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

With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), ahuge amount of data has been collected related to students' learning. With thecareful analysis of this data, educators can gain useful insights into theperformance of their students and their behavior in learning a particulartopic. This paper proposes a new model for predicting student performance basedon an analysis of how students interact with an interactive online eTextbook.By being able to predict students' performance early in the course, educatorscan easily identify students at risk and provide a suitable intervention. Weconsidered two main issues the prediction of good/bad performance and theprediction of the final exam grade. To build the proposed model, we evaluatedthe most popular classification and regression algorithms on data from a datastructures and algorithms course (CS2) offered in a large public researchuniversity. Random Forest Regression and Multiple Linear Regression have beenapplied in Regression. While Logistic Regression, decision tree, Random ForestClassifier, K Nearest Neighbors, and Support Vector Machine have been appliedin classification.

A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook