| Title |
A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system
|
|---|---|
| Published in |
SpringerPlus, March 2013
|
| DOI | 10.1186/2193-1801-2-81 |
| Pubmed ID | |
| Authors |
Konstantina Chrysafiadi, Maria Virvou |
| Abstract |
In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging. |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 2% |
| Ecuador | 1 | 2% |
| Unknown | 53 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 35% |
| Student > Master | 8 | 15% |
| Lecturer | 5 | 9% |
| Professor | 5 | 9% |
| Other | 4 | 7% |
| Other | 13 | 24% |
| Unknown | 1 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 30 | 55% |
| Engineering | 9 | 16% |
| Medicine and Dentistry | 4 | 7% |
| Business, Management and Accounting | 3 | 5% |
| Mathematics | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 2 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 March 2013.
All research outputs
#20,184,694
of 22,699,621 outputs
Outputs from SpringerPlus
#1,461
of 1,852 outputs
Outputs of similar age
#171,043
of 194,736 outputs
Outputs of similar age from SpringerPlus
#65
of 126 outputs
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