Author name: siavosh kaviani

Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

DIGITALIZATION IN EDUCATION AS AN ENHANCING FACTOR IN TEACHING MATH

DIGITALIZATION IN EDUCATION AS AN ENHANCING FACTOR IN TEACHING MATH

ABSTRACT The development of digitalization and digital technologies in Russia happens rapidly, changing the usual course of human life. The professional education system responds to current changes by adjusting to

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The Ethics of Learning Analytics in Australian Higher Education

The Ethics of Learning Analytics in Australian Higher Education

Introduction The increasing pervasiveness of data and analytics in higher education creates many new opportunities to support and enhance learning environments for students. As a result, there is growing interest

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An Ontological Model to Predict Dropout Students Using Machine Learning Techniques

An Ontological Model to Predict Dropout Students Using Machine Learning Techniques

Abstract E-learning systems become more popular than it has ever been. However the popularity of e-learning systems, still suffer from some problems related to the completion rate of online courses

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Students Perception of Mobile Learning during COVID-19 in Bangladesh: University Student Perspective

Students Perception of Mobile Learning during COVID-19 in Bangladesh: University Student Perspective

ABSTRACT The aim of this study is to measures the student’s perception of using mobile for learning during COVID-19 in Bangladesh especially at the university student’s perspective. During the COVID-19

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PDD: Predictive Diabetes Diagnosis using Datamining Algorithms

PDD: Predictive Diabetes Diagnosis using Data mining Algorithms

Abstract Data analytics is used to obtain useful insights from small or large data set to conclude some useful information and also used for future recommendation and decision making. Predictive

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Analyzing Product Usage Based on Twitter Users Based on Datamining Process

Analyzing Product Usage Based on Twitter Users Based on Data mining Process

Abstract This paper further enhances the techniques and chronological methods in order to perform the corresponding manipulation and further prediction analysis. We have acquired a real-time dataset based on the

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