A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system

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.

Fuzzy cognitive mapping of LMS users’ Quality of Interaction within higher education blended-learning environment

“…The variety of these problem solving categories reveals the ample space for applicability of the FCM in the area of education. In this vein, the related findings show that: i) insight into the context of educational software adoption in schools could be achieved, which can guide both educational decision-makers and software developers in terms of more appropriate software development efforts (Hossain & Brooks, 2008), ii) support to the online learning community could be provided, by allowing prediction comparisons to be made between numerous tools measured by multiple factors and its relations, so decision makers can be helped to efficiently/effectively select e-learning technologies (Salmeron, 2009), iii) causalities of the education management could be easily understood by linked graph representation (Nownaisin, Chomsuwan, & Hongkrailert, 2012), iv) the success factors of educational organizations could better be understood (Yesil, Ozturk, Dodurka, & Sahin, 2013), v) the assessment of learning on interactive environments could be facilitated (Barón, Crespo, Espada, & Martínez, 2014), vi) learning style could be recognized, by handling the uncertainty and fuzziness of a learning style diagnosis in an efficient way (Georgiou & Botsios, 2008), vii) game-based learning could be promoted (Luo, Wei, & Zhang, 2009), viii) highly participatory scenario frameworks, which involve a blend of qualitative, semi-quantitative, and quantitative methods, could be established, linking stakeholders and modelers in scenario studies (van Vliet, Kok, and Veldkamp, 2010), ix) the domain knowledge could be represented in a more realistic way, allowing the adaptive and/or personalized tutoring system to dynamically deliver the learning material to each individual learner, taking into account his/her learning needs and his/her different learning pace (Chrysafiadi & Virvou, 2013), x) decision-making services could be provided by an intelligent and adaptive web-based educational s stem, provoking learners' traits to adapt lectures to enhance their apprenticeship (Peña-Ayala & Sossa-Azuela, 2014), xi) critical decision-making in medical education could be supported, b exploring extensive "what-if" scenarios in case studies and preparing for dealing with critical adverse events ( eorgopoulos, Chouliara, St lios, ), xii) student's grade evaluation and prediction the forthcoming semesters could achieved (Takács, Rudas, & Lantos, 2014), xiii) stress factors during learning could be identified (Anusha & Ramana, 2015), xiv) making decisions concerning networked learning could be supported from both a static and dynamic perspective (Tsadiras Stamatis,8), xv) a better understanding on students' progress could be offered both to teachers and students via appropriate indicators (Yang, Li, & Lau, 2011), and xvi) modeling and solving decision problems with multiple, con...…”

Fuzzy System Inference and Fuzzy Cognitive Maps for a Cognitive Tutor of Algebra

“…In education, the causality characteristic allows the FCMs to be adequate to represent the dependence between key concepts of some domain of the knowledge in question, allowing to detect the learning material that should be delivered, to some student, with respect to their knowledge level and personal needs [1,10]. For FCM reasoning process, a simple mathematical formulation is usually used.…”

“…Here lies the importance of the representation of the dependency between the key concepts in the knowledge domain to be learned, and of not only using a general representation of knowledge. In other words, FCM must represent how the knowledge of a domain concept of the teaching material, may be affected by the knowledge of another domain concept [1].…”

Automatic evaluation technique for certain types of open questions in semantic learning systems

“…Reference [8] proposed a FCM to determine the concepts dependences. That is by using the network graphic representation.…”

“…The learning objects in the same level of Ontology based are related if they are in the same ontology class. So in proof questions, we can generate weight matrix as in FCM [8]. In correct solution only the weight function calculates weight for rules was used.…”