A4 Artikkeli konferenssijulkaisussa
Future Educational Technology with Big Data and Learning Analytics

Julkaisun tekijät: Kanth R., Laakso M., Nevalainen P., Heikkonen J.
Kustantaja: Institute of Electrical and Electronics Engineers Inc.
Julkaisuvuosi: 2018
Kirjan nimi *: 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE)
Tietokannassa oleva lehden nimi: IEEE International Symposium on Industrial Electronics
ISBN: 978-1-5386-3705-0
eISBN: 978-1-5386-3706-7


In the recent years, big data and learning analytics have been emerging
as fast-growing research fields. The application of these emerging
research areas is gradually addressing the contemporary challenges of
school and university education. Tracing out the information regarding
students' misconceptions and dropping-out probabilities from the courses
at the right instant of time, development of detectors of a range of
educational importance and achieving the highest level of quality in the
higher education are becoming more challenging. Moreover, providing
well timed and the best suitable solutions to the students at-risk are
even more strenuous. In this concept paper, we aim to address these
contemporary challenges of school and the university education and their
probable solutions by utilizing our research experiences of automated
assessment, immediate feedback, learning analytics and the IT
technologies. Solving such problems by knowing the history of students'
activities, submissions, and the performances data is possible. The
identification of students' misconceptions during the learning process,
examining behavioral patterns and significant trends by efficiently
aggregating and correlating the massive data, improving the
state-of-the-art skills in creative thinking and innovation, and
detecting the drop-outs on-time are highlighted in this article. We are
aiming at extracting such knowledge so that adaptive and personalized
learning will become a part of the current education system. Not only
the available algorithm of supervised learning methods such as support
vector machine, neural network, decision trees, discriminant analysis,
and nearest neighborhood method but also new engineering and
distillation of relevant data features can be carried out to solve these
educational challenges.

Last updated on 2019-30-01 at 00:07