Data Mining In Higher Education Thesis

Data Mining In Higher Education Thesis-70
keywords: Education and Big Data; Business Intelligence applied to education; Educational Data Mining; Predictive moddeling; Learning Analytics; Academic performance Team Supervisor: Vera Miguéis (DEGI-FEUP) Students: André Filipe Roque Silva ; Pedro Afonso Paulino Ferreira de Castro Contributors: Ana Freitas (LEA, FEUP), Paulo Garcia (DEF-FEUP; LEA-FEUP); UPorto Digital Dates: September 2015 to …

keywords: Education and Big Data; Business Intelligence applied to education; Educational Data Mining; Predictive moddeling; Learning Analytics; Academic performance Team Supervisor: Vera Miguéis (DEGI-FEUP) Students: André Filipe Roque Silva ; Pedro Afonso Paulino Ferreira de Castro Contributors: Ana Freitas (LEA, FEUP), Paulo Garcia (DEF-FEUP; LEA-FEUP); UPorto Digital Dates: September 2015 to …

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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.

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.

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.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.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.MOOCs illustrate the many types of big data that can be collected in learning environments.Large amounts of data can be gathered not only across many learners (broad between-learner data) but also about individual learner experiences (deep within-learner data).Whereas big data is beginning to be utilized for decision making in higher education as well, practical applications in higher education First, the sector lacks much of the computational infrastructure, tools, and human capacity required for effective collection, cleaning, analysis, and distribution of large datasets.In addition, in collecting and analyzing student data, colleges and universities face privacy, safety, and security challenges not found in many scientific disciplines.

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