Classification of Pedagogical content using conventional machine learning and deep learning model

TL;DR

This paper compares conventional machine learning and deep learning models, KNN and LSTM respectively, for classifying pedagogical content, achieving high accuracy with KNN.

Contribution

It introduces a comparative analysis of KNN and LSTM models for pedagogical content classification, highlighting their effectiveness.

Findings

KNN achieved 92.52% accuracy.

LSTM achieved 87.71% accuracy.

KNN outperformed LSTM in this task.

Abstract

The advent of the Internet and a large number of digital technologies has brought with it many different challenges. A large amount of data is found on the web, which in most cases is unstructured and unorganized, and this contributes to the fact that the use and manipulation of this data is quite a difficult process. Due to this fact, the usage of different machine and deep learning techniques for Text Classification has gained its importance, which improved this discipline and made it more interesting for scientists and researchers for further study. This paper aims to classify the pedagogical 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.