Mehregan Mahdavi
Mehregan Mahdavi has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Andrew Levula
Department of Information Technology, Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
2 shared publications - Shamsur Rahim
Department of Information Technology, Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
2 shared publications - Zawar Shah
Department of Information Technology, Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
2 shared publications - Adel Binbusayyis
Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
1 shared publication - Anil Kumar
Doctor of Business Administration (DBA), SP Jain School of Global Management, Mumbai 400070, Maharashtra, India
1 shared publication - Imdad Ullah
School of Computer Science, Faculty of Engineering, the University of Sydney, Sydney, Australia
1 shared publication
Latest Publications
- Open Access
- 10 July 2022
- Published by MDPI AG in Sustainability
- Vol. 14 (14) , 8431
Teacher Views on Teaching Sustainability in Higher Education Institutes in Australia
Higher education for sustainable development (HESD) plays a key role in achieving the United Nations’ 17 Sustainable Development Goals (SDGs). This research study specifically examined HESD in terms of eLearning initiatives in Australian private higher education providers from the perspective of teachers. A qualitative structured interview method was adopted wherein 10 teachers were interviewed in order to gain an understanding of their general knowledge of HESD, their attitudes and experience towards HESD, their teaching practices related to HESD, and their understandings of strategy as well as planning initiatives for their institution. The main findings suggest that (1) teachers in private higher education providers tend to have a limited knowledge of sustainability concepts and limited experience in teaching sustainability; (2) eLearning can be a valuable approach in teaching sustainability, but this approach presents teachers with challenges such as student engagement; and (3) private higher education providers require proper resources and governance frameworks in order for any sustainability initiative to be successful. This research highlights the resourcing aspect of private higher education providers in training staff, developing learning materials, and developing practical guidelines to achieve the SDGs by 2030.
- 10 July 2022
- Published by MDPI AG in Sustainability
- Vol. 14 (14) , 8431
Open AccessTeacher Views on Teaching Sustainability in Higher Education Institutes in Australia
Higher education for sustainable development (HESD) plays a key role in achieving the United Nations’ 17 Sustainable Development Goals (SDGs). This research study specifically examined HESD in terms of eLearning initiatives in Australian private higher education providers from the perspective of teachers. A qualitative structured interview method was adopted wherein 10 teachers were interviewed in order to gain an understanding of their general knowledge of HESD, their attitudes and experience towards HESD, their teaching practices related to HESD, and their understandings of strategy as well as planning initiatives for their institution. The main findings suggest that (1) teachers in private higher education providers tend to have a limited knowledge of sustainability concepts and limited experience in teaching sustainability; (2) eLearning can be a valuable approach in teaching sustainability, but this approach presents teachers with challenges such as student engagement; and (3) private higher education providers require proper resources and governance frameworks in order for any sustainability initiative to be successful. This research highlights the resourcing aspect of private higher education providers in training staff, developing learning materials, and developing practical guidelines to achieve the SDGs by 2030.
- Open Access
- 29 June 2022
- Published by MDPI AG in Sustainability
- Vol. 14 (13) , 7950
ILS Validity Analysis for Secondary Grade through Factor Analysis and Internal Consistency Reliability
In differentiated learning, the teacher needs to be aware of the learning styles of students in the classroom to accommodate specific learning preferences, e.g., the Felder–Silverman learning style model. The corresponding instrument, i.e., the Felder–Silverman Index of Learning Style (ILS), was designed to assess learning styles, specifically for engineering students. The ILS has been tested at the middle school level to identify the learning styles; however, validity/reliability had not been established in earlier studies with large samples. The focus of this study was to identify the validity and reliability of an ILS instrument for middle school students () by investigating the factor structure through factor analysis. This includes internal consistency reliability and constructing validity report of the ILS. An exploratory and confirmatory factor analysis was undertaken to investigate the factor structure to establish validity. As a result of the study, the reliability of the instrument was established. Five-factors emerged through exploratory factor analysis (EFA), which were subjected to confirmatory factor analysis (CFA). The outcome provided five-factors (i.e., Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Residual (SRMR), and Goodness of Fit (GFI)), out of which four factors were related to the four dimensions of the Felder–Silverman model, and the fifth factor was related to the association of sensing/intuitive and sequential/global dimensions of the model, which is in agreement with the theoretical construct of ILS. As a result of CFA, ILS entailing 24 items indicates a good fit with five-factor structure. ; ; . This study suggests that the ILS for the secondary-grade students needs to be revised with fewer items to improve the reliability, as supported by empirical evidence through the EFA and CFA.- 29 June 2022
- Published by MDPI AG in Sustainability
- Vol. 14 (13) , 7950
Open AccessILS Validity Analysis for Secondary Grade through Factor Analysis and Internal Consistency Reliability
In differentiated learning, the teacher needs to be aware of the learning styles of students in the classroom to accommodate specific learning preferences, e.g., the Felder–Silverman learning style model. The corresponding instrument, i.e., the Felder–Silverman Index of Learning Style (ILS), was designed to assess learning styles, specifically for engineering students. The ILS has been tested at the middle school level to identify the learning styles; however, validity/reliability had not been established in earlier studies with large samples. The focus of this study was to identify the validity and reliability of an ILS instrument for middle school students () by investigating the factor structure through factor analysis. This includes internal consistency reliability and constructing validity report of the ILS. An exploratory and confirmatory factor analysis was undertaken to investigate the factor structure to establish validity. As a result of the study, the reliability of the instrument was established. Five-factors emerged through exploratory factor analysis (EFA), which were subjected to confirmatory factor analysis (CFA). The outcome provided five-factors (i.e., Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Residual (SRMR), and Goodness of Fit (GFI)), out of which four factors were related to the four dimensions of the Felder–Silverman model, and the fifth factor was related to the association of sensing/intuitive and sequential/global dimensions of the model, which is in agreement with the theoretical construct of ILS. As a result of CFA, ILS entailing 24 items indicates a good fit with five-factor structure. ; ; . This study suggests that the ILS for the secondary-grade students needs to be revised with fewer items to improve the reliability, as supported by empirical evidence through the EFA and CFA. - 31 March 2022
- Published by Springer Nature
- p. 55-68
How Australians Are Coping with the Longest Restrictions: An Exploratory Analysis of Emotion and Sentiment from Tweets
- 31 March 2022
- Published by Springer Nature
- p. 55-68
How Australians Are Coping with the Longest Restrictions: An Exploratory Analysis of Emotion and Sentiment from Tweets
- 31 March 2022
- Published by Springer Nature
- p. 192-203
A Socially-Aware, Privacy-Preserving, and Scalable Federated Learning Protocol for Distributed Online Social Networks
- 31 March 2022
- Published by Springer Nature
- p. 192-203
A Socially-Aware, Privacy-Preserving, and Scalable Federated Learning Protocol for Distributed Online Social Networks
- Open Access
Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.
Open AccessMachine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.