E-Inclusion Prediction Modelling in Blended Learning Courses

Educating Engineers for Future Industrial Revolutions: Proceedings of the 23rd International Conference on Interactive Collaborative Learning (ICL2020). Advances in Intelligent Systems and Computing, Vol.1328 2020
Ieva Vītoliņa, Atis Kapenieks

This study aims to address the e-inclusion problem related to digital skills improvement and meaningful use. In professional work, more and more jobs require the use of digital skills. Combining e-learning and face-to-face training is a convenient and affordable way to learn new digital skills. However, the problem is the low number of e-learning graduates and the even lower number of those who use the newly acquired skills for professional or personal purposes. Machine learning approach is used to predict student achievement and other events. However, no comprehensive study has been conducted, analyzing how to improve digital skills training by ensuring that newly acquired skills are meaningfully used in professional life. This paper explores how to predict students’ learning impact outcomes - the use of newly acquired digital skills. We are using classification algorithms and machine learning approaches. We have compared five different classification algorithms and selected the ones with the best performance: LMT and lazy.LWL. We concluded that at least 81.60% of learners identified as not e-included are correctly predicted with lazy.LWL.

Atslēgas vārdi
Digital skills; E-inclusion; Machine learning
DOI
10.1007/978-3-030-68198-2_30
Hipersaite
https://link.springer.com/chapter/10.1007%2F978-3-030-68198-2_30

Vītoliņa, I., Kapenieks, A. E-Inclusion Prediction Modelling in Blended Learning Courses. No: Educating Engineers for Future Industrial Revolutions: Proceedings of the 23rd International Conference on Interactive Collaborative Learning (ICL2020). Advances in Intelligent Systems and Computing, Vol.1328, Igaunija, Tallinn, 23.-25. septembris, 2020. Cham: Springer Nature Switzerland AG, 2020, 327.-337.lpp. ISBN 978-3-030-68197-5. e-ISBN 978-3-030-68198-2. ISSN 2194-5357. e-ISSN 2194-5365. Pieejams: doi:10.1007/978-3-030-68198-2_30

Publikācijas valoda
English (en)