A G Hugo Parada

Analysing the Impact of Built-In and External Social Tools in a MOOC on Educational Technologies

MOOCs have been a disruptive educational trend in the last months. Some MOOCs just replicate traditional teaching pedagogies, adding multimedia elements like video lectures. Others go beyond, trying to engage the massive number of participants by promoting discussions and relying on their contributions to the course. MOOC platforms usually provide some built-in social tools for this purpose, although instructors or participants may suggest others to foster discussions and crowdsourcing. This paper analyses the impact of two built-in Q&A and forum and three external social tools Facebook, Twitter and MentorMob in a MOOC on educational technologies. Most of the participants agreed on the importance of social tools to be in touch with their partners and share information related to the course, the forum being the one preferred. Furthermore, the lessons learned from the enactment of this MOOC employing social tools are summarized so that others may benefit from them.

Experiences of running MOOCs and SPOCs at UC3M

The appearance of MOOCs has boosted the use of educational technology in all possible contexts. Universities are trying to understand this new phenomenon, while carrying out the first trials. Best practices are still scarce and will be developed in the coming months. In this paper, we present first experiences carried out at Universidad Carlos III de Madrid, both with MOOCs (Massive Open Online Courses) and with SPOCs (Small Private Online Courses), which are MOOC counterparts for internal use.

Towards combining individual and collaborative work spaces under a unified e-portfolio

E-portfolios in learning environments have been attributed numerous benefits and their presence has been steadily increasing. And so has the variety of environments in which a student participates. Collaborative learning requires communication and resource sharing among team members. Students may participate in multiple teams throughout a long period of time, sometimes even simultaneously. Conventional eportfolios are oriented toward showcasing individual achievements, but they need to also equally reflect collaborative achievements. The approach described in this paper has the objective of offering students an e-portfolio as a local folder their personal computer containing a combined view of their individual and collaborative work spaces. The content of this folder can be synchronized with a remote server thus achieving resource sharing and publication of a clearly identified set of resources.

Coverage Metrics for Learning-Event Datasets Based on Client-Side Monitoring

The collection of learner events within a server-client architecture occurs either at server, client or both complementarily. Such collection may be incomplete due to various factors, particularly for client-based monitoring, where learners can disable, delete or even modify their event logs due to privacy policies. The quality and accuracy of any analysis based on such data collections depends critically on the quality of the subjacent dataset. We propose three initial metrics to evaluate the completeness of a learning dataset: client-to-server ratio, event-to-activity ratio and subjective ratio. These metrics provide a glimpse on the coverage rate of the monitoring and can be applied to distinguish subsets of data with a minimum level of reliability to be used in a learning analytics study.

A Tool for the Analysis of Change Management Processes in Software Development Cycles

Change management processes theory specifies the life cycle of a change through an organization. It is a wellknown process present in day-to-day operations, with up to hundreds of changes passing through its phases each day. There is a broad range of tools that help with keeping track of each of those changes. However, the use of these tools, and hence the process itself, is not always translated perfectly into an organization. Therefore, it is necessary to analyse how the process has been implemented and how to correct it. Change management systems often offer some degree of analysis, but it is either too little or too obtuse. In this paper we present a tool that can help analyse the data gathered by these systems in order to detect bottle-necks and irregularities in a visual way tailored to the special time needs of the data.

Classification in Sparse, High Dimensional Environments Applied to Distributed Systems Failure Prediction

Network failures are still one of the main causes of distributed systems’ lack of reliability. To overcome this problem we present an improvement over a failure prediction system, based on Elastic Net Logistic Regression and the application of rare events prediction techniques, able to work with sparse, high dimensional datasets. Specifically, we prove its stability, fine tune its hyperparameter and improve its industrial utility by showing that, with a slight change in dataset creation, it can also predict the location of a failure, a key asset when trying to take a proactive approach to failure management.