Data Mining In Higher Education Thesis

Data Mining In Higher Education Thesis-62
In this context, in the last decade it has been conducted a deep analysis, particularly on higher Education, which forced the evaluation, review and reformulation of the processes used to guarantee the quality of the education services provided.In Portugal, this reflection has been encouraged by the publication of a legal framework on quality assessment in higher education and the creation of the Agency for Assessment and Accreditation of Higher Education – A3ES.However, institutions have not been able to analyze this data and turn it into valuable information.

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This project aims at exploring the use of educational data (e.g.

social-economic, demographic, higher education access average and academic results) to identify ‘bottlenecks’ (at the curricular unit level) that constraint academic sucess and to predict students’ academic performance.

Data in MOOCs includes longitudinal data (dozens of courses from individual students over many years), rich social interactions (e.g., videos of group problem-solving over videoconference), and detailed data about specific activities (e.g., watching various segments of a video, individual actions in an educational game, or individual actions in problem solving).

The depth of the data is determined not only by the raw amount of data on a learner but also by the availability of contextual information.

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.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.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.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.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.Our discussion of the promises and pitfalls of big data analysis in higher education places a particular emphasis on veracity.In addition, our discussion focuses on MOOCs (massively open online courses) as an opportunity for data-intensive research and analysis in higher education.


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