The effect of considering individual learning processes when creating online learning environments: A comparison of offline and online content materials

• Published in: 2017 IEEE Region 10 Symposium (TENSYMP) pp. 1 - 5
• Main Authors: Cheng Wang, Prasad, P. W. C., Maag, Angelika, Alsadoon, Abeer, Elchouemi, A., Sreedharan, Sasikumaran
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Australia; Bibliographies; Bloom's Taxonomy; Education; Feedback; Formative Assessment; IEEE Regions; Learning Analytics; Learning Process; Online learning; Schema; Taxonomy; Videos
• DOI: 10.1109/TENCONSpring.2017.8070017

For long, online learning has received criticism on not being able to provide helpful insight on students' learning, for a lack of necessary and appropriate interaction between instructor and students. Despite the fact that learning analytics (LA) are on the rise in terms of their popularity in academic institutions, the usefulness of LA come into question, especially the elements intended to provide effective feedbacks on students' learning process. The main problem for this seems to be a lack of analysis of the characteristics of individuals. Therefore, more needs to be done to actually help students' learning process. The study aims to produce a significant body data by analyzing the content available in online learning environments using Bloom's Taxonomy. Initial results show that most content found in online learning environments consist of offline learning material that was transferred to online learning platforms by taking very limited advantage of the resources of the Internet. It is hypothesized that in this study learning content in online environments do not support learners efficiently transfer to higher cognitive levels described in Bloom's taxonomy.