Abstract. Data quality is a major issue when conducting studies in behavioral sciences. One of the possible threats to data quality in questionnaire studies are careless responses (CR). When responding carelessly, subjects don’t pay sufficient attention to the questions and therefore compromise the interpretability of the responses. The aim of the current study was to gain a better understanding of the occurrence and identification of CR in Ecological Momen-tary Assessment (EMA) studies, where usually several questionnaires per day are administered to the subjects over the course of some days, weeks or even months. For this purpose, explorative post-hoc analysis was con-ducted using the data of an existing EMA study in audiological research. Completion time, variance, skipped items, acquiescence bias and number of textboxes were analyzed as potential indicators for CR both inter- and intraindividually. Furthermore, consistency was examined using linear mixed models and scanning individual questionnaires. Results showed barely any systematic inconsistencies, indicating the absence of large-scale CR. However, this type of analysis might not be appropriate to identify CR when only occurring occasionally. Moreo-ver, the reliability of indicators of CR might be limited in EMA studies, as the indicators also vary over the course of the study and be-tween different situations. Possibilities for future studies are discussed.

Abstract. The business data analytics domain exhibits a particularly diversified and demanding field of interaction for the end-users. It entails complex tasks and actions expressed by multidimensional data visualization and exploration contents that users with different business roles, skills and experiences need to understand and make decisions so to meet their goals. Many times this engagement is proven to be overwhelming for professionals, highlighting the need for adaptive and personalized solutions that would consider their level of expertise towards an enhanced user experience and quality of outcomes. However, measuring adequately the perceived expertise of individuals using standardized means is still an open challenge in the community. As most of the current approaches employ participatory research design practices that are time consuming, costly, difficult to replicate or to produce comparable, unbiased, results for informed interpretations. Hence, this paper proposes a systematic alternative for capturing expertise through a Perceived Expertise Tool (PET) that is devised based on grounded theoretical perspectives and psychometric properties. Preliminary evaluation with 54 professionals in the data analytics domain showed the accepted internal consistency and validity of PET as well as its significant correlation with other affiliated theoretical and domain-specific concepts. Such findings may suggest a good basis for the standardized modeling of users' perceived expertise that could lead to effective adaptation and personalization.

Abstract. This paper presents a qualitative study in which we evaluate the core parts of an adaptive algorithm for next-exercise selection in an e-learning system. The algorithm was previously constructed from a series of studies where participants played the role of a teacher and chose the difficulty of a subsequent exercise for a learner based on their performance, mental effort and self esteem. In this paper, we present these findings to real teachers to gain insights into whether the algorithm is effective and appropriate for future inclusion in an intelligent tutoring system. Overall, we found that teachers believed that the recommendations from the algorithm were appropriate.