Monitoring the Text Comprehension of Students for Profiling in ReTuDiS

Σαμαράκου, Μαρία; Tsaganou, Grammatiki; Papadakis, Andreas; Gelegenis, John; Fylladitakis, Emmanouil D.; Grigoriadou, Maria

doi:10.14355/jitae.2013.0204.02

Cited by 2 publications

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“…The tasks of student profiling and personalized feedback are being performed by the profiling subsystem, using fuzzy logic AI and based on the comprehension theory of Denhière and Baudet (Denhière & Baudet, 1992;Samarakou et al, 2013c). Note that even though the system will propose a specific type of text after the completion of the diagnostic text, the learner is free to choose from any type of text available (relational, transformative or teleological).…”

Section: Fig 3 Learner's User Interface Theoretical Sectionmentioning

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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Section: Fig 3 Learner's User Interface Theoretical Sectionmentioning

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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show abstract

“…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%

“…The tasks of learner profiling and personalized feedback are being performed by the profiling subsystem, using fuzzy logic AI and based on the comprehension theory of Denhière & Baudet [27,34]. Note that even though the system will propose a specific type of text after the completion of the diagnostic text, the learner is free to choose from any type of text available (relational, transformative or teleological) [34].…”

Section: ) Learner User Interfacementioning

confidence: 99%

“…Note that even though the system will propose a specific type of text after the completion of the diagnostic text, the learner is free to choose from any type of text available (relational, transformative or teleological) [34]. After selecting and studying a text, the learner can start an assessment activity that is based on the exact type of text he or she just studied.…”

Section: ) Learner User Interfacementioning

confidence: 99%

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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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Monitoring the Text Comprehension of Students for Profiling in ReTuDiS

Cited by 2 publications

References 0 publications

Evaluation of an intelligent open learning system for engineering education

Evaluation of an intelligent open learning system for engineering education

An Advanced eLearning Environment Developed for Engineering Learners

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