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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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Deep learning-based user experience evaluation in distance learning.

Rahim Sadigov1, Elif Yıldırım1, Büşra Kocaçınar1

  • 1Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey.

Cluster Computing
|January 16, 2023
PubMed
Summary
This summary is machine-generated.

The Covid-19 pandemic negatively impacted distance education quality, with 54.5% of tweets showing negative emotions. Machine learning models analyzed over 160,000 tweets to assess e-learning effectiveness during the pandemic.

Keywords:
Deep learningDistance learningNLPSentiment analysis

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Area of Science:

  • Education Technology
  • Computational Social Science
  • Public Health

Background:

  • The Covid-19 pandemic necessitated a rapid shift to distance education in higher education.
  • Assessing the quality and impact of e-learning during this transition is critical.
  • Factors like resource availability and socioeconomic status influence e-learning effectiveness.

Purpose of the Study:

  • To evaluate the effectiveness of e-learning during the Covid-19 pandemic.
  • To analyze public sentiment and identify key topics related to distance education on Twitter.
  • To understand the emotional impact of pandemic-induced educational changes.

Main Methods:

  • Collected over 160,000 tweets related to education during the pandemic.
  • Developed deep learning-based sentiment analysis models (LSTM with word2vec).
  • Applied topic modeling using the Latent Dirichlet Allocation (LDA) algorithm.

Main Results:

  • The proposed sentiment analysis model achieved 76% accuracy.
  • 54.5% of analyzed tweets expressed negative emotions regarding distance education.
  • Identified key topics and negative sentiment associated with the shift to e-learning.

Conclusions:

  • The Covid-19 pandemic significantly impacted individuals' emotions towards distance education.
  • E-learning effectiveness is influenced by various factors, and public sentiment was largely negative.
  • Machine learning techniques provide valuable insights into the challenges of remote education during crises.