Virtual patients in a behavioral medicine massive open online course (MOOC) : a case-based analysis of technical capacity and user navigation pathways

BACKGROUND: Massive open online courses (MOOCs) have been criticized for focusing on presentation of short video clip lectures and asking theoretical multiple-choice questions. A potential way of vitalizing these educational activities in the health sciences is to introduce virtual patients. Experiences from such extensions in MOOCs have not previously been reported in the literature. OBJECTIVE: This study analyzes technical challenges and solutions for offering virtual patients in health-related MOOCs and describes patterns of virtual patient use in one such course. Our aims are to reduce the technical uncertainty related to these extensions, point to aspects that could be optimized for a better learner experience, and raise prospective research questions by describing indicators of virtual patient use on a massive scale. METHODS: The Behavioral Medicine MOOC was offered by Karolinska Institutet, a medical university, on the EdX platform in the autumn of 2014. Course content was enhanced by two virtual patient scenarios presented in the OpenLabyrinth system and hosted on the VPH-Share cloud infrastructure. We analyzed web server and session logs and a participant satisfaction survey. Navigation pathways were summarized using a visual analytics tool developed for the purpose of this study. RESULTS: The number of course enrollments reached 19,236. At the official closing date, 2317 participants (12.1% of total enrollment) had declared completing the first virtual patient assignment and 1640 (8.5%) participants confirmed completion of the second virtual patient assignment. Peak activity involved 359 user sessions per day. The OpenLabyrinth system, deployed on four virtual servers, coped well with the workload. Participant survey respondents (n=479) regarded the activity as a helpful exercise in the course (83.1%). Technical challenges reported involved poor or restricted access to videos in certain areas of the world and occasional problems with lost sessions. The visual analyses of user pathways display the parts of virtual patient scenarios that elicited less interest and may have been perceived as nonchallenging options. Analyzing the user navigation pathways allowed us to detect indications of both surface and deep approaches to the content material among the MOOC participants. CONCLUSIONS: This study reported on first inclusion of virtual patients in a MOOC. It adds to the body of knowledge by demonstrating how a biomedical cloud provider service can ensure technical capacity and flexible design of a virtual patient platform on a massive scale. The study also presents a new way of analyzing the use of branched virtual patients by visualization of user navigation pathways. Suggestions are offered on improvements to the design of virtual patients in MOOCs

A Platform for Collaborative e-Science Applications

Abstract A novel, holistic, approach to scientific investigations should, besides analysis of individual phenomena, integrate different, interdisciplinary sources of knowledge about a complex system to obtain a deep understanding of the system as a whole. This innovative way of research, recently called system-level science [1], requires advanced software environments to support collaborating research groups. Most problem-solving environments and virtual laboratories In the ViroLab project The Virtual Laboratory (see The Experiment Planning Environment supports rapid experiment plan development while the Experiment Management Interface enables loading and execution of experiments. The Experiment Repository developers and published for future use. The virtual laboratory engi Operation Invoker which instantiates grid object repr operation invocations. The GridSpace Applic load balancing on computational servers. The Data Access Service remote databases located in research institutions and Fig. 1. Architecture of the Virtual Laboratory The provenance approach in the ViroLab virtual laboratory ontology-based semantic modeling, monitoring of infrastructure, and database technologies, in order to coll the execution of experiments, represent it in a meaningful way, repository. In the ViroLab project, this virtual laboratory is used to plan and virological experiments, with various types of analysis of as the calculation of drug resistance, querying historical and about experiments, a drug resistance system based on the Retrogram been applied to other application domains, such as comparison, data mining using the Weka library, series of Gaussian application on the EGEE infrastructure. computer science classes. We have developed an environment for collaborative planning, execution of e-Science applications. It facilitates fast, close cooperation and users so it may be used by groups of experts running In-silico experiments undergo frequent changes, this platform encourages quick, agile simulation software releasing