Quantification of Learner Characteristics for Collaborative Agent based e-learning Automation

Objectives: The objective of this research work is to analyze on learner characteristics like active, reflective, group or solo learning qualities called 'learner portfolios' that are to be analyzed by a collaborative agent in an e-learning environment. Methods: This research work proposes a methodology that can determine e-learner characteristics from respective user profiles and interact with any adaptive e-learning system in an asynchronous mode. Since most of the learner characteristics are subjective, with literature support, this research work presents social survey results. This research work also introduces a collaborative agent based model for correlating learner characteristics. Findings: Adaptive e-learning systems employ software agents in asynchronous modes for various non-dependent process activities. As software agents act independently, such integrated approaches are expected to increase the efficiency. Literatures on collaborative agents that deal with learner portfolios of subjective attributes are very rare to be seen. Application/Improvement: These portfolios, if documented as learner profiles, can help e-learning systems to understand e-learners' behavior better and respond appropriately back to them.