CNN-integrated NLP methods for Automatic Grading of Student Programming Assignment
Keywords:
Automatic Grading, Natural Language Processing, Convolutional Neural Networks, BERT, ROBERTA, DistilBERT, Automatic EvaluationAbstract
Automatic grading has recently acquired significant traction in the educational industry due to its ability to revolutionize the evaluation process. With the advent of communications technology, automatic grading has grown quickly in the educational sector. Manually assessing and grading programming assignments is a time-consuming and demanding undertaking. Furthermore, the results of manual review are more subject to human mistake. Hence, there is a need to automate the grading process using modern techniques such as machine learning and Natural Language Processing (NLP). This study describes an innovative strategy to automating the grading process that combines Convolutional Neural Networks (CNN), Natural Language Processing (NLP), and transformation techniques including BERT, ROBERTA, and DistilBERT. The suggested approach takes advantage of CNN's ability to extract important information from both the assignment and the grades automatically. These attributes are subsequently translated using NLP and transformation models, resulting in a single representation of the assignments' quality, accuracy, and clarity. The CNN model's performance is evaluated using transformation algorithms for Unigram, Bigram, and N-gram text. The results of the experiment show that CNN-based NLP transformation approaches routinely beat traditional methods of computerized grading.
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