A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories

Due to the COVID-19 pandemic and the development of educational technology, e-learning has become essential in the educational process. However, the adoption of e-learning in sectors such as engineering, science, and technology faces a particular challenge as it needs a special Laboratory Learning Management System (LLMS) capable of supporting online lab activities through virtual and controlled remote labs. One of the most challenging tasks in designing such LLMS is how to assess a student’s performance while an experiment is being conducted and how stuttering students can be automatically detected while experimenting and providing the appropriate assistance. For this, a generic technique based on Artificial Intelligence (AI) is proposed in this paper for assessing student performance while conducting online labs and implemented as a performance evaluation module in the LLMS. The performance evaluation module is designed to automatically detect the student performance during the experiment run time and triggers the LLMS virtual assistant service to provide struggling students with the appropriate help when they need it. Also, the proposed performance assessment technique is used during the lab exam sessions to support the automatic grading process conducted by the LLMS Auto-Grading Module. The proposed performance evaluation technique has been developed based on analyzing the student’s mouse dynamics to work generally with any type of simulation or control software used by virtual or remote controlled laboratories; without the need for special interfacing. The study has been applied to a novel dataset built by the course instructors and students simulating a circuit on TinkerCad. Using mouse dynamics fetching, the system extracts features and evaluates them to determine if the student has built the experiment steps in the right way or not. A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. The study confirmed that Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the best models to be used for automatically evaluating student performance while conducting the online labs with a precision reaching up to 91%.