Layered feedback in user-system interaction

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Motivation to learn:Engaging students with congenital and acquired deafblindness

People are intrinsically motivated to learn. This also holds for children with deafblindness, even though deafblindness can negatively influence their motivation to learn. Double sensory loss can hinder the ability to explore, observe, imitate and communicate. Teachers have an important role in creating an accessible and safe environment in which learning can take place. An important part of this involves teachers taking into account their students’ needs. According to Self-Determination Theory, the psychological needs for competence, autonomy and relatedness influence whether people are motivated to learn. Teachers can support these needs by providing structure, autonomy support and involvement. This research explored the extent to which teachers support the needs of students with congenital and acquired deafblindness and what its effect is on student motivation. Video analysis was the most important method applied in this study. The results showed that students’ motivation is generally high when teachers provide need support, suggesting that need-supporting teacher behavior does indeed influence student motivation. In general, teachers provide more structure than autonomy support and involvement. Furthermore, it appeared possible to coach teachers in need-supportive behaviors. We concluded this research with the recommendation to pay more attention to need-supportive teaching in the professionalization of teachers. The provision of autonomy support deserves particular attention, given that the teachers in this study focused least on autonomy

Log-based Evaluation of Label Splits for Process Models

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.Comment: Paper accepted at the 20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, to appear in Procedia Computer Scienc

Heuristic Approaches for Generating Local Process Models through Log Projections

Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create so-called local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible single overall process model exists that is able to describe the traces of the process from start to end. The practical application of LPM discovery is however hindered by computational issues in the case of logs with many activities (problems may already occur when there are more than 17 unique activities). In this paper, we explore three heuristics to discover subsets of activities that lead to useful log projections with the goal of speeding up LPM discovery considerably while still finding high-quality LPMs. We found that a Markov clustering approach to create projection sets results in the largest improvement of execution time, with discovered LPMs still being better than with the use of randomly generated activity sets of the same size. Another heuristic, based on log entropy, yields a more moderate speedup, but enables the discovery of higher quality LPMs. The third heuristic, based on the relative information gain, shows unstable performance: for some data sets the speedup and LPM quality are higher than with the log entropy based method, while for other data sets there is no speedup at all.Comment: paper accepted and to appear in the proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM), special session on Process Mining, part of the Symposium Series on Computational Intelligence (SSCI