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

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

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

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

SAFE: A Sentiment Analysis Framework for E-Learning

The spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption, in the field of the e-learning, of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment grabber. By 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. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/her teaching approach. In fact, the proposed method has been tested on datasets coming from e-learning platforms. A preliminary experimental campaign shows how the proposed approach is effective and satisfactory

Mixed Graph of Terms: Beyond the bags of words representation of a text

The main purpose of text mining techniques is to identify common patterns through the observation of vectors of features and then to use such patterns to make predictions. Vectors of features are usually made up of weighted words, as well as those used in the text retrieval field, which are obtained thanks to the assumption that considers a document as a "bag of words". However, in this paper we demonstrate that, to obtain more accuracy in the analysis and revelation of common patterns, we could employ (observe) more complex features than simple weighted words. 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 small set of documents through the probabilistic Topic Model. The graph has demonstrated its efficiency in a classic "ad-hoc" text retrieval problem. Here we consider expanding the initial query with this new structured vector of features

A Latent Dirichlet Allocation Approach using Mixed Graph of Terms for Sentiment Analysis

The spread of generic (as Twitter, Facebook orGoogle+) or specialized (as LinkedIn or Viadeo) social networks allows to millions of users to share opinions on different aspects of life every day. Therefore this information is a rich source of data for opinion mining and sentiment analysis. This paper presents a novel approach to the sentiment analysis based on the Latent Dirichlet Allocation (LDA) approach. The proposed methodology aims to identify a word-based graphical model (we call it a mixed graph of terms) for depicting a positive or negative attitude towards a topic. By the use of this model it will be possible to automatically mine from documents positive and negative sentiments.Experimental evaluation, on standard and real datasets, shows that the proposed approach is effective and furnishes good and reliable results

Вища математика. Ч.1. Диференціальне числення у прикладах та задачах

Викладено набір теоретичних та практичних тестів з диференціального числення функції однієї та багатьох змінних. Докладні відповіді, вказівки, розв’язання типових завдань та достатня кількість прикладів для самостійної роботи дозволяють використовувати посібник для всіх видів занять