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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Our project is motivated by the early drop out and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer granularity information to make more accurate predictions of dropout and performance. The results show that adding final-grained temporal or non-temporal information into behaviour features provides more predictive power in the early phases of a POSA MOOC. As a next step, we plan to determine if we could use these features to better profile students with unsupervised learning methods.

Early Prediction of Student Dropout and Performance in MOOCs using Higher Granularity Temporal Information