Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.

Central-processing-unit schedulers have traditionally allocated resources fairly among processes. By contrast, a fair Share scheduler allocates resources so that users get their fair machine share over a long period.

The SMILI☺ (Student Models that Invite the Learner In) Open Learner Model Framework was created to provide a coherent picture of the many and diverse forms of Open Learner Models (OLMs). The aim was for SMILI☺ to provide researchers with a systematic way to describe, compare and critique OLMs. We expected it to highlight those areas where there had been considerable OLM work, as well as those that had been neglected. However, we observed that SMILI☺ was not used in these ways. We now reflect on the reasons for this, and conclude that it has actually served a broader role in defining the notion of OLM and informing OLM design. Since the initial SMILI☺ paper, much has changed in technology-enhanced learning. Notably, learning technology has become far more pervasive, both in formal and lifelong learning. This provides huge, and still growing amounts of learning data. The fields of Learning Analytics (LA), Learning at Scale (L@S), Educational Data Mining (EDM) and Quantified Self (QS) have emerged. This paper argues that there has also been an important shift in the nature and role of learner models even within Artificial Intelligence in Education and Intelligent Tutoring Systems research. In light of these trends, and reflecting on the use of SMILI☺, this paper presents a revised and simpler version of SMILI☺ alongside the original version. In both cases there are additional categories to encompass new trends, which can be applied, omitted or substituted as required. We now offer this as a guide for designers of interfaces for OLMs, learning analytics and related fields, and we highlight the areas where there is need for more research.