E-Learning and Personalized Learning Path: A Proposal Based on the Adaptive Educational Hypermedia System

The E-Learning is becoming an effective approach for the improving of quality of learning. Many institutions are adopting this approach both to improve their traditional courses both to increase the potential audience. In the last period great attention is paid in the introduction of methodologies and techniques for the adaptation of learning process to the real needs of students. In this scenario the Adaptive Educational Hypermedia System can be an effective approach. Adaptive hypermedia is a promising area of research at the crossroads of hypermedia and adaptive systems. One of the most important fields where this approach can be applied is just the e-Learning. In this context the adaptive learning resources selection and sequencing is recognized as among one of the most interesting research questions. An Adaptive Educational Hypermedia System is composed by services for the management of the Knowledge Space, the definition of a User Model, the observation of student during his learning period and, as previously said, the adaptation of the learning path according to the real needs of the students. In particular the use of ontologyâ??s formalism for the modeling of the â??knowledge spaceâ? related to the course can increase the sharable of learning objects among similar courses or better contextualize their role in the course. This paper addresses the design problem of an Adaptive hypermedia system by the definition of methodologies able to manage each its components, In particular an original user, learning contents, tracking strategies and adaptation model are developed. The proposed Adaptive Educational Hypermedia System has been integrated in an e-Learning platform and an experimental campaign has been conducted. In particular the proposed approach has been introduced in three different blended courses. A comparison with traditional approach has been described and the obtained results seem to be very promising

Improving relevance feedback-based query expansion by the use of a weighted word pairs approach

In this article, the use of a new term extraction method for query expansion (QE) in text retrieval is investigated. The new method expands the initial query with a structured representation made of weighted word pairs (WWP) extracted from a set of training documents (relevance feedback). Standard text retrieval systems can handle a WWP structure through custom Boolean weighted models. We experimented with both the explicit and pseudorelevance feedback schemas and compared the proposed term extraction method with others in the literature, such as KLD and RM3. Evaluations have been conducted on a number of test collections (Text REtrivel Conference [TREC]-6, -7, -8, -9, and -10). Results demonstrated that the QE method based on this new structure outperforms the baseline

“Magic mirror in my hand, what is the sentiment in the lens?”: An action unit based approach for mining sentiments from multimedia contents

In psychology and philosophy, emotion is a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. Emotion could be also considered as a “positive or negative experience” that is associated with a particular pattern of physiological activity. So, the extraction and recognition of emotions from multimedia contents is becoming one of the most challenging research topics in human–computer interaction. Facial expressions, posture, gestures, speech, emotive changes of physical parameters (e.g. body temperature, blush and changes in the tone of the voice) can reflect changes in the user's emotional state and all this kind of parameters can be detected and interpreted by a computer leading to the so-called “affective computing”. In this paper an approach for the extraction of emotions from images and videos will be introduced. In particular, it involves the adoption of action units' extraction from facial expression according to the Ekman theory. The proposed approach has been tested on standard and real datasets with interesting and promising results

Learning to Classify Text Using a Few Labeled Examples

It is well known that supervised text classification methods need to learn from many labeled examples to achieve a high accuracy. However, in a real context, sufficient labeled examples are not always available. In this paper we demonstrate that a way to obtain a high accuracy, when the number of labeled examples is low, is to consider structured features instead of list of weighted words as observed features. The proposed vector of features considers a hierarchical structure, named a mixed Graph of Terms, composed of a directed and an undirected sub-graph of words, that can be automatically constructed from a set of documents through the probabilistic Topic Model

Sentiment detection in social networks and in collaborative learning environments

Daily millions of messages appear on the web, which is becoming a rich source of data for opinion mining and sentiment analysis. The computational study of opinions, feelings and emotions expressed in a text often relates to the identification of agreement or disagreement with statements, contained in comments or reviews, that convey positive or negative feelings. The detection and analysis of sentiment in textual communication is a topic attracting attention also in the context of collaborative learning in social networks, being learners actively engaged in presenting and defending ideas and opinions, as well as exchanging moods about courses with peers. In this paper, we investigate the adoption of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment Grabber. Through this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. The proposed method has been tested in different context: a standard dataset containing movie reviews; a real-time analysis of social networks posts; a collaborative learning scenario. The experimental evaluation shows how the proposed approach is effective and satisfactory

A Context-Aware Mobile Solution for Assisting Tourists in a Smart Environment

The cultural heritage of the italian territory is an impressive breadth of resources, unfortunately still little known today. Such a cultural heritage should be valued for the purpose is to better understand the Italian citizens their cultural identity, is to make known to the citizens of other nations the history of the place, with its story and its characters, and the life of the inhabitants, with their own traditions and customs. \ \ In this paper, it is introduced an adaptive Context-Aware app able to collect not-structured data, belonging to heterogeneous sources and develop tailored recommendations for the user, in order to support a tourist inside a town. The solution found takes advantage of information technologies, like Internet of Thing and Internet of Services and the objective is reached through the use of a system of description of the context through a graphical formalism named Context Dimension Tree. \ \ The system described was implemented in the city of Salerno in Italy and the results of a questionnaire distributed to the users show great appreciation

Germanium-on-Glass solar cells: fabrication and characterization

We report on Germanium on Glass solar cells realized by wafer bonding, layer splitting and epitaxial regrowth. We provide a detailed description of the layer transfer process and discuss the material characterization. The solar cells are fabricated and tested to extract the most significant figures of merit, evaluating their performance versus device area and operating temperature. The cells exhibit typical conversion efficiencies exceeding 2.4% under AM1.5 irradiation and a maximum efficiency of 3.7% under concentrated excitation. This Germanium on Glass approach is promising in terms of added flexibility in multi-junction engineering and allows a significant cost reduction thanks to the re-usability of the Ge substrates

Guided-wave photodetectors in germanium on optical chips in silicon-on-insulator

We report the integration of near-infrared waveguide photodetectors in polycrystalline Germanium with Silicon on Insulator optical chips for power monitor applications. We discuss design and fabrication of the devices as well as their characterization in terms of dark current, responsivity and signal to noise ratio