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

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

Alpha Precision : Estimating the Significant System Behavior in a Model

One of the goals of process discovery is to construct, from a given event log, a process model which correctly represents the underlying system. As with any abstraction, one does not necessarily want to represent all possible behavior, but only the significant behavior. While various discovery algorithms support this use case of discovering the significant process behavior, proper evaluation measures for this use case appear to be missing. Therefore, this paper presents a new precision metric that quantifies to what extent the discovered model contains significant system behavior. Besides being a metric with a clear and intuitive interpretation, the metric distinguishes itself in two other areas. Firstly, it introduces the concept of α -significance, which only measures precision with respect to significant behavior. Secondly, it is designed as a system measure and estimates the precision with respect to the underlying system rather than the observed log. This work introduces a new precision measure and a statistical estimation method. Additionally, an empirical demonstration and evaluation of the metric are provided, which creates initial insights and knowledge about the performance and characteristics of the new measure. The results show that the α -precision measure provides a solid foundation for future work on developing quality measures for this particular use case.</p

Exploring Task Execution Patterns in Event Graphs

Classical process mining aims to capture the behavior of a process based on a single dimension: the sequence of activities grouped by process cases. This viewpoint fails to capture how individual actors are organizing their work across multiple cases. We present a tool that uses the graph database Neo4j to model actor behavior over different cases as an event graph. We then use Neo4j queries to detect task execution patterns in the graph describing how multiple actors collaborate across multiple cases. Exploring and visualizing these patterns enables the data driven analysis of tasks, routines, and habits as studied in organizations research