Ernesto D'Avanzo

Sentiment Analysis to Evaluate Teaching Performance

The aim of this work is to review a specific learning analytics method-sentiment analysis-in the field of Higher Education, showing how it is employed to monitor student satisfaction on different platforms, and to propose an architecture of Sentiment Analysis for Higher Education purposes, which trace and unify what emerges from the literature review. First, a literature review is carried out, which proves the widespread and increasing interest of the communities, of both scholars and practitioners, in the use of sentiment analysis in the field of Higher Education. The analysis, focused on three different e-learning domains, identifies weaknesses and gaps, and in particular the lack of a unifying approach which is able to deal with the different domains. Secondly, a prototype architecture-LADEL Learning Analytics Dashboard for E-Learning-is introduced, which is able to deal with the different e-learning domains. Some preliminary experiments are carried out, highlighting some limitations and open issues, as stimulus to continue the development of the platform.

Building and using domain ontologies for learning in various domains: a semantic web-based learning perspective

The deployment of semantic web in the context of learning requires a multidimensional consideration of critical issues mostly related with the integration of Semantic Web technologies into the learning context. The main purpose of this article is to discuss in depth the concept of domain ontologies and their role for the effectiveness of semantic web-based e-learning applications. The representation of the learning content and the exploitation of domain ontologies are analysed in the context of a semantic-based prototype, which will be the basis for an FP7 STREP Project proposition. The knowledge acquisition process and the continuous enrichment of the learning content base is recognised as key functions towards high learning outcomes. The main contribution of this article relates to the understanding of ontological challenges for the design and implementation of e-learning systems.

Using Twitter sentiment and emotions analysis of Google Trends for decisions making

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).,The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.,The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.,The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

Perceived innovative teaching procedures in higher education from students’ perspectives.

Enhancing Knowledge Discovery and Innovation in the Digital Era is a vibrant reference source on the latest research on student education, open information, technology enhanced learning (TEL), and student outcomes. Featuring widespread coverage across a range of applicable perspectives and topics, such as engineering education, data mining, and 3D printing, this book is ideally designed for professionals, upper-level students, and academics seeking current research on knowledge management and innovation networks.