ASAG tasks and makes recommendations for evaluation metrics that are suitable for regression and classification situations.

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Impact of Machine Learning in Education

September 2023

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131 Reads

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Krunal Suthar

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Education can shape a better tomorrow, and it positively affects learning outcomes when it is delivered with better methods and procedures. Academic institutions are always looking for solutions to improve their efficiency and boost student outcomes. For many scholars in the past several years, data mining has become an area of interest because of statistics mining’s ability to uncover hidden patterns and trends in the data. Learning with the aid of technology, also known as educational data mining, is a productive process that significantly contributes to the growth of the academic sector. It is incredibly simple to retrieve the necessary data from various sources using machine learning techniques, and users like students, teachers, and professors can immediately utilize this data. The use of various tools to deliver better education utilizing efficient material produced from a very large database and provided to the target audience is one of many trends that are currently becoming popular in the educational system. In the current scenario when thinking about the use of machine learning in education, many techniques are available like adaptive learning, predictive learning, personalized learning, assessment evaluation, and many more. A different researcher works on it, but still, there is a lack of technique that handles multiple criteria and give concrete way using which the education domain get beneath. Therefore, the purpose of this work is to identify and analyze the numerous machine learning techniques that support the extraction of crucial information.