Data Mining in Education

In 2007, two papers were published. In first paper titles as ‘Applying Data Mining Techniques to e-Learning Problems.’ by Castro F., Vellido A., Nebot Г. & Mugica F. The authors aim is to provide an up-to-date snapshot of the current state of research and applications of Data Mining methods in e-learning. In this paper, authors presented a categorisation of e-learning complications to which DMT like, Neural Networks, Genetic Algorithms, Clustering and Visualization Methods, Fuzzy Logic, Intelligent agents, and Inductive Reasoning, have been applied including, for instance: Students’ classification based on their learning performance; detection of irregular learning activities; e-learning system direction-finding and communication optimization; clustering according to similar e-learning system usage; and systems’ adaptability to students’ desires and capabilities.

Authors have presented a general and up-to-date survey on Data Mining application in e-learning. Second paper authors, (VraniД‡, M., Pintar, D., & SkoДЌir, Z.) developed the paper titled as ‘The use of Data Mining in Education Environment’.

This paper presents how DMT and algorithms can be used in the academic community to potentially improve some aspects of education quality.

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However, the importance of acquiring adequate datasets has to be especially emphasized. (Machine learning and data mining give us techniques which can be used to analyze to data and uncover previously unknown rules and, the associations, hidden knowledge which once acquired — and properly interpreted — can be used in multitude of ways.)
In 2008, ‘A Decisional Tool for Quality Improvement in Higher Education’ paper was presented by Selmoune, N., & Alimazighi, Z. In the design and the deployment of tool, the concepts of data warehouse, multidimensional model and data mining, are essentials.

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In this paper author described the design and implementation of a decisional tool to improve the quality of service, by analysing the pedagogical results, to discover the success and failure factors, and attempt to increase success chances of students. It offers a data mining module based on associative rules discovery, to check association within the marks achieved in different academic modules.
In 2010, 6 papers were presented. Davis H. C., Carr L., Hey J. M. N., Howard Y., Millard D., Morris D., & White S. ‘Bootstrapping a Culture of Sharing to Facilitate Open Educational Resources’.

It seems self-evident that life for teachers would be simplified if there existed a large corpus of relevant resources that was available for them to reuse and for inquisitive students to download. The learning object community has worked for the past decade and more to provide the necessary infrastructure, standards, and specifications to facilitate such beneficial activity, but the take-up has been disappointingly small, mainly in Higher Education. The main aim of this paper is how to inspire educational groups to share their knowledge. 2. Zhou, M. ‘Data Mining and Student e-Learning Profiles’. Data mining techniques have been applied to educational research in various ways. In this paper author presented the application of sequential data mining algorithms to analyse computer logs to profile learners in terms of their learning tactic use and motivation in a web-based learning environment. The application of data mining in education has requirements not present in other domains, mainly the need to take into account pedagogical aspects of the learner and the learning system as well as individual characteristics of learners, such as motivation, cognition, and so forth. The advanced analysis methods, such as data mining algorithms, are promising to address issues that of utmost importance in education but hard to solve with traditional methods. Buldu, A., & ГњГ§gГјn, K. ‘Data mining application on students’ data’. In data mining techniques, association rules are one of the most preferred techniques.

Apriori algorithm is the most used one in these association rules. Data mining can be used effectively in educational institutes for leading education activities in an effective way, for watching students’ performances continuously and directing students in course and profession choosing. Thus, the level of students’ success can be raised. Being evaluated previously of the associations in which the students are unsuccessful with associations being observed, different strategies can be determined to make this situation away. Besides, it can be used as a helping tool in profession choosing of students according to their aptitudes and characteristics. Romero C., & Ventura S. ‘Educational Data Mining: A Review of the State of the Art’. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide.

It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed. MacBride, G., Hayward, E. L., Hayward, G., Spencer, E., Ekevall, E., Magill, J., Stimpson, B. ‘Engineering the Future: Embedding Engineering Permanently Across the SchoolUniversity Interface’. This paper describes the design, implementation, and evaluation of an educational program. The paper concludes that sustainable long-term promotion of engineering within schools to support the transition to university is possible if certain conditions are fulfilled. These conditions include the use of a model that facilitates partnerships among researchers, policy-makers, and practitioners in all sectors.

Building such essential linkages is often challenging, but this effort is necessary if changes in engineering education are to be realized and sustained. Molina-Gaudo, P., Baldassarri, S., Villarroya-Gaudo, M., & Cerezo, E. ‘Perception and Intention in Relation to Engineering: A Gendered Study Based on a One-Day Outreach Activity’. This paper explores both how male and female high school pupils (1516 years old) perceive the engineering profession and their willingness to pursue a career in this area. The gender distribution of engineering students varies according to countries and engineering specialties, but the underrepresentation of women is prevalent. Since it appears that sex differences in interest levels emerge during childhood, there is a need for educators and researchers to: 1) understand the factors that determine children’s occupational interests and goals; and 2) develop efficient programs aimed at increasing the interest of girls in engineering.

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Data Mining in Education. (2020, May 16). Retrieved from

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