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Many prediction tasks can be done based on users’ trace data. In this paper, we explored convergent thinking as a personality-related attribute and its relation to features gathered in interactive and non-interactive tasks of an online course. This is an under-utilized attribute that could be used for adapting online courses according to the creativity level to enhance the motivation of learners. Therefore, we used the logfile data of a 60 minutes Moodle course with N=128 learners, combined with the Remote Associates Test (RAT). We explored the trace data and found a weak correlation between interactive tasks and the RAT score, which was the highest considering the overall dataset. We trained a Random Forest Regressor to predict convergent thinking based on the trace data and analyzed the feature importance. The result has shown that the interactive tasks have the highest importance in prediction, but the accuracy is very low. We discuss the potential for personalizing online courses and address further steps to improve the applicability.
In the time of digitalization the demand for organizational change is rising and demands ways to cope with fundamental changes on the organizational as well as individual level. As a basis, learning and forgetting mechanisms need to be understood in order to guide a change process efficiently and successfully. Our research aims to get a better understanding of individual differences and mechanisms in the change context by performing an experiment where individuals learn and later re-learn a complex production process using a simulation setting. The individual’s performance, as well as retentivity and prior knowledge is assessed. Our results show that higher retentivity goes along with better learning and forgetting performances. Prior knowledge did not reveal such relation to the learning and forgetting performances. The influence of age and gender is discussed in detail.
How games spoil creativity
(2020)
The demand for a creative workforce is every growing and effective measures to improve individual creativity are searched for. This study analyzes the possibility to use games as a prime for a creative mindset. Two short entertainment games, plus a no-game-comparison condition were set up in three versions of an online-study, along with two creativity tasks and scales to assess the individual creative mindset (fixed-vs-growth, creative self-efficacy and affect). Results indicate priming effects of the games, but in the opposite intended direction: gaming diminished the creative test performances. Those playing the games reported more ideas in the open-ended creative problem task, but those answers were of less quality and they solved less closed-problem items compared to those not playing. An impact of further mindset differences could be ruled out.
Expanding modeling notations
(2021)
Creativity is a common aspect of business processes and thus needs a proper representation through process modeling notations. However, creative processes constitute highly flexible process elements, as new and unforeseeable outcome is developed. This presents a challenge for modeling languages. Current methods representing creative-intensive work are rather less able to capture creative specifics which are relevant to successfully run and manage these processes. We outline the concept of creative-intensive processes and present an example from a game design process in order to derive critical process aspects relevant for its modeling. Six aspects are detected, with first and foremost: process flexibility, as well as temporal uncertainty, experience, types of creative problems, phases of the creative process and individual criteria. By first analyzing what aspects of creative work modeling notations already cover, we further discuss which modeling extensions need to be developed to better represent creativity within business processes. We argue that a proper representation of creative work would not just improve the management of those processes, but can further enable process actors to more efficiently run these creative processes and adjust them to better fit to the creative needs.
This study aims to compare online vs. offline flirting and dating behavior using the example of the location-based real-time dating (LBRTD) app Tinder, a popular dating platform. We focus on persons' self-descriptions like self-esteem, social desirability, state social anxiety, and adjustment behavior on Tinder and the perceived data privacy of the app. Data was gathered using a survey approach with Tinder users reporting their behavior in offline and online settings. The comparison between offline and online behavior was made using Response Surface Analysis. The results suggest that the different conditions of the natural and digital worlds do not influence the individual's behavior and emotional perception. The results are analyzed and discuss gender, age, motivation to use the app, and the user's relationship status.
This paper aims to investigate the possibility to include aspects of forgetting into business process modeling. To date, there is no possibility to model forgotten or to-be- forgotten elements beyond the mere deletion. On a first attempt, we focus on the individual level and model knowledge transformation within a single person. Using the Knowledge Model Description Language, we propose ways to include different forms of forgetting into the realm of modeling tools. Using data from an experimental setting within an assembly line production environment, the usability of those new modeling tools is tested. So far, the applicability of modeling features for forgetting on the individual level is mostly restricted to a research context. However, clear requirements to transfer the tools onto the team- and organizational level are set out.