Abstract:
Natural Language Processing (NLP) approach is adopted in this research to identify the
causes of students’ productivity problem in Bangladesh. To the best of the authors’
knowledge, the current study adopted a cross-sectional design where 3, 079 entries and
two-columns questionnaires incorporating the five target attributes; ‘Drifting’,
‘Technoference’, ‘Laziness’, ‘Rushed’, and ‘Procrastinating’ were employed to reveal the
underlying drivers of productivity interruption. Particularly, the study is based on surveys,
interviews of learners, and the data gathered from social networks to consider the main
difficulties that high school and university students meet at their studying process. In the
variety of Deep Learning and Machine Learning techniques, namely Decision Trees (DT),
Support Vector Machines (SVM), Logistic Regression (LR), Random Forest and
Convolutional Neural Networks (CNN) algorithms are used for the dataset. The efficiency
of these models is measured based on accuracy and here the accuracy of the CNN model
is 99.58%. They perform well in predicting and classifying the productivity interruptions,
hence depicting a use of the algorithms to address productivity issues among students.
Therefore, these findings of the study are pertinent to educational policymaking,
administration and practice in Bangladesh. Thus, achieving higher learning results, the
primary causes negatively influencing student productivity are revealed to create specific
measures and actions. Additionally, the study adds to the existing body of knowledge on
the problems of education in developing nations and shows how NLP and machine learning
can be applied for solving another tough issue concerning education.
