Thomas Daniel Ullmann

Social network analysis for technology-enhanced learning: review and future directions

By nature, learning is social. The interactions by which we learn from others inherently form a network of relationships among people, but also between people and resources. This paper gives an overview of the potential social network analysis (SNA) may have for social learning. It starts with an overview of the history of social learning and how SNA may be of value. The core of the paper outlines the state-of-art of SNA for technology-enhanced learning (TEL), by means of four possible types of SNA applications: visualisation, analysis, simulation, and interventions. In an outlook, future directions of SNA research for TEL are provided.

Components of a research 2.0 infrastructure

In this paper, we investigate the components of a Research 2.0 infrastructure. We propose building blocks and their concrete implementation to leverage Research 2.0 practice and technologies in our field, including a publication feed format for exchanging publication data, a RESTful API to retrieve publication and Web 2.0 data, and a publisher suit for refining and aggregating data. We illustrate the use of this infrastructure with Research 2.0 application examples ranging from a Mash-Up environment, a mobile and multitouch application, thereby demonstrating the strength of this infrastructure.

Research Evidence on the Use of Learning Analytics: Implications for Education Policy

Learning analytics is an emergent field of research that is growing fast. It takes advantage of the last decade of e-learning implementations in education and training as well as of research and development work in areas such as educational data mining, web analytics and statistics. In recent years, increasing numbers of digital tools for the education and training sectors have included learning analytics to some extent, and these tools are now in the early stages of adoption. This report reviews early uptake in the field, presenting five case studies and an inventory of tools, policies and practices. It also provides an Action List for policymakers, practitioners, researchers and industry members to guide work in Europe.

Reflective writing analytics: empirically determined keywords of written reflection

Despite their importance for educational practice, reflective writings are still manually analysed and assessed, posing a constraint on the use of this educational technique. Recently, research started to investigate automated approaches for analysing reflective writing. Foundational to many automated approaches is the knowledge of words that are important for the genre. This research presents keywords that are specific to several categories of a reflective writing model. These keywords have been derived from eight datasets, which contain several thousand instances using the log-likelihood method. Both performance measures, the accuracy and the Cohens κ, for these keywords were estimated with ten-fold cross validation. The results reached an accuracy of 0.78 on average for all eight categories and a fair to good interrater reliability for most categories even though it did not make use of any sophisticated rule-based mechanisms or machine learning approaches. This research contributes to the development of automated reflective writing analytics that are based on data-driven empirical foundations.

The STELLAR Science 2.0 Mash-Up Infrastructure

The field of technology-enhanced learning has been blessed with interdisciplinarity. At the same time, this heterogeneity is also its curse, as each of the participating scientific communities brings along its own research tradition, and established as well as innovative tools to support the research process. Within this contribution, we propose a framework that will help to leverage this plurality to integrate the fragmented technologies into an open, distributed, and participatory research infrastructure in support of technology-enhanced learning. Therefore, we first further conceptualise the idea of a Science 2.0, we describe the framework and its core components against the dimensions of interoperability, outline the key characteristics of the infrastructure and exemplify it with use cases and applications.

Making sense of learner and learning Big Data: reviewing five years of Data Wrangling at the Open University UK

ABSTRACT Most distance learning institutions collect vast amounts of learning data. Making sense of this ‘Big Data’ can be a challenge, in particular when data are stored at different data warehouses and require advanced statistical skills to interpret complex patterns of data. As a leading institute on learning analytics, the Open University UK instigated in 2012 a Data Wrangling initiative. This provided every Faculty with a dedicated academic with expertise data analysis and whose task is to provide strategic, pedagogical and sense-making advice to staff and senior management. Given substantial changes within the OU (e.g. new Faculty structure, real-time dashboards, two large-scale adoptions of predictive analytics approaches, increased reliance on analytics), this embedded case study provides an in-depth review of lessons learned of five years of data wrangling. We will elaborate on the design of the new structure, its strengths and potential weaknesses, and affordances to be adopted by other institutions.

Understanding Accessibility as a Process through the Analysis of Feedback from Disabled Students

Accessibility cannot be fully achieved through adherence to technical guidelines, and must include processes that take account of the diverse contexts and needs of individuals. A complex yet important aspect of this is to understand and utilise feedback from disabled users of systems and services. Open comment feedback can complement other practices in providing rich data from user perspectives, but this presents challenges for analysis at scale. In this paper, we analyse a large dataset of open comment feedback from disabled students on their online and distance learning experience, and we explore opportunities and challenges in the analysis of this data. This includes the automated and manual analysis of content and themes, and the integration of information about the respondent alongside their feedback. Our analysis suggests that procedural themes, such as changes to the individual over time, and their experiences of interpersonal interactions, provide key examples of areas where feedback can lead to insight for the improvement of accessibility. Reflecting on this analysis in the context of our institution, we provide recommendations on the analysis of feedback data, and how feedback can be better embedded into organisational processes.

Interdisciplinary Cohesion of TEL --- An Account of Multiple Perspectives

Research areas and academic disciplines are not static: they change over time with new strands emerging and old ones disappearing. Technology-enhanced learning is a relatively young field of academic activity, getting more broad in scope as it matures. In this paper we seek to assess the state of interdisciplinarity in this academic community, presenting the findings of a quantitative study on mutual engagement, shared practices and methodologies, and sense of joint enterprise via a European research network in between learning and technology disciplines. An exploratory cluster analysis is used to identify different stakeholder groups in technology-enhanced learning research and a social network analysis shows how these are connected to each other. Statistical analysis suggests that a multidisciplinary workplace and study background of researchers are major influencing factors for the choice of border-crossing methodology and terminology. Additionally, results from a supplementary survey on the interdisciplinary cohesion between the fields of technology-enhanced learning and educational development support the view that pedagogical and technological sub-disciplines highly intersect in this field.