Educational Data Mining , Coming from the
future.
The Educational Data Mining is described in the official website http://www.educationaldatamining.org/ as an “emerging discipline” a new research area which aims to collect data from
different learning settings and by leveraging them to enhance the learning
outcomes. EDM focuses on how valuable data could be collected from the various
educational software that are being used in the learning process (Bienkowski, Feng & Means, 2012) . These learning data and the different variables that affect
a student’s performance can be gathered by EDM for the development of a
predictive model of a student’s behavior and hence the building of an adaptive
learning system where all the predictions can be the base to improve and change
what children will experience next (Bienkowski, Feng & Means, 2012) .
source of the picture: http://stefedu.blogspot.be/2011/01/lak11-learning-analytics-week-1.html Retrieved on 9/12/2012
According to Calders and Pechenizkiy (2012) while each one’s learning outcomes and
skills increase, vary and change a method for tracing and modeling them is
needed and at the same time different alternatives concerning the level of data’s
aggregation are offered. Some of them are:
Classication of student’s learning styles and
preferences
Predictive modeling of student’s likelihood to succeed in a course
Clustering similar students or similar courses,
Biclustering: detect what is appropriate for
each student in terms of questions, tasks etc.
Frequent pattern mining: find ways of appliance
and different study programs of the Learning management Systems
Emerging pattern mining: Find ways which detect
and explain the differences between the students that succeeded and those who
did not, even through different generations of students.
Collaborative filtering and recommendations: According
to the result outcomes recommend to students appropriate learning objects and
classes.
Visual analytics: Recommendation the use of visual
models for the facilitation of students’ collaboration.
Process mining: understand how students
perceive the curriculum in different study programs.
The definition of the accurate software and how
exactly it could be applied is an ongoing procedure. Nevertheless, according to
researches the implementation of the EDM has a quite sufficient framework to
work with in terms of technical resources, software and servers (Bienkowski, Feng & Means, 2012) .
Educational Data Mining and the impact that it
may cause to education in general is an ongoing debate and it can be seen from
different perspectives. The fact that students will be getting better advising
and how this could help the learning process is presented above, however, interesting
ways that EDM will change the current state in college marketing and the
administrative demands of schools, universities and colleges are being
discussed (Lepi, 2012) . Based on the gathered data analysis the predictions and
suggestions, students could have more accurate career suggestions while
colleges could be sufficiently prepared in order to accept them, as an overview
of their learning profiles would might be available (Lepi, 2012) .
It is quite strange that I wasn’t able to find
a well argued critical article towards the Educational Data Mining, probably
because it is actually an upcoming field in educational technology and science.
However, as a potential educator I would like to express my huge curiosity
concerning how this field will evolve in the future. I am always suspicious
when it comes to the “electronic grouping” of individuals, but as it is always with
technology it will probably be a challenge in the future to isolate and take
advantage of only the beneficial aspects of this extremely promising project.
For further information:
references/sources:
Bienkowski. M., Feng. M., & Means, B (2012) Enhancing teaching and Learning Through Educational Data Mining and Learning Analytics, An issue Brief. US Department of Education, Office of Educational Technology. p.9, 38. Retrieved on 9/12/2012 from http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf
Calders, T., & Pechenizkiy, M (2012) Introduction to the special section on educatiuonal Data Mining, Department of Computer science, Eindhoven University of Technology, Vol 13, Issue 2, p. 4. Retrieved on 9/12/2012 freom: http://www.kdd.org/sites/default/files/issues/13-2-2011-12/V13-02-02-Calders(introduction).pdf
Lepi, K (2012) The 10 ways Data Mining is about to change education, edudemic. Retrieved on 9/12/2012 from: http://edudemic.com/2012/08/data-education-evolutiong/
From YouTube:
Interesting! Is EDM only institution or organization based? It is clear what that universities might benefit from it, but is it also possible that this is something which could be student-driven? Thanks!
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