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 mining is a computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data.
Data mining tools predict behaviors and future trends, allowing decision makers to make proactive, knowledge-driven decisions.
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.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.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.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.