34,816 research outputs found
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
Educating the educators: Incorporating bioinformatics into biological science education in Malaysia
Bioinformatics can be defined as a fusion of computational and biological sciences. The urgency to process and analyse the deluge of data created by proteomics and genomics studies has caused bioinformatics to gain prominence and importance. However, its multidisciplinary nature has created a unique demand for specialist trained in both biology and computing. In this review, we described the components that constitute the bioinformatics field and distinctive education criteria that are required to produce individuals with bioinformatics training. This paper will also provide an introduction and overview of bioinformatics in Malaysia. The existing bioinformatics scenario in Malaysia was surveyed to gauge its advancement and to plan for future bioinformatics education strategies. For comparison, we surveyed methods and strategies used in education by other countries so that lessons can be learnt to further improve the implementation of bioinformatics in Malaysia. It is believed that accurate and sufficient steerage from the academia and industry will enable Malaysia to produce quality bioinformaticians in the future
Pedagogy: How to best teach population health to future healthcare leaders
Our healthcare system is moving from a fee-for-service reimbursement model to one that provides payment for improvements in three areas related to care: quality, coordination, and cost. Healthcare organizations must use a population health approach when delivering care under this new paradigm. Health administration programs play a critical role in training future leaders of healthcare organizations to be adaptable and effective in this dynamic environment. The purpose of this research was to: (1) engage health administration educators in a dialogue about population health and its relevance to healthcare administration education; (2) describe pedagogical methods appropriate for teaching population health skills and abilities needed for successful careers in our healthcare environment; and (3) identify current student learning outcomes that participants can tailor to utilize in their undergraduate and graduate health management courses. Authors conducted focus groups of participants attending this educational session at the 2018 annual AUPHA meeting. Qualitative analysis of the focus group discussions identified themes by a consensus process. Study findings provide validated recommendations for population health in the health administration curriculum. The identification of pedagogical approaches serves to inform educators as they prepare future health administrators to practice in this new era of healthcare delivery
Ecomining as a pattern of integrated approach towards sustainable mining
This paper briefly describes the Educational Project “EcoMining: Development of Integrated PhD
Program for Sustainable Mining & Environmental Activities” (2019–2022), which is being implemented
between Dnipro University of Technology (DUT, Ukraine) and Technical University Bergakademie
Freiberg (TU BAF, Germany) under support of German Academic Exchange Service (DAAD)
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Several efforts to predict student failure rate (SFR) at school accurately
still remains a core problem area faced by many in the educational sector. The
procedure for forecasting SFR are rigid and most often times require data
scaling or conversion into binary form such as is the case of the logistic
model which may lead to lose of information and effect size attenuation. Also,
the high number of factors, incomplete and unbalanced dataset, and black boxing
issues as in Artificial Neural Networks and Fuzzy logic systems exposes the
need for more efficient tools. Currently the application of Genetic Programming
(GP) holds great promises and has produced tremendous positive results in
different sectors. In this regard, this study developed GPSFARPS, a software
application to provide a robust solution to the prediction of SFR using an
evolutionary algorithm known as multi-gene genetic programming. The approach is
validated by feeding a testing data set to the evolved GP models. Result
obtained from GPSFARPS simulations show its unique ability to evolve a suitable
failure rate expression with a fast convergence at 30 generations from a
maximum specified generation of 500. The multi-gene system was also able to
minimize the evolved model expression and accurately predict student failure
rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap
with arXiv:1403.0623 by other author
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
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