ML Framework for Efficient Assessment and Prediction of Human Performance in Collaborative Learning Environments

Abstract

Collaborative learning methods have been implemented broadly by organizations at all stages, as research recommends that active human involvement in cohesive and micro group communications is critical for effective learning. In current research, an important line of inquiry focuses on finding accurate evidence and valid assessment of these micro-level interactions which supports collaborative learning. Even though there is a long practice of using mathematical models for modeling human behavior, Cipresso (2015) introduced a computational psychometrics-based method for modeling characteristics of real behavior. Cipresso’s article provides us with a way to extract dynamic interaction features from multimodal data for modeling and analyzing actual situations. The objective of this work is to propose a machine learning-based methodology system architecture and algorithms to find patterns of learning, interaction, and relationship and effective assessment for a complex system involving massive data that could be obtained from a proposed collaborative learning environment (CLE). Collaborative learning may take place between dyads or larger team members to find solutions for real time events or problems, and to discuss concepts or interactions during situational judgment tasks (SJT). Modeling a collaborative, networked system that involves multimodal data presents many challenges. This paper focuses on proposing a Machine Learning - (ML)-based system architecture to promote understanding of the behaviors, group dynamics, and interactions in the CLE. Our framework integrates techniques from computational psychometrics (CP) and deep learning models that include the utilization of convolutional neural networks (CNNs) for feature extraction, skill identification, and pattern recognition. Our framework also identifies the behavioural components at a micro level and can help us model behaviors of a group involved in learning.