CNN in Practice: A Guide to Evaluating Multiple-Choice Exams
DOI:
https://doi.org/10.32870/recibe.v15i2.486Keywords:
Convolutional Neural Networks, Artificial Intelligence, Automated Evaluation, Image Processing, Deep Learning, Public SectorAbstract
This guide presents a convolutional neural network (CNN)-based system for automating the evaluation of multiple-choice exams. Python and TensorFlow were used to develop a model capable of detecting answers with high accuracy (99.18%), overcoming the limitations of digital image processing (DIP) and a recall of 99.45%. The results show the model's effectiveness in automatic answer classification. The research highlights the importance of image preprocessing and the selection of the activation function based on the problem of enhancing performance. Although the system is efficient, it faces limitations related to dataset diversity and the need for specialized hardware. Future improvements include expanding the dataset and optimizing the model for low-resource devices. This work contributes to the field of artificial intelligence applied to education by facilitating automated exam evaluation and optimizing grading time.References
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