APLe: Agents for Personalized Learning in Distance Learning

Panagiotis, Stamatis; Panagiotopoulos, Ιoannis; Goumopoulos, Christos; Kameas, Achilles

doi:10.1007/978-3-319-29585-5_3

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Structured representations capture relationships between important domain concepts. This often entails using an existing ontology, for example, ACM taxonomy (Nasraoui and Zhuhadar, 2010;Ruiz-Iniesta, Jimenez-Diaz, & Gomez-Albarran, 2014), or creating a new one (Gherasim, Harzallah, Berio, & Kuntz, 2013;Panagiotis, Ioannis, Christos, & Achilles, 2016). Although ontologies are designed to have a good coverage of their domains, the output is still dependent on the view of its builders, and, because of handcrafting, existing ontologies cannot easily be adapted to new domains.…”

Section: Related Workmentioning

confidence: 99%

Improving e‐learning recommendation by using background knowledge

2018

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

Improving e‐learning recommendation by using background knowledge

2018

View full text Add to dashboard Cite

show abstract

“…This often entails using an existing ontology [11,15], or creating a new one [12]. Although ontologies are designed to have a good coverage of their domains, the output is still dependent on the view of its builders, and because of handcrafting, existing ontologies cannot easily be adapted to new domains.…”

Section: Related Workmentioning

confidence: 99%

Harnessing Background Knowledge for E-Learning Recommendation

Mbipom

Craw

Massie

2016

Research and Development in Intelligent Systems XXXIII

View full text Add to dashboard Cite

Section 6 of the "Repository policy for OpenAIR @ RGU" (available from http://www.rgu.ac.uk/staff-and-currentstudents/library/library-policies/repository-policies) provides guidance on the criteria under which RGU will consider withdrawing material from OpenAIR. If you believe that this item is subject to any of these criteria, or for any other reason should not be held on OpenAIR, then please contact openair-help@rgu.ac.uk with the details of the item and the nature of your complaint. Abstract The growing availability of good quality, learning-focused content on the Web makes it an excellent source of resources for e-learning systems. However, learners can find it hard to retrieve material well-aligned with their learning goals because of the difficulty in assembling effective keyword searches due to both an inherent lack of domain knowledge, and the unfamiliar vocabulary often employed by domain experts. We take a step towards bridging this semantic gap by introducing a novel method that automatically creates custom background knowledge in the form of a set of rich concepts related to the selected learning domain. Further, we develop a hybrid approach that allows the background knowledge to influence retrieval in the recommendation of new learning materials by leveraging the vocabulary associated with our discovered concepts in the representation process. We evaluate the effectiveness of our approach on a dataset of Machine Learning and Data Mining papers and show it to outperform the benchmark methods.

show abstract