Data Mining In Higher Education Thesis

Data Mining In Higher Education Thesis-55
Furthermore, education has the ability to change and to induce change and progress in society.One of the Europe 2020 targets stipulates that at least 40% of the population aged 30-34 should have tertiary education attainment by 2020.Our discussion of the promises and pitfalls of big data analysis in higher education places a particular emphasis on veracity.

Furthermore, education has the ability to change and to induce change and progress in society.One of the Europe 2020 targets stipulates that at least 40% of the population aged 30-34 should have tertiary education attainment by 2020.

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Project abstract Education is essential for country’s development.

Education provides children, youth and adults with the knowledge and skills to be active citizens and to fulfil themselves as individuals.

Furthermore, this project aims discussing the main factors that underlie academic performance.

The models developed will be supported by data mining techniques and markov chains.

Technological and methodological advances have enabled an unprecedented capability for decision making based on big data.

This use of big data has become well established in business, entertainment, science, technology, and engineering.Higher education is also concerned with long-term goals—such as employability, critical thinking, and a healthy civic life.Since it is difficult to measure these outcomes, particularly in short-term studies, those of us in higher education often rely on theoretical and substantive arguments for shorter-term proxies.Measuring these types of tasks can increase the relevance and the precision of the results regarding what students learn, can allow the tailoring of instruction to specific students' needs, and can give individualized feedback across a range of learning issues.In addition, social interactions have increasingly moved from in-person to online.This will contribute to the achievement of satisfactory levels of attainment.Currently, high education institutions have made a big effort and investment on creating systems to collect education related data.Beyond the potential to enhance student outcomes through just-in-time, diagnostic data that is formative for learning and instruction, the evolution of higher education practice overall could be substantially enhanced through data-intensive research and analysis.A worthy next step would be to improve our capacity to rapidly process and understand today's increasingly large, heterogeneous, noisy, and rich data sets.The Agency has promoted the establishment of internal quality assurance systems, fostering the creation of a systematic collection of data that may enable to identify the main constraints and problems, enhancing the decision-making process.Having a better understanding of which students are more likely to face difficulties in their educational process and identifying the factors that influence these difficulties, higher education institutions will be able to timely develop strategies to increase the graduation rate and mitigate their attrition rates.

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