Hendrik Thüs

A reference model for learning analytics

Recently, there is an increasing interest in learning analytics in Technology-Enhanced Learning TEL. Generally, learning analytics deals with the development of methods that harness educational datasets to support the learning process. Learning analytics LA is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics and visualisation. LA is also a field in which several related areas of research in TEL converge. These include academic analytics, action analytics and educational data mining. In this paper, we investigate the connections between LA and these related fields. We describe a reference model for LA based on four dimensions, namely data and environments what?, stakeholders who?, objectives why? and methods how?. We then review recent publications on LA and its related fields and map them to the four dimensions of the reference model. Furthermore, we identify various challenges and research opportunities in the area of LA in relation to each dimension.

Tag-Based Collaborative Filtering Recommendation in Personal Learning Environments

The personal learning environment (PLE) concept offers a learner-centric view of learning and suggests a shift from knowledge-push to knowledge-pull approach to learning. One concern with a PLE-driven knowledge-pull approach to learning, however, is information overload. Recommender systems can provide an effective mechanism to deal with the information overload problem in PLEs. In this paper, we study different tag-based collaborative filtering recommendation techniques on their applicability and effectiveness in PLE settings. We implement 16 different tag-based collaborative filtering recommendation algorithms, memory based as well as model based, and compare them in terms of accuracy and user satisfaction. The results of the conducted offline and user evaluations reveal that the quality of user experience does not correlate with high-recommendation accuracy.

Mobile learning in context

The widespread use of mobile technologies has led to an increasing interest in mobile learning. Context is a central topic of research in that area. In fact, a major benefit of mobile devices is that they enable learning across contexts. In this paper, we explore how context can deliver significant benefits in mobile learning and provide an extensive review of the current literature and research on mobile learning in context. Furthermore, we identify various challenges and research opportunities in this area and propose the conceptual framework CAMeL for context-aware mobile learning.

Context-Aware Mobile Professional Learning in PRiME

Technology Enhanced Learning (TEL) in professional and organizational settings is increasingly gaining importance. The high availability of mobile end devices and their ability to support learning across contexts open up new perspectives for effective professional learning and knowledge management. The BMBF project Professional Reflective Mobile Personal Learning Environments (PRiME) addresses the challenge of mobile learning in context and realizes a seamless learning framework which connects learning and work processes. PRiME enables the mobile professional learner to harness implicit knowledge and supports continuous knowledge creation and reflection at three different layers: the personal learning environment (PLE), the personal knowledge network (PKN), and the network of practice (NoP).

Learner Modeling in Academic Networks

Learning analytics (LA) deals with the development of methods that harness educational data sets to support the learning process. To achieve particular learner entered LA objectives such as intelligent feedback, adaptation, personalization, or recommendation, learner modeling is a crucial task. Learner modeling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. In this paper, we focus on learner modeling in academic networks. We present theoretical, design, implementation, and evaluation details of PALM, a service for personal academic learner modeling. The primary aim of PALM is to harness the distributed publication information to build an academic learner model.

Forschungsfeld Learning Analytics

Summary Learning analytics has attracted a great deal of attention in technology enhanced learning (TEL) in recent years as educational institutions and researchers are increasingly seeing the potential that learning analytics has to support the learning process. Learning analytics has been identified as a possible key future trend in learning and teaching (Johnson et al., 2011). Analytics can be a powerful tool to support learning. There are, however, a number of issues that need to be addressed before starting analytics projects. In this paper, we identify various challenges and research opportunities in the emerging area of learning analytics. Zusammenfassung Das Thema Learning Analytics hat in letzter Zeit große Aufmerksamkeit erlangt. Es verspricht, vor allem selbstgesteuerte Lernprozesse zu unterstützen, die typisch für das E-Learning in Verbindung mit Web 2.0 und sozialen Medien sind. Dabei wird versucht, durch Aufzeichnung, Analyse und geeignete Präsentation von Lernaktivitäten und deren Kontext, die Reflexion und Optimierung eines Lehrangebots oder des Lernverhaltens zu fördern. In Johnson et al. (2011) wurde Learning Analytics als eine der Schlüsseltechnologien für zukünftige Lehr- und Lernansätze identifiziert. Einige der damit verbundenen Forschungsfragen werden in diesem Artikel vorgestellt.

Evolution of Interests in the Learning Context Data Model

A key area of application for Learning Analytics (LA) and Educational Data Mining (EDM) is lifelong learner modeling. The aim is that data gathered from different learning environments would be fed into a personal lifelong learner model that can be used to foster personalized learning experiences. As learning is increasingly happening in open and networked environments beyond the classroom and access to knowledge in these environments is mostly context-sensitive and interest-driven, learner’s contexts and interests need to constitute important features to be modeled. The context data of a learner as it is already represented by the Learning Context Data Model (LCDM) specification, describes the learner’s activities, her biological conditions, as well as the characteristics of the learning environment. Towards a lifelong learner model, a model consisting of context data can further be refined with an evolving set of interests. This paper describes an approach to extend the existing LCDM specification with interests, taking into account the importance of the interests as well as their evolution over time.

Layered Knowledge Networking in Professional Learning Environments

Knowledge Management (KM) and Technology Enhanced Learning (TEL) became a very important issue in modern organizational professional learning and work process integration. Former learning and KM theories which characterize knowledge as a thing or process no longer fit todays digital world where the amount of required information is no more manageable and the half-time of knowledge in general is rapidly decreasing. Younger approaches such as the Learning as a Network (LaaN) theory describe knowledge as complex and emergent and put a heavier focus on knowledge networking. The LaaN theory further stresses the convergence of the learning and work processes in professional learning settings and views KM and TEL as two sides of the same coin. Driven by the LaaN theory, the Professional Reflective Mobile Personal Learning Environments (PRiME) project describes an integrated KM and TEL framework which connects learning and work processes. It enables the professional learner to harness implicit knowledge and offers knowledge networking at three different layers: the Personal Learning Environment (PLE), the Personal Knowledge Network (PKN) and the Network of Practice (NoP). Continuous knowledge networking results in constant evolution of knowledge leading to personal as well as organizational learning.

Vielfalt in der Informatik - Ergebnisse des Forschungsprojektes IGaDtools4MINT

Das interdisziplinare Forschungsprojekt „IGaDtools4MINT – Integration von Gender and Diversity in MINT-Studiengangen an Hochschulen“ arbeitet vor dem Hintergrund des geringen Frauenanteils sowie des Anteils an weiteren bisher unterreprasentierten Studierendengruppen in der Informatik und den anderen MINT-Fachern an einem koharenten Gesamtkonzept, um mittelfristig die Studierendenanteile der beschriebenen Gruppen zu erhohen und gleichzeitig die Abbruchquoten zu senken.

Harnessing Collective Intelligence in Personal Learning Environments

The Personal Learning Environment (PLE) driven approach to learning suggests a shift in emphasis from a teacher driven knowledge-push to a learner driven knowledge pull learning model. One concern with knowledge-pull approaches is knowledge overload. Thus, there is a crucial need for knowledge filters to help learners cope with the problem of knowledge overload. In this paper, we present the details of PLEM as a Web 2.0 driven service for personal learning management, which acts as a knowledge filter for learning. The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to help learners find quality knowledge nodes that can populate their PLEs.