Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Educational online social networking in Greece: A case study of a Greek educational online social network

Over the past few years, the rate of Web 2.0 diffusion has been dramatic. Social media and emerging technologies such as wikis, blogging, online gaming, tagging and social networking have spread widely and rapidly and are gaining increased attention for use in education. Finding coherence in the midst of rapid changes is very difficult. This study attempts to contribute to an on-going dialogue about the trends and implications of online social networking in education and especially in lifelong learning. The paper provides an overview of the current status of educational online social networking in Greece and presents a case study of the Greek Educational Online Social Network (EOSN) "Logo in Education: a learning community of practice" (http://logogreekworld.ning.com/). The specific EOSN has been created in late May 2009 by an independent initiative of an educator with the purpose to facilitate communications and interactions, exchange of information, ideas and educational material and to promote co-operation and collaboration between the members of the educational community mostly interested in Logo programming language and philosophy. In this paper we describe main features of the EOSN; discuss key aspects, general implementation details, changes and developments and conclude with considerations for future work. © Common Ground, Katerina Glezou, Maria Grigoriadou, Maria Samarakou

Supporting knowledge sharing with community-driven technologies: The case of CRICOS

The notion of communities has been widely studied and recognized as an increasingly important social structure for creating, sharing and applying knowledge in organizational and educational settings. With the advent of Web 2.0 a wide range of community driven technologies has greatly enhanced the development of online communities. In this paper we present CRICOS, a web-based environment which enhances knowledge discovery and sharing within interconnected communities by integrating several community-driven technologies such as collaborative filtering, social navigation and social tagging. The communities are evolved in the system as their members create and improve both the content and the structure of their information spaces. The paper describes the facilities provided to the users by CRICOS and presents an empirical study of the use of the web-based environment in a university course. The results from the empirical study are encouraging regarding the usefulness and the usability of the provided facilities and revealed the students' positive attitude to the CRICOS web-based environment. © Common Ground, Stefanos Ziovas, Maria Grigoriadou, Maria Samarakou

Comparison results of two optimization techniques for a combined wind and solar power plant

Two optimization techniques have been tested on an hour‐by‐hour simulation of a combined wind and solar power plant. The system also includes a battery storage system as well as a group of diesel generators. The two optimization techniques are: simplex from the package of MINUITS written at CERN and a modified steepest descent algorithm. Both techniques are suited to hour‐by‐hour simulation for the above system since the function being minimized is monotonically decreasing towards a minimum. The comparison results showed that the steepest descent algorithm converges slightly faster than the simplex one. Moreover, the application of the techniques for two different sites with different load profiles let us conclude that the results are stable. Copyright © 1988 John Wiley & Sons, Ltd

A neuro-fuzzy approach to detect student's motivation

In this paper the fuzzy knowledge representation of a neural network-based fuzzy model is presented. The model is used to assess student's motivational state in a discovery learning environment. Student's observable behavior and motivational factors are described with linguistic variables. The inputs of the model are tailored from real students' data, with the assistance of a group of expert teachers. Results of our preliminary study were encouraging, since data obtained from real students' log files, have been successfully used to form the membership functions that assign membership degrees to the linguistic values of the linguistic variables

Enhancing web document accessibility by authoring texts and text comprehension activities

In this paper we discuss recent directions concerning the structural analysis of science documents and cognitive aspects of document elements aiming at document comprehension. Structural analysis of documents, according to text comprehension theory, promotes document understanding and enhances universal accessibility of documents. We outline the process of structuring science documents and activities for comprehension using the authoring tool ReTuDiSAuth. This process improves universal accessibility of document supporting authors to structure text and text activities for students with different abilities, requirements and preferences. © 2009 Springer Berlin Heidelberg

Survey on sound and video analysis methods for monitoring face-to-face module delivery

The objective of this work is to identify unobtrusive methodologies that allow the monitoring and understanding of the educational environment, during face-to-face activities, through capturing and processing of sound and video signals. It is a survey on applications and techniques that exploit these two signals (sound and video) retrieved in classrooms, offices and other spaces. We categorize such applications based upon the high level characteristics extracted from the analysis of the low level features of the sound and video signals. Through the overview of these technologies, we attempt to achieve a degree of understanding of the human behavior in a smart classroom, on behalf of the students and the teacher. Additionally, we illustrate openresearch points for further investigation. © 2019, Kassel University Press GmbH

Monitoring students' actions and using teachers' expertise in implementing and evaluating the neural network-based fuzzy diagnostic model

In this paper, the implementation of a neural network-based fuzzy modeling approach to assess aspects of students' learning style in the discovery learning environment "Vectors in Physics and Mathematics" is presented. Fuzzy logic is used to provide a linguistic description of students' behavior and learning characteristics, as they have been elicited from teachers, and to handle the inherent uncertainty associated with teachers' subjective assessments. Neural networks are used to add learning and generalization abilities to the fuzzy model by encoding teachers' experience through supervised neural-network learning. The neural network-based fuzzy diagnostic model is a general diagnostic model which is implemented in an Intelligent Learning Environment by eliciting teachers' expertise regarding students' characteristics based on real students' observation and on data being collected from students' interaction. The model has been successfully implemented, trained and tested in the learning environment "Vectors in Physics and Mathematics" by using the recommendations of a group of five experienced teachers. The performance of our model in real classroom conditions has been evaluated during an experiment with an experienced Physics teacher and 49 students of secondary school attending Physics lessons. © 2006 Elsevier Ltd. All rights reserved

Using simulated students for machine learning

In this paper we present how simulated students have been generated in order to obtain a large amount of labeled data for training and testing a neural network-based fuzzy model of the student in an Intelligent Learning Environment (ILE). The simulated students have been generated by modifying real students' records and classified by a group of expert teachers regarding their learning style category. Experimental results were encouraging, similar to experts' classifications. © Springer-Verlag 2004