Visualizing PLE Usage Jose Luis Santos Katrien Verbert Sten Govaerts Erik Duval Katholieke Universiteit Leuven Celestijnenlaan 200A 3001 Leuven (Belgium) {joseluis.santos, katrien.verbert , sten.govaerts, erik.duval}@cs.kuleuven.be ABSTRACT In recent years, several researchers have been developing methodologies, technologies and systems to support the assembly of learning services, tools and resources in personal learning environments (PLEs). The overall goal is to enrich or even replace traditional learning management systems like Moodle and Blackboard with mash-ups of widgets and services that can be combined and configured in a flexible way, according to the specific needs of the user. In this paper, we describe our approach to visualize user interactions with widgets and services within such personal learning environments. These visualizations enable the exploration of learner behavior within PLEs. The major objective is to improve and evolve PLE related research and development according to feedback mechanisms based on empirical observation. In this paper, we present an overview of our method to capture usage behavior and a first prototype of a visualization dashboard that enables the analysis and interpretation of these data as a basis for evaluation. Categories and Subject Descriptors H3.4 (Web View/Social Networking/Web 2.0) General Terms Measurement, Design, Standardization Keywords Visualization, Analytics, Dashboard, Standardization, Contextualized Attention Metadata, Personal Learning Environments 1. INTRODUCTION The development and proliferation of Web 2.0 technologies has impacted the way users interact with information and with each other. Web-based communities, wikis, blogs and social networks have experienced an exponential growth of both users and content, leading to potentially viral social networking, collaboration, communication and resource sharing opportunities. The abundance of these technologies and services creates many new opportunities in various areas. One of those areas is Technology Enhanced Learning (TEL) that aims to bring together new technological developments and learning models to support learning processes. The ROLE project [3] is researching methods and technologies to enable learners to construct their own personal learning environments (PLEs). The overall goal is to create a flexible and open environment for the federation and mash-up of learning services according to the needs of the learner. Whereas first prototypes have been elaborated in a successful way [5], the measurement of success of PLEs and the components that they aggregate needs further development and elaboration [4]. Within the scope of PLEs, different widgets and services are deployed that are implemented by different developers and, potentially, for different purposes. To measure success of these widgets and services within different contexts, the capturing and analysis of usage data is a key requirement. Such data is usually difficult to collect and analyze, because of the different ways log data are generated within different tools. In this paper, we present a schema to generate usage data within widgets and services in a uniform way. Then, we present our dashboard that enables the visualization of usage data as a basis to detect changes in usage patterns. The purpose is to detect variations in the use of PLEs based on changes in usage patterns with widgets and services. The dashboard also provides insights into whether other similar widgets and services are also affected. The Latour’s actor-network theory (ANT) [9] suggests that understanding how human and the non-human entities interact with each other is the basis of the evolution. Based on Ben Shneiderman’s Visualization Information Seeking Mantra [6] (“Overview first, zoom and filter, then details-on-demand”), we enable users to dig deeper into the data by filtering and interlinking different visualizations of usage patterns. These visualizations provide a basis for gaining insights into the uptake and usage of PLEs and the widgets and services that they aggregate. In addition, they can be used to detect evolution patterns in the use of widgets and services and their composition in PLEs. The paper is organized as follows: in the next section, we present a schema for representing usage data in a uniform way. Section 3 presents the objectives of analyzing these data to detect changes in usage patterns and evolutions in PLEs. Implementation details of the visualizations and the back-end infrastructure to store usage data are presented in Section 4. A use case is presented in Section 5. Conclusions and future work are described in Section 6. 2. USAGE DATA This paper focuses on visualizing usage data to enable awareness of user activities in a PLE and the evolution of widget usage. PLEs have high evolvable characteristics [10] such as modularity, retargetable mechanisms or robustness to environmental and context change. Widget containers allow users to mash up their own learning environment in a flexible way. This enables the system to evolve and adapt to the new needs or requirements of the users. 34