Towards Confirmatory Process Discovery: Making AssertionsAbout the Underlying System

The focus in the field of process mining, andprocess discovery in particular, has thus far been onexploring and describing event data by the means ofmodels. Since the obtained models are often directly basedon a sample of event data, the question whether they alsoapply to the real process typically remains unanswered. Asthe underlying process is unknown in real life, there is aneed for unbiased estimators to assess the system-quality ofa discovered model, and subsequently make assertionsabout the process. In this paper, an experiment is describedand discussed to analyze whether existing fitness, precisionand generalization metrics can be used as unbiased esti-mators of system fitness and system precision. The resultsshow that important biases exist, which makes it currentlynearly impossible to objectively measure the ability of amodel to represent the system

Confronting conflicts:history teachers’ reactions on spontaneous controversial remarks

Sometimes, things don't go to plan. Current events come into the classroom, especially the history classroom. How should students' responses to current affairs be dealt with there? How should students' desire to voice their opinions be handled if their opinion is unpopular. What if the student is simply wrong? How far can moral relativism be acknowledged, explored and scrutinised in the history classroom, when the topic under discussion is controversial and urgent? Working in the Netherlands and Belgium, Wansink, Patist, Zuiker, Savenije and Janssenswillen have developed and refined ways of doing this. In this article they provide an overview of researchers' thinking on the issue, and clear strategies and guidelines for what a history teacher might do to ensure that any unplanned discussion is, at least, respectful, engaging and rigorous

Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project

Learning analytics (LA) has emerged as a field that offers promising new ways to support failing or weaker students, prevent drop-out and aid retention. However, other research suggests that large datasets of learner activity can be used to understand online learning behaviour and improve pedagogy. While the use of LA in language learning has received little attention to date, available research suggests that understanding language learner behaviour could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways. This paper first discusses previous research in the field of language learning and teaching based on learner tracking and the specific affordances of LA for CALL, as well as its inherent limitations and challenges. The second part of the paper analyses data arising from the European Commission (EC) funded VITAL project that adopted a bottom-up pedagogical approach to LA and implemented learner activity tracking in different blended or distance learning settings. Referring to data arising from 285 undergraduate students on a Business French course at Hasselt University which used a flipped classroom design, statistical and process-mining techniques were applied to map and visualise actual uses of online learning resources over the course of one semester. Results suggested that most students planned their self-study sessions in accordance with the flipped classroom design, both in terms of their timing of online activity and selection of contents. Other metrics measuring active online engagement – a crucial component of successful flipped learning - indicated significant differences between successful and non-successful students. Meaningful learner patterns were revealed in the data, visualising students’ paths through the online learning environment and uses of the different activity types. The research implied that valuable insights for instructors, course designers and students can be acquired based on the tracking and analysis of language learner data and the use of visualisation and process-mining tools