Unsupervised Learning for Understanding Student Achievement in a Distance Learning Setting

Many factors could affect the achievement of students in distance learning settings. Internal factors such as age, gender, previous education level and engagement in online learning activities can play an important role in obtaining successful learning outcomes, as well as external factors such as regions where they come from and the learning environment that they can access. Identifying the relationships between student characteristics and distance learning outcomes is a central issue in learning analytics. This paper presents a study that applies unsupervised learning for identifying how demographic characteristics of students and their engagement in online learning activities can affect their learning achievement. We utilise the K-Prototypes clustering method to identify groups of students based on demographic characteristics and interactions with online learning environments, and also investigate the learning achievement of each group. Knowing these groups of students who have successful or poor learning outcomes can aid faculty for designing online courses that adapt to different students' needs. It can also assist students in selecting online courses that are appropriate to them

On requirements for federated data integration as a compilation process

Data integration problems are commonly viewed as interoperability issues, where the burden of reaching a common ground for exchanging data is distributed across the peers involved in the process. While apparently an effective approach towards standardization and interoperability, it poses a constraint to data providers who, for a variety of reasons, require backwards compatibility with proprietary or non-standard mechanisms. Publishing a holistic data API is one such use case, where a single peer performs most of the integration work in a many-to-one scenario. Incidentally, this is also the base setting of software compilers, whose operational model is comprised of phases that perform analysis, linkage and assembly of source code and generation of intermediate code. There are several analogies with a data integration process, more so with data that live in the Semantic Web, but what requirements would a data provider need to satisfy, for an integrator to be able to query and transform its data effectively, with no further enforcements on the provider? With this paper, we inquire into what practices and essential prerequisites could turn this intuition into a concrete and exploitable vision, within Linked Data and beyond

A platform for semantic web studies

The Semantic Web can be seen as a large, heterogeneous network of ontologies and semantic documents. Characterizing these ontologies, the way they relate and the way they are organized can help in better understanding how knowledge is produced and published online. It also provides new ways to explore and exploit this large collection of ontologies. In this paper, we present the foundation of a research platform for characterizing the Semantic Web, relying on the collection of ontologies and the functionalities provided by the Watson Semantic Web search engine. We more specifically focus on formalizing and monitoring relationships between ontologies online, considering a variety of different relations (similarity, versioning, agreement, modularity) and how they can help us obtaining meaningful overviews of the current state of the Semantic Web

Linking Data Across Universities: An Integrated Video Lectures Dataset

This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme

Semantic technologies to support the user-centric analysis of activity data

There is currently a trend in giving access to users of on-line services to their own data. In this paper, we consider in particular the data which is generated from the interaction between a user and an organisation online: activity data as held in websites and Web applications logs. We show how we use semantic technologies including RDF integration of log data, SPARQL and lightweight ontology reasoning to aggregate, integrate and analyse activity data from a user-centric point of view

DOOR: towards a formalization of ontology relations

In this paper, we describe our ongoing effort in describing and formalizing semantic relations that link ontolo- gies with each others on the Semantic Web in order to create an ontology, DOOR, to represent, manipulate and reason upon these relations. DOOR is a Descriptive Ontology of Ontology Relations which intends to define relations such as inclusion, versioning, similarity and agreement using ontological primitives as well as rules. Here, we provide a detailed description of the methodology used to design the DOOR ontology, as well as an overview of its content. We also describe how DOOR is used in a complete framework (called KANNEL) for detecting and managing semantic relations between ontologies in large ontology repositories. Applied in the context of a large collection of automatically crawled ontologies, DOOR and KANNEL provide a starting point for analyzing the underlying structure of the network of ontologies that is the Semantic Web

Explaining clusters with inductive logic programming and linked data

Knowledge Discovery consists in discovering hidden regularities in large amounts of data using data mining techniques. The obtained patterns require an interpretation that is usually achieved using some background knowledge given by experts from several domains. On the other hand, the rise of Linked Data has increased the number of connected cross-disciplinary knowledge, in the form of RDF datasets, classes and relationships. Here we show how Linked Data can be used in an Inductive Logic Programming process, where they provide background knowledge for finding hypotheses regarding the unrevealed connections between items of a cluster. By using an example with clusters of books, we show how different Linked Data sources can be used to automatically generate rules giving an underlying explanation to such clusters

Propagating Data Policies: a User Study

When publishing data, data licences are used to specify the actions that are permitted or prohibited, and the duties that target data consumers must comply with. However, in complex environments such as a smart city data portal, multiple data sources are constantly being combined, processed and redistributed. In such a scenario, deciding which policies apply to the output of a process based on the licences attached to its input data is a difficult, knowledge- intensive task. In this paper, we evaluate how automatic reasoning upon semantic representations of policies and of data flows could support decision making on policy propagation. We report on the results of a user study designed to assess both the accuracy and the utility of such a policy-propagation tool, in comparison to a manual approach

FABilT – finding answers in a billion triples

This submission presents the application of two coupled systems to the Billion Triples Challenge. The first system (Watson) provides the infrastructure which allows the second one (PowerAqua) to pose natural language queries to the billion triple datasets. Watson is a gateway to the Semantic Web: it crawls and indexes semantic data online to provide a variety of access mechanisms for human users and applications.We show here how we indexed most of the datasets provided for the challenge, thus obtaining an infrastructure (comprising web services, API, web interface, etc.) which supports the exploration of these datasets and makes them available to any Watson-based application. PowerAqua is an open domain question answering system which allows users to pose natural language queries to large scale collections of heterogeneous semantic data. In this paper, we discuss the issues we faced in configuring PowerAqua and Watson for the challenge and report on our results. The system composed of Watson and PowerAqua, and applied to the Billion Triples Challenge, is called FABilT