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Use Case

Predicting User Engagement in Online Courses

Analyzing factors driving course engagement to improve student outcomes and learning experiences.

1Overview & Strategic Importance

Predicting User Engagement in Online Courses
Classification Solution EdTech Data

Problem Statement

E-learning platforms face significant challenges in understanding and improving course completion rates. Many users begin courses but fail to complete them, leading to dissatisfaction and lower platform credibility. Key factors like time spent, engagement with videos and quizzes, and user demographics play a critical role. This issue is pivotal for platforms aiming to improve learner outcomes and maintain a competitive edge in the education technology industry.

Required Solutions

  • Analyzing relationships between engagement metrics and completion rates.
  • Identifying high-risk users and factors contributing to non-completion.
  • Generating insights to optimize course design and support strategies.

Solution Objectives

  • Perform exploratory data analysis to uncover trends impacting completion.
  • Build a classification model to predict `CourseCompletion`.
  • Identify optimal engagement strategies for improving completion rates.
  • Provide recommendations for personalized learning pathways.

Understanding the Problem

Online course completion is influenced by content, user engagement, and accessibility.

Understanding these dynamics can help platforms develop personalized interventions, such as timely reminders and engaging content, to enhance satisfaction and long-term loyalty.

2About the Data

Data Collection

The dataset originates from an online course platform, capturing user activity, engagement patterns, and completion statuses. Methods include tracking interactions, quiz scores, and time spent.

Dataset Source

Major Parameters Description

Download Training Data
UserID

Unique identifier for each user.

CourseCategory

Category of the course (e.g., Programming, Business, Arts).

TimeSpentOnCourse

Total time spent by the user on the course in hours.

NumberOfVideosWatched

Total number of videos watched by the user.

NumberOfQuizzesTaken

Total number of quizzes taken by the user.

QuizScores

Average scores achieved by the user in quizzes (percentage).

CompletionRate

Percentage of course content completed by the user.

DeviceType

Type of device used (Desktop = 0, Mobile = 1).

CourseCompletion

Target variable indicating course completion status (0 = Not Completed, 1 = Completed).

3Using iDareAI

Guided Mode Initialization

AUploading Dataset

Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `course_engagement_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.

Upload UI

BChoosing Analysis Mode

Choose between autonomous machine learning or manual building. In autonomous mode, simply ask a question like:
  • What changes can improve course completion based on the patterns in this data?
  • Which factors in the data have the biggest impact on course completion?

Operation Using Autonomous Guided Mode

AQuery Response

The most significant factors are 'NumberOfQuizzesTaken', 'NumberOfVideosWatched', and 'TimeSpentOnCourse'. Students who engage more with quizzes are more likely to complete courses.

Auto Analysis

BAI Application

The AI application allows users to explore different scenarios by adjusting predictor values, enabling them to understand how changes in engagement affect completion.

Auto Application

Model Fine-Tuning/Manual Model Building

ASelecting Prediction Target

'CourseCompletion' column was selected as the target.

Target Selection

BSelecting Analysis Type

The analysis target is a categorical column. Hence, the 'Classification' analysis type is selected.

Analysis Type

CSelecting Model Group/Item

Model Group

DSelecting Features

Select all relevant engagement and demographic features.

Feature Selection

ESelecting Training Level

The "Slow" training level with "High Performance" configuration was selected for thorough analysis.

Training Level

AI Modeling Details

XGB is considered the best ML model for course engagement, offering the highest accuracy and lowest possible error percentages.

Modeling Details

Training Analysis Details

APredicted Target

Predicted Target

BROC AUC

ROC AUC

CError Trend

Error Trend

DFeature Importance

Feature Importance

Finalize Models

Once satisfied with performance, click 'Deploy'. The system saves and deploys models for real-time engagement monitoring.

Finalize Models

4AI APPLICATION

Manual Model Building

In Manual Training Mode, users can modify sliders for variables like TimeSpentOnCourse, CompletionRate, and NumberOfQuizzesTaken. The output directly corresponds to the selected feature values.

Manual App

AI Application Demo

High values of TimeSpentOnCourse, CompletionRate, and NumberOfQuizzesTaken yield to positive course completion predictions.

Saving the Project

Save your project by clicking the icon at the bottom left corner of the textbox.

Saving

Sharing the Project

Share the application for single on-demand predictions once the analysis is saved.

Sharing

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