Classification of pedagogical content using conventional machine and deep learning model

Authors

  • Vedat Apuk Department of Computer Science and Engineering, University for Business and Technology, Prishine, Kosovo
  • Krenare Pireva Nuci Department of Computer Science and Engineering, University for Business and Technology, Prishine, Kosovo

Keywords:

Document Classification, KNN, LSTM, coursera dataset, education, text classification, deep learning models, machine learning models

Abstract

ed this discipline and made it more interesting for scientists and researchers for further study. This paper aims to classify the pedagogically content using two different models, the K-Nearest Neighbor (KNN) from the conventional models and the Long short-term memory (LSTM) recurrent neural network from the deep learning models. The result indicates that the accuracy of classifying the pedagogical content reaches 92.52 % using KNN model and 87.71 % using LSTM model.

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Published

2021-07-30

How to Cite

Apuk, V., & Pireva Nuci, K. (2021). Classification of pedagogical content using conventional machine and deep learning model. WiPiEC Journal - Works in Progress in Embedded Computing Journal, 7(1). Retrieved from https://wipiec.digitalheritage.me/index.php/wipiecjournal/article/view/28

Issue

Vol. 7 No. 1 (2021): WiPiEC Journal

Section

Articles

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