Data Mining In Higher Education Thesis

Data Mining In Higher Education Thesis-31
However, institutions have not been able to analyze this data and turn it into valuable information.Therefore, data analysis in this context is promising, as it enables institutions to discover and extract hidden knowledge of students’ patterns from educational environment.Since the definition of is still developing, we will start with our use of the term.

However, institutions have not been able to analyze this data and turn it into valuable information.Therefore, data analysis in this context is promising, as it enables institutions to discover and extract hidden knowledge of students’ patterns from educational environment.Since the definition of is still developing, we will start with our use of the term.

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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.

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.

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.

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).

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