Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12540/462
Title: Pre-assessment and learning recommendation mechanism for a multi-agent system
Authors: Ehimwenma, Kennedy E. 
Beer, Martin 
Crowther, Paul 
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Source: Ehimwenma, K., Beer, M., & Crowther, P. (2014). Pre-assessment and learning recommendation mechanism for a multi-agent system. 2014 IEEE 14th International Conference on Advanced Learning Technologies, 122-123.
Journal: 2014 IEEE 14th International Conference on Advance Learning Technologies 
Conference: 2014 IEEE 14th International Conference on Advance Learning Technologies 
Abstract: Diagnostic assessment is a vital and effective strategy in any teaching-learning process such that it provides a pre-learning assessment of the learners state of knowing with regard to a given knowledge concept. Current intelligent learning systems still do not integrate effective techniques for evaluating prior knowledge that can be used effectively to diagnose gaps that will inhibit future learning and for making recommendations for learning and tutoring to fill them. In this paper, we present a mechanism for pre-assessment of previous learning upon which the recommendation for a new or appropriate learning level is based. Our approach is based on message passing procedure between agents in a multi-agent system. We have tested the pre-assessment technique with a prototype based on the Jason AgentSpeak language, and using learning materials from a structured query language (SQL) revision module.
URI: https://hdl.handle.net/20.500.12540/462
DOI: 10.1109/ICALT.2014.43
Appears in Collections:Scholarly Publications

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