Abstract—We are looking for new ways to instrument
classrooms towards the ideals of adaptive learning
environments and massively personal education. In this work,
we are focusing on a framework to provide affordable
alternatives for data collection, information services, and
Analytic models about the classroom environment. This
development advances the state-of-the-art by introducing an
alternative to analyse the education performance based on
differentiated multi-dimensional data and large data sets of
relevant data and information. We designed a proof-of-concept
experiment to detect variations of level of attentiveness, activity
and task performance. In our initial tests, we could successfully
collect and analyse relevant signals in a classroom environment
and relate them to education performance.
Index Terms—Massive education, learning analytics, big data, ambient intelligence.
F. Koch is with the SAMSUNG Research Institute, in Campinas, Brazil. He was with IBM Research, Brazil, by the time he worked on this paper (e-mail: email@example.com).
C. Rao is with IBM Research, in Melbourne, Australia (e-mail: firstname.lastname@example.org).
Cite: Fernando Koch and Chaitanya Rao, "Towards Massively Personal Education through Performance Evaluation Analytics," International Journal of Information and Education Technology vol. 4, no. 4, pp. 297-301, 2014.