Title: Relevance ranking metrics for learning objects
Authors: Ochoa, Xavier
Duval, Erik
Issue Date: 2007
Publisher: Springer
Host Document: Lecture Notes in Computer Science vol:4753 pages:262-276
Conference: ECTEL07: European Conference on Technology Enhanced Learning - Creating New Learning Experiences on a Global Scale edition:2 location:Crete, Greece date:17-20 September
Abstract: Technologies that solve the scarce availability of learning ob-
jects have created the opposite problem: abundance of choice. The so-
lution to that problem is relevance ranking. Unfortunately current tech-
niques used to rank learning ob jects are not able to present the user
with a meaningful ordering of the result list. This work interpret the
Information Retrieval concept of Relevance in the context of learning
ob ject search and use that interpretation to propose a set of metrics to
estimate the Topical, Personal and Situational relevance. These metrics
are calculated mainly from usage and contextual information. An ex-
ploratory evaluation of the metrics shows that even the simplest ones
provide statistically signi´Čücant improvement in the ranking order over
the most common algorithmic relevance metric.
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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