Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

Key courses of academic curriculum uncovered by data mining of students' grades

Learning is a complex cognitive process that depends not only on an individual capability of knowledge absorption but it can be also influenced by various group interactions and by the structure of an academic curriculum. We have applied methods of statistical analyses and data mining (Principal Component Analysis and Maximal Spanning Tree) for anonymized students' scores at Faculty of Physics, Warsaw University of Technology. A slight negative linear correlation exists between mean and variance of course grades, i.e. courses with higher mean scores tend to possess a lower scores variance. There are courses playing a central role, e.g. their scores are highly correlated to other scores and they are in the centre of corresponding Maximal Spanning Trees. Other courses contribute significantly to students' score variance as well to the first principal component and they are responsible for differentiation of students' scores. Correlations of the first principal component to courses' mean scores and scores variance suggest that this component can be used for assigning ECTS points to a given course. The analyse is independent from declared curricula of considered courses. The proposed methodology is universal and can be applied for analysis of student's scores and academic curriculum at any faculty

Experiences and opportunities in teaching ukrainian students at the faculty of mining and geoengineering in AGH University of Science and Technology

The paper presents the influence of various factors on the process of internationalisation of higher education in Poland, and particularly in AGH University of Science and Technology from the perspective of the Faculty of Mining and Geoengineering. It lays out educational opportunities for learners at mining and geology study courses, and the benefits stemming therefrom for international students, including students from Ukraine. Possibilities of academic exchange were discussed and that of international cooperation, in particular with Ukraine, in order to support the potential of science and higher education in both countries. Lastly, factors were indicated in favour of taking education with AGH

Career Development Program for Refugee and Migrant Youth

The Career Guidance for Refugee and Migrant Young People project is an initiative of the South Metropolitan Migrant Resource Centre funded by the Department of Education and Training. It aims to develop, pilot and evaluate a career development and planning program that specifically meets the learning levels and needs of refugee youth with low levels of education, cultural life skills and English language ability

A level playing ‘field’? A Bourdieusian analysis of the career aspirations of further education students on sports courses

There is currently a distinct dearth of research into how sports students’ career aspirations are formed during their post-compulsory education. This article, based on an ethnographic study of sport students in tertiary education, draws on data collected from two first-year cohorts (n = 34) on two different courses at a further education college in England. The study draws on ethnographic observations, and semi-structured group interviews, to examine in-depth the contrasting occupational perspectives emergent within these two groups of mainly working-class students, and how specific cultural practices affect students’ career aspirations. Utilising a Bourdieusian framework, the paper analyses the internalised, often latent cultural practices that impact upon these students’ diverse career aspirations. The hitherto under-researched dimension of inter-habitus interaction and also the application of doxa are outlined. The article reveals how the two student cohorts are situated within a complex field of relations, where struggles for legitimisation, academic accomplishment and numerous forms of lucrative capital become habituated. The study offers salient Bourdieusian-inspired insights into the career aspirations of these predominantly working-class students and the ways in which certain educational practices contribute to the production and reproduction of class inequalities

Creating an environment for free education and technology-enhanced learning

The purpose of this paper is to present a project aimed at making knowledge publically available through opene ducational resources (OER). The focus is on open online courses which will be created by educational institutions and best practice examples offered by leading companies, with the purpose to support life-long education and enhancement of academic education with practical knowledge. The goal is to create diverse high quality educational materials in electronic format, which will be publically available. The educational material will follow basic pedagogical-didactic principles, in order to best meet the needs of the potential learners. In accordance with that a review of didactic principles that can contribute to producing OER content of excellence is given. The choice of a convenient platform, as well as the application of appropriate information technologies enable content representation in a suitable, innovative and meaningful way

Unravelling the dynamics of learning design within and between disciplines in higher education using learning analytics

Designing effective learning experience in virtual learning environment (VLE) can be supported by learning analytics (LA) through explicit feedback on how learning design (LD) influences students’ engagement, satisfaction and performance. Marrying LA with LD not only puts existing pedagogical theories in instructional design to the test with actual learning data, but also provides the context of learning which helps educators translate established LA findings to direct interventions. My dissertation aims at unpacking the complexity of LD and its impact on students’ engagement, satisfaction and performance on VLE using LA. The context of this study is 400+ online and blended learning modules at the Open University (OU) UK. This research combines multiple sources of data from the OU Learning Design Initiative (OULDI), system log data, self-reported surveys, and performance data. Given the scope of this study, a wide range of visualization techniques, social network analysis, multi-level modelling, and machine learning will be used

Building Australia’s comparative advantages

This discussion paper, Building Australia’s Comparative Advantages, builds on the work of the BCA’s 2013 Action Plan for Enduring Prosperity. It seeks to start a conversation about what it will take to build an innovative economy, foster globally competitive industries and identify the types of jobs that can be created in an advanced economy like Australia. The paper focuses on actions government can take to foster an innovative and dynamic economy. The Business Council of Australia will facilitate further discussion on what businesses can do to come to terms with a global marketplace. We will also examine in detail the challenges that each sector faces to becoming globally competitive