Pre-assessment and learning recommendation mechanism for a multi-agent system

EHIMWENMA, Kennedy, BEER, Martin and CROWTHER, Paul (2014). Pre-assessment and learning recommendation mechanism for a multi-agent system. In: IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014, Athens, Greece, 7-9 July 2014. 122-123.

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Official URL: http://dx.doi.org/10.1109/ICALT.2014.43
Link to published version:: 10.1109/ICALT.2014.43

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 Agent Speak language, and using learning materials from a structured query language (SQL) revision module.

Item Type: Conference or Workshop Item (Paper)
Research Institute, Centre or Group: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Identification Number: 10.1109/ICALT.2014.43
Depositing User: Hilary Ridgway
Date Deposited: 08 Dec 2014 10:30
Last Modified: 09 Nov 2016 16:50
URI: http://shura.shu.ac.uk/id/eprint/8950

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