Davide Taibi

The Open Learning Object model to promote Open Educational Resources

In this paper we present the results of research work, that forms part of the activities of the EU-funded project SLOOP: Sharing Learning Objects in an Open Perspective, aimed at encouraging the definition, development and management of Open Educational Resources based on the Learning Object paradigm (Wiley, 2000). We present a model of Open Learning Object (OpenLO) for a greater awareness in the use of LOs by teachers, giving them a more active role in the evolution of educational resources; the OpenLO model extends the concept of reusability, providing pedagogical sustainability. Moreover, we compare FreeLOms, a Learning Object Management System that implements the proposed OpenLO model, to traditional tools for handling LOs. Editors: Patrick McAndrew (Open University, UK). Reviewers: Anonymous Review (Anonymous) and Patrick McAndrew (Open University, UK).

Social knowledge building in a mobile learning environment

According to the social constructivism paradigm, knowledge is not transferred from teachers to students, but is the result of collaborative activities; when these activities take place in a mobile environment, it is extremely important to consider how the temporal and spatial factors can influence the collaborative process. In this work we define a model for mobile Knowledge Building Process (mKBP); starting from this model, we propose to analyse the evolution of a mobile collaborative learning experience through its interactions with the social, the information and the geographic spaces. Finally, we present the methodologies and the tools used to investigate the data gathered during a specific mobile learning experience carried out at the Institute for Educational Technologies of the Italian National Research Council, in the framework of the Mobile and Ubiquitous Learning (MoULe) project.

The learning analytics & knowledge (LAK) data challenge 2014

The LAK Data Challenge 2014 continues the research efforts of the second edition by stimulating research on the evolving fields Learning Analytics (LA) and Educational Data Mining (EDM). Building on a series of activities of the LinkedUp project, the challenge aims to generate new insights and analysis on the LA & EDM disciplines and is supported through the LAK Dataset - a unique corpus of LA & EDM literature, exposed in structured and machine-readable formats.

Towards integration of web data into a coherent educational data graph

Personalisation, adaptation and recommendation are central aims of Technology Enhanced Learning (TEL) environments. In this context, information retrieval and clustering techniques are more and more often applied to filter and deliver learning resources according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the available data, such as metadata about learning resources as well as users. However, quantity and quality of both is still limited. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. In this paper, we propose a large-scale educational dataset which has been generated by exploiting Linked Data methods together with clustering and interlinking techniques to extract import and interlink a wide range of educationally relevant data. We also introduce a set of reusable techniques which were developed to realise scalable integration and alignment of Web data in educational settings.

Towards Embedded Markup of Learning Resources on the Web: An Initial Quantitative Analysis of LRMI Terms Usage

Embedded markup of Web pages have emerged as a significant source of structured data on the Web. In this context, the LRMI initiative has provided a set of vocabulary terms, now part of the schema.org vocabulary, to enable the markup of resources of educational value. In this paper we present a preliminary analysis of the use of LRMI terms on the Web by assessing LRMI-based statements extracted from the Web Data Commons dataset.

SCS Connector - Quantifying and Visualising Semantic Paths Between Entity Pairs

A key challenge of the Semantic Web lies in the creation of semantic links between Web resources. The creation of links serves as a mean to semantically enrich Web resources, connecting disparate information sources and facilitating data reuse and sharing. As the amount of data on the Web is ever increasing, automated methods to unveil links between Web resources are required. In this paper, we introduce a tool, called SCS Connector, that assists users to uncover links between entity pairs within and across datasets. SCS Connector provides a Web-based user interface and a RESTful API that enable users to interactively visualise and analyse paths between an entity pair \((e_i,e_j)\) through known links that can reveal meaningful relationships between \((e_i,e_j)\) according to a semantic connectivity score (\(SCS\)).

Educational Linked Data on the Web - Exploring and Analysing the Scope and Coverage

Throughout the last few years, the scale and diversity of datasets published according to Linked Data (LD) principles has increased and also led to the emergence of a wide range of data of educational relevance. However, sufficient insights into the state, coverage and scope of available educational Linked Data seem still missing. In this work, we analyse the scope and coverage of educational linked data on the Web, identifying the most significant resource types and topics and apparent gaps. As part of our findings, results indicate a prevalent bias towards data in areas such as the life sciences as well as computing-related topics. In addition, we investigate the strong correlation of resource types and topics, where specific types have a tendency to be associated with particular types of categories, i.e. topics. Given this correlation, we argue that a dataset is best understood when considering its topics, in the context of its specific resource types. Based on this finding, we also present a Web data exploration tool, which builds on these findings and allows users to navigate through educational linked datasets by considering specific type and topic combinations.

Multimodality in a Mobile Learning Environment

The aim of this paper is to investigate the use of multimodal interfaces as an opportunity for students involved in mobile learning activities to enhance their didactic experience. The MoULe (Mobile and Ubiquitous Learning) system, a technological platform that we developed in order to support on-site learning experiences through handheld devices, allows the students to read and modify the educational content available on a e-learning platform, and to record new information during on site activities, by using different multimedia formats, such as textual notes, annotated audio and photos. Each student can use his/her handheld device to store these notes, or s/he can publish them on the server using a wireless connection. Afterwards, the published notes can be shared amongst the students and used to construct wiki pages. In this paper we analyse conditions under which the multimodality can support the student/learning environment interaction and, consequently, enhance the learning process.

Learning Analytics for Interpreting.

An important activity in the life of interpreters is terminology work. A primary method for learning technical vocabulary is the creation of personal glossaries. The current paper describes the design and creation of a system that guides the students in autonomous vocabulary work, supports the students’ learning progress, and helps the teacher in monitoring the student commitment to and achievements in the creation of personal glossaries. The system includes a tool for the creation of glossaries, a tracking system that records the students’ actions and the websites they visit while searching the Web for linguistic and content information, and a learning analytics dashboard. The system was tested on a class of 34 university students training in interpreting and the paper reports some preliminary results.

The 3 rd LAK data competition

The LAK Data Challenge 2015 continues the research efforts of the previous data competitions in 2013 and 2014 by stimulating research on the evolving fields Learning Analytics (LA) and Educational Data Mining (EDM). Building on a series of activities of the LinkedUp project, the challenge aims to generate new insights and analysis on the LA & EDM disciplines and is supported through the LAK Dataset - a unique corpus of LA & EDM literature, exposed in structured and machine-readable formats.