English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The current study explored the relationship between learner profiles and the nature of their interaction with content in massive, open, and online learning environments. The research was conducted on the Anadolu University Open Education System, and data from 597,164 learners enrolled in 86 different degree programs were analyzed by unsupervised machine learning methods. Cluster analysis was used to identify learner profile groups and association rules were applied to identify learner-content interaction patterns. As a result of the analyses, five clusters were obtained, and it was determined that the attribute with the highest discrimination in determining the clusters was the learners’ semester grade point average. The clusters were named according to learner-content interactions and the learners’ semester grade point average. Analysis of the association rules revealed that various learner-content interactions emerged in the context of profile groups.

Relationship between learner profiles and learner–content interaction in online learning: Exploring implications for learning experience design