Investigating The Effect Of Automated Writing Evaluation Through Deep Neural Network And Teachers' Written Evaluation...

Computer-assisted language learning technologies have made the greatest gains in language learning, especially in the context of artificial intelligence (AI), over the past few years. There are various technologies, such as automated writing evaluation (AWE hereafter) and automated essay scoring (AES), that are considered pillars of language learning, not only in computer disciplines but also in education. The evaluation can be done only in the form of holistic scores using AWE technology, which has been quite effective in enhancing language learning and, therefore, cannot provide detailed or in-depth feedback. To provide detailed writing feedback on two major elements of a language (i.e., Grammar and Fluency), a computer-aided implementation system entailing neural network models, and two semantic-based natural language processing (NLP hereafter) methods have been considered. To that end, 90 Pakistani university students who were English second-language learners (ESL) were randomly assigned to the control, instructor feedback, and experimental groups. The computer-assisted evaluation encompassed a neural network. The results of the comparison test between the AWE baseline model and the instructors who graded showed a correlation between the computer-aided feedback that used these neural networks. The implications of such results lie in ESL writing pedagogy, especially in situations where linguistic accuracy and fluency pose a challenge.