B.M. Monjurul Alom

Work place: Assessment Research Centre Melbourne Graduate School of Education Level 8, 100 Leicester Street, The University of Melbourne, Victoria 3010 Australia

E-mail: monjurul.alom@unimelb.edu.au

Website:

Research Interests: Data Mining, Data Compression, Data Structures and Algorithms

Biography

Dr. B.M. Monjurul Alom is a Programmer at the Assessment Research Centre, University of Melbourne, Australia. He completed a PhD (Computer Science) in 2011 at the University of Newcastle, Australia, where he worked as a research fellow from 2011–12. He was an Assistant Professor at Dhaka University of Engineering & Technology from 2000–2007. His work includes the development of collaborative web applications as a basis for research in the area of educational assessment. He has developed a registration portal system, web database application (multiplayer game) and scoring engine to assess the ability of students which is being used in schools in Australia and Internationally. His research also includes data mining and management for large scale databases. Monjurul also works on software development specifically for the assessment of 21st century skills, including collaborative problem solving. Monjurul has proficiency in web programming (game development, database application), server administration.

Author Articles
Educational Data Mining: A Case Study Perspectives from Primary to University Education in Australia

By B.M. Monjurul Alom Matthew Courtney

DOI: https://doi.org/10.5815/ijitcs.2018.02.01, Pub. Date: 8 Feb. 2018

At present there is an increasing emphasis on both data mining and educational systems, making educational data mining a novel emerging field of research. Educational data mining (EDM) is an attractive interdisciplinary research domain that deals with the development of methods to utilise data originating in an educational context. EDM uses computational methodologies to evaluate educational data in order to study educational questions. The first part of this paper introduces EDM, describes the different types of educational data environments, diverse phases of EDM, the applications and goals of EDM, and some of the most promising future lines of research. Using EDM, the second part of this paper tracks students in Australia from primary school Year 1 through to successful completion of high school, and, thereafter, enrolment in university. The paper makes an assessment of the role of student gender on successive rates of educational completion in Australia. Implications for future lines of enquiry are discussed.

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