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

Across the vast majority of educational data mining research, models are evaluated based on their predictive accuracy. Most often, this takes the form of assessing the model’s ability to correctly predict successes and failures in a set of student response outcomes. Much less commonly, models may be validated based on their ability to predict post-test outcomes (e.g., Corbett & Anderson, 1995) or pre-test/post-test gains (e.g., Liu & Koedinger, 2015).

Going Beyond Better Data Prediction to Create Explanatory Models of Educational Data