Swarm-based sequencing recommendations in e-learning

Berg, Bert van den; Es, René van; Tattersall, Colin; Janssen, José; Manderveld, Jocelyn; Brouns, Francis; Kurvers, Hub; Koper, Rob

doi:10.1109/isda.2005.88

Cited by 24 publications

(10 citation statements)

References 15 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Learning Path produced by the system was: HTML Basis, Line Breaks, Paragraphs, Ordered List, Unordered List, Headings. In this execution example a Cognitive State was used with every concept associated with the value h0, 0i and a Learning Preferences module with default values for the preferred learning categories (5). Note that, despite the fact that Text Formatting is atomic (see Definition 2, Sect.…”

Section: A Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Adaptive course generation through learning styles representation

Sangineto

Capuano

Gaeta

et al. 2007

Univ Access Inf Soc

113

View full text Add to dashboard Cite

This paper presents an approach to automatic course generation and student modeling. The method has been developed during the European funded projects Diogene and Intraserv, focused on the construction of an adaptive e-learning platform. The aim of the platform is the automatic generation and personalization of courses, taking into account pedagogical knowledge on the didactic domain as well as statistic information on both the student's knowledge degree and learning preferences. Pedagogical information is described by means of an innovative methodology suitable for effective and efficient course generation and personalization. Moreover, statistic information can be collected and exploited by the system in order to better describe the student's preferences and learning performances. Learning material is chosen by the system matching the student's learning preferences with the learning material type, following a pedagogical approach suggested by Felder and Silverman. The paper discusses how automatic learning material personalization makes it possible to facilitate distance learning access to both able-bodied and disabled people. Results from the Diogene and Intraserv evaluation are reported and discussed

show abstract

Section: A Case Studymentioning

confidence: 99%

“…Koper et al [5,25] emulate the behaviour of a swarm of insects which are able to self-organize a path by means of the aggregation of multiple pheromone tracks. In fact, the authors propose to represent in a transition matrix the most frequent transitions made by previous learners from every LO A to every LO B of the system's repository.…”

Section: Introductionmentioning

confidence: 99%

Adaptive course generation through learning styles representation

Sangineto

Capuano

Gaeta

et al. 2007

Univ Access Inf Soc

113

View full text Add to dashboard Cite

show abstract

“…These theories have been applied in Social Navigation systems [22,55] to guide people such as learners, to relevant information using the data from previous interactions with the system. Unlike these approaches, which suggest that the user follow trails taken by the majority, in the Implicit Culture approach the user's actions are compared with actions of the whole community and not necessarily the most popular ones are suggested.…”

Section: Swarm Intelligencementioning

confidence: 99%

Implicit: a multi-agent recommendation system for web search

Birukou

Blanzieri

Giorgini

2010

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

For people with non-ordinary interests, it is hard to search for information on the Internet because search engines are impersonalized and are more focused on "average" individuals with "standard" preferences. In order to improve web search for a community of people with similar but specific interests, we propose to use the implicit knowledge contained in the search behavior of groups of users. We developed a multi-agent recommendation system called Implicit, which supports web search for groups or communities of people. In Implicit, agents observe behavior of their users to learn about the "culture" of the community with specific interests. They facilitate sharing of knowledge about relevant links within the community by means of recommendations. The agents also recommend contacts, i.e., who in the community is the right person to ask for a specific topic. Experimental evaluation shows that Implicit improves the quality of the web search in terms of precision and recall.

show abstract

“…This property can be very useful for virtual learning. In a virtual learning environment considered by [17], learners' interactions with learning resources and activities are recorded automatically as they progress through a body of knowledge. The time stamping of these interactions allows learning sequences to be identified which can be processed and aggregated to derive a given "pheromone strength" favoring paths along which more learners have been successful.…”

Section: Exploring Emergence For Virtual Learningmentioning

confidence: 99%

Learning about Learners: System Learning in Virtual Learning Environment

Chen

2008

INT J COMPUT COMMUN

View full text Add to dashboard Cite

Editorial Note: The program committee of ICVL 2007 sent to IJCCC nine of the best papers presented at the conference, with the recommendation to be published in an extended form. After a new evaluation, our reviewers decided that five of those papers can be published in IJCCC, two of which in this issue.Abstract: Virtual learning is not just about a set of useful IT tools for learning. From an examination on where virtual learning stands in the overall learning spectrum, we point out the important impact of natural computing on virtual learning. We survey and analyze selected literature on important role of natural computing aspects, such as emergence (using swarm intelligence to achieve collective intelligence) and emotion, to virtual learning. In addition, in order to effectively incorporate these aspects into virtual learning, we propose using infrastructural support for virtual learning through system learning: The virtual learning environment not only provides facilities for learners, but also observes the behavior of learners and takes actions, so that its own performance can be improved (i.e., to better serve the learners). In this sense, system learning is concerned with learning about learners. Consequently, a virtual learning environment is a true human-machine symbiosis, paired by human learning and system learning.

show abstract

Swarm-based sequencing recommendations in e-learning

Abstract: Open and distance Learning (ODL)

Cited by 24 publications

References 15 publications

Adaptive course generation through learning styles representation

Adaptive course generation through learning styles representation

Implicit: a multi-agent recommendation system for web search

Learning about Learners: System Learning in Virtual Learning Environment

Contact Info

Product

Resources

About