A Fuzzy Model for Enhanced Student Evaluation

Σαμαράκου, Μαρία; Papadakis, Andreas; Prentakis, Pantelis; Karolidis, Dimitrios; Athineos, Spyros S.

doi:10.18848/1447-9494/cgp/v16i10/46630

Cited by 6 publications

(5 citation statements)

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“…However, these methods are quite limited in term of handling uncertain and imprecise data. Under the paradigm of fuzzy logic, approaches have been proposed such as learner's profile modeling( [14], [15]), evaluation issue ( [16], [17], [18]), learning styles prediction ( [19], [20]), which cover different sides of e-learning systems.…”

Section: Related Workmentioning

confidence: 99%

Personalization of learning situations within a virtual environment for training based on fuzzy clustering

Mohamed¹,

Jakimi²,

Bermi³

2017

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Advances in networks, computers and multimedia technology have changed traditional methods for learning and skills training. Nowadays, Virtual Environments for Training (VET) have been popular, they can provide an environment where virtual reality can be used to create interactive interfaces and real-time software that can control every response and action made by the user. VET have proven to be advantageous to put learners into varied training situations to acquire knowledge and competencies, especially when these situations are taking place in uncontrolled circumstances, or when they are dangerous, unrealizable, or expensive to establish in reality. However individual learners find it difficult to select suitable learning situations for their particular situation because often, there is no personalized service to response to the user needs. Personalization of learning in a VET is a very important way of improving the effectiveness and the quality of the training, yet it also a complex process that requires consideration of several factors such as learners profiles. The goal is to associate suitable learning situations to each learner based on his profile. However, personalization of learning becomes an issue with the uncertainty and imprecision of data that may contains a learner profile. To address this issue, this study is an attempt to integrate a fuzzy clustering into the process of the personalization of learning.

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Section: Related Workmentioning

confidence: 99%

Personalization of learning situations within a virtual environment for training based on fuzzy clustering

Mohamed¹,

Jakimi²,

Bermi³

2017

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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“…The operation of these subsystems is imperceptible by the users. The profiling, modelling and evaluation of the learners is being performed through the use of artificial intelligence and, specifically, fuzzy logic (Samarakou et al, 2009;Chrysafiadi & Virvou, 2012). Detailed information on the five subsystems of StuDiAsE may also be found in (Samarakou et al, 2014).…”

Section: System Architecturementioning

confidence: 99%

“…This is especially problematic in the case of engineering education, where the student classification and assessment should not be based on the result of text activities only. However, more advanced assessment algorithms do exist for adaptive learning environments today, which can be used to develop innovative and formidable learning tools (Tsai, Tseng, & Lin, 2001;Samarakou, Papadakis, Prentakis, Karolidis, & Athineos, 2009). StuDiAsE has been designed with engineering students in mind and is capable of monitoring their comprehension, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate a learner's performance both quantitatively and qualitatively through artificial intelligence (Tsaganou, Grigoriadou, & Cavoura, 2004;Grigoriadou & Tsaganou, 2005;Samarakou et al, 2013b).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an intelligent open learning system for engineering education

Σαμαράκου

Fylladitakis

Karolidis

et al. 2016

Knowledge Management &Amp; E-Learning: An International Journal

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In computer-assisted education, the continuous monitoring and assessment of the learner is crucial for the delivery of personalized education to be effective. In this paper, we present a pilot application of the Student Diagnosis, Assistance, Evaluation System based on Artificial Intelligence (StuDiAsE), an open learning system for unattended student diagnosis, assistance and evaluation based on artificial intelligence. The system demonstrated in this paper has been designed with engineering students in mind and is capable of monitoring their comprehension, assessing their prior knowledge, building individual learner profiles, providing personalized assistance and, finally, evaluating a learner's performance both quantitatively and qualitatively by means of artificial intelligence techniques. The architecture and user interface of the system are being exhibited, the results and feedback received from a pilot application of the system within a theoretical engineering course are being demonstrated and the outcomes are being discussed.

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“…The operation of these subsystems is imperceptible by the learners, as StuDiAsE provides personalized educational material and support based on the profile and performance of the learner. The profiling, modelling and evaluation of the learners is being performed by the use of artificial intelligence and, specifically, fuzzy logic [32][33][34]. Using artificial intelligence and exploiting the data logged during the educational process, StuDiAsE is capable of deriving personalized learner profiles.…”

Section: Paper An Advanced Elearning Environment Developed For Enginementioning

confidence: 99%

An Advanced eLearning Environment Developed for Engineering Learners

Σαμαράκου¹,

Fylladitakis²,

Früh³

et al. 2015

Int. J. Emerg. Technol. Learn.

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Abstract-Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented.

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A Fuzzy Model for Enhanced Student Evaluation

Cited by 6 publications

References 0 publications

Personalization of learning situations within a virtual environment for training based on fuzzy clustering

Personalization of learning situations within a virtual environment for training based on fuzzy clustering

Evaluation of an intelligent open learning system for engineering education

An Advanced eLearning Environment Developed for Engineering Learners

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