The Next Generation eLearning Platform for Computing Education
eLearning Platform - Web System - Data Analysis


The eLearning platform aims to provide students a collaborative programming environment with different features for them to be able to learn better on programming and computing. It can analyze data and deliver timely feedback to provide various aids to students.


  • Target: Students in the course COMP2123 Programming Technologies and Tools

  • Programming Language: C++

  • Data analysis focus points: classifying student's performance on passing the test cases

  • Examples of evaluation metrics for student performance are time elapsed in development and number of test cases passed


  • Majority of eLearning platforms focus on learning resources distribution and assignment submission

  • No specialized platform available for programming and computing education

  • New platform supports collaborative programming and various coding support

  • Student could be expected to learn programming in self-learning manner with the aid of the platform or from peers

Web system

Web system and user interface which is the frontend part of the platform are provided for users to interact with the platform.

Here is the structure of the online eLearning Platform:

Data Analytics

The system will use data analytics for classifying test cases into different categories, which corresponds to different programming concepts.

Having the ability to categorize test cases allows pinpointing the weakness of student and giving corresponding suggestions to student.

Below are the details of it:



Dr. Chui Chun Kit


Cai Tung San


Chan Wai Lun


Cheung Siu Wai


Wong Ching Quen



Date Progress

August, 2016

Project Planning and literature review.


Build user interface and basic functions of the eLearning platform including user login system, place for viewing assignment, place for compiling and executing codes, display basic cases results upon execution of codes and storage of result into the database.



Implement more features to the platform including group allocation and group chat, design diagram and discussion forum. Create training and test dataset for preprocessing of machine learning. Design and implementation of the predicative models and algorithms decided.


January, 2017

Fine tune and testing to make sure the system is bug-free before being used by students.


The system should be ready. Students of COMP2123 can use the system in their class and data should be collected. Data collected should be used for fine tuning the system.



Prepare for final report and presentation

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