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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOURNAL OF EDUCATION SCIENCE Emerging Science Journal
Md. Sadekur Rahman, Md. Sadekur
Daffodil International University
Computer Science & IT Education Engineering Environmental Science

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Engaging Students To Take Ownership Of Their Learning Through A Stepped Teaching Model Based On The Qur’an: Evaluation By Teachers Trained Using This Model Badruzzaman Biplob, Khalid Been Md; M.Islam, Yousuf; Rahman, Md. Sadekur
JOURNAL OF EDUCATION SCIENCE Vol 1, No 1 (2015)
Publisher : JOURNAL OF EDUCATION SCIENCE

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Getting students to learn and be confident has always challenged teachers and educationists. With the introduction of the subject of Instructional Design, teaching is now looked upon as a stepped process through which students must be taken through. To this end many teaching models have been proposed and used by teachers all over the world. At the same time, it has been noticed that students who take “ownership” of their learning are most likely to become independent learners. Also, with the huge rise in demand for tertiary level education all over the world and more so in developing countries like Bangladesh developing successful models to manage the wave of new students has become even more important. In Bangladesh, the increased demand is coming from rural students who have had a limited access to proper primary and secondary education available in the rural areas of Bangladesh. This has added to the challenge of being able to deliver teaching that can turn around students who have poor study and language skills. In this paper we propose a stepped teaching model based on verses from the Qur’an that talk about the brain. We apply this model to training 54 newly recruited teachers who have joined Daffodil International University in the semester of spring, 2015. The idea is the teachers should evaluate the model and if perceived effective use the model in their own teaching. We demonstrate the model in action with these teachers and share the evaluation done by them.
Machine learning based COVID-19 study performance prediction Rahman, Md. Ataur; Rahman, Md. Sadekur; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1130-1139

Abstract

COVID-19 has impacted education worldwide. In this troublesome situation, it is hard enough for an institution to predict a student’s performance. Students’ performance prediction has always been a complex task and this pandemic situation has led this task to be more complex. The main focus of this work is to come up with a machine learning model based on a classical machine learning technique to predict the change in students’ performance due to COVID-19. Initially, some relevant features are selected, based on which the data are collected from students of some private universities in Bangladesh. After the entire data set is formed, we preprocessed the dataset to remove redundancy and noise. These preprocessed data are used for testing and training using the proposed model. The model is extensively evaluated in this way using three separate classical machine learning techniques, namely linear regression, k-nearest neighbors (k-NN), and decision tree. Finally, the results of the entire experiment follow, demonstrating the power of the machine learning model in such an application. It is observed that the proposed model with linear regression exhibits the best performance with an R2 error of 0.07% and an accuracy of 99.84%.
Accent Classification Across Continents: A Deep Learning Approach Hossain, Md. Fahad; Khan, Anzir Rahman; Rahman, Md. Sadekur; Ohidujjaman
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-030

Abstract

This study focuses on a deep learning based accent classification across continents and greatly enhances speech recognition systems by identifying the accents of Asia, Europe, North America, Africa, and Oceania. The Convolutional Neural Network (CNN) was trained on the Mozilla Common Voice dataset, which comprises the features extracted - Mel-Frequency Cepstral Coefficients, Delta, Delta-Delta, Chroma Frequency, and spectral features- and trained to classify accents. Multiple convolutional and dense layers for accent classification were combined with dropout and batch normalization layers to avoid overfitting during training. Out of the total validation data, 82% accuracy has been achieved. The Asian and European accents were classified with greater accuracy since their datasets were larger, whereas African and Oceanian accents were more misclassified due to limited representation and the greater diversity of languages. In contrast to the past research, which focused only on country-based accent classification, this work introduced a feature based deep learning approach of continent-based accent classification along the way. The recognition of this accent variation, in turn, helps integrate and improve various aspects of speech recognition systems and makes their application more inclusive for voice assistants and language learning tools with diverse linguistic patterns. The future work will concentrate on extending the dataset to the seven continents while enhancing classification accuracy via better feature engineering and model tuning.
Co-Authors Habib, Md. Tarek Hasan, Mahady Hossain, Md. Fahad Islam, Mohammad Monirul Khalid Been Md Badruzzaman Biplob, Khalid Been Md Khan, Anzir Rahman Ohidujjaman Rahman, Md. Ataur Yousuf M.Islam, Yousuf