Knowledge Acquisition Data Visualization in eLearning Delivery
CSEDU 2020 : Proceedings of the 12th International Conference on Computer Supported Education. Vol.2 2020
Atis Kapenieks, Iveta Daugule, Kristaps Kapenieks, Viktors Zagorskis, Jānis Kapenieks, Žanis Timšāns, Ieva Vītoliņa

The aim of the study is to create the complete landscape model for learner behavior and knowledge acquisition data, and mapping the real learner performance data on it. This paper reports on a TELECI approach for learner knowledge acquisition data visualization. We present the new metrics for determination the relevance of the e-course content and delivery approach to learners. This approach is based on the assumption that knowledge acquisition of real e-content can be quantified by superposition of the impact of learning “perfect” content, “too easy” content, and “too complicated” content. The user learning performance data are generated in the TELECI e-learning environment with additional short, easy-to-use multiple-choice questions before and after each content subunit. This approach was well accepted by learners. The learner knowledge acquisition data are visualized on knowledge acquisition surface. This surface is calculated from the set of artificial data. The experimental data are positioned in curves called “telecides”. The presented telcide of Basic Business course delivered for 61 students’ group describes the appropriateness of each course unit to the learning needs of student group. We present also the experimental data on the learning acquisition surface from individual students. Each point corresponds learning acquisition for one student.

Atslēgas vārdi
eLearning, Learning Data, Telecide, User Behavior

Kapenieks, A., Daugule, I., Kapenieks, K., Zagorskis, V., Kapenieks, J., Timšāns, Ž., Vītoliņa, I. Knowledge Acquisition Data Visualization in eLearning Delivery. No: CSEDU 2020 : Proceedings of the 12th International Conference on Computer Supported Education. Vol.2, Austrija, Vienna, 2.-4. maijs, 2020. [S.l.]: SciTePress, 2020, 507.-513.lpp. ISBN 978-989-758-417-6. Pieejams: doi:10.5220/0009803505070513

Publikācijas valoda
English (en)
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