Research Area: Web Technology
The increasing trend of sharing educational resources on the World Wide Web has attracted several contributions from the research community. Since most Technology Enhanced Learning users retrieve resources from the Web for teaching or learning, it is clear that the Web is a source of educational material. Therefore, it should be possible to use the Web as a repository for teaching resources. Regarding the retrieval of online resources, a big issue is that the Web is a huge and mostly unorganised space. Hence, there is no guarantee that items retrieved by current search engines are appropriate for educational uses. Automatically identifying Web-content suitable and usable for education is one of the most challenging objectives because it requires extraordinary attention. Indeed, an inappropriate recommendation in such eld may result in reduced learning outcomes by students in assignments and exams or, even worse, in teachers building their courses on incorrect or incomplete foundations.
Studies in Information Retrieval and Technology Enhanced Learning have proposed several solutions to support the teaching and learning needs of instructors and pupils within an enclosed platform. Other studies o er different techniques for collecting Web resources that have specific characteristics. However, to the best of our knowledge, none of the current proposals in the state-of-the-art has paid attention to gathering Web resources that can be used for learning or teaching, without any restriction on topic or terminology. Personalisation also improved Web-search by identifying what topics users prefer, and some progress has been achieved in deducing the purpose of the search (e.g., the user is about to book a trip) for tailored advertising; however, this is a very different use of recommendation. Instead, we focus here on identifying documents with a purpose in the sense of being of value for a learning objective.
This contribution is built on the rationale that the classification of textual materials and natural language processing are strictly related. Thus, we propose to involve natural language processing methods to analyse the content of Web-pages suitable for inclusion in teaching and learning environments. In the eld of the Semantic Web, it is common to apply Information Retrieval from classified online pages. The rapid expansion of the Web creates an ever-increasing demand for faster and yet reliable altering of Web-pages, according to the information needs of users and aiming to eliminate displaying irrelevant and harmful content. The accuracy of the classification is not the only difficulty when applying Information Retrieval techniques on the sheer volume of documents hosted on the World Wide Web. Accessing the most valuable data as quick as possible raises further research questions about the trade-o in accuracy versus the computational time required by a Webpage classifier. Another characteristic of Web-pages is the multitude of traits (features to be used as independent variables) that may be used for their description.
Then, the framework is evaluated in altering task performed on the same dataset, comparing our proposal on both accuracy and speed against popular algorithms for feature selection and feature reduction. In both aspects, our framework outperforms current feature reduction algorithms, achieving more accurate and faster classification of Web-pages in several scenarios. So, we can declare our framework suitable to be used in a purpose-driven crawling task. Smart systems in Technology Enhanced Learning can use our proposal for retrieving an enormous amount of resources and information ready to be used for educational purposes. For example, recommender systems in Technology Enhanced Learning would benefit from the result of this study for suggesting educational resources for both building and improving courses, significantly enhancing the support provided to teachers and students.
Name of the Researcher: Lombardi, Matteo
Name of the Supervisor(s): Estivill-Castro, Vladimir
Year of Completion: 2018
University: Griffith University
Thesis Link: Home Page Url
