Data Mining In Higher Education Thesis

Data Mining In Higher Education Thesis-9
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 …

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.

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).

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.Since the definition of is still developing, we will start with our use of the term.In 2001 Doug Laney, an analyst with the META Group (now part of Gartner), described big data with a collection of "v" words, referring to (1) the increasing size of data (—to encompass the widely differing qualities of data sources, with significant differences in the coverage, accuracy, and timeliness of 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.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.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.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.

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