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We investigated retrieval-induced forgetting of motor sequences in samples of Chinese participants. Retrieval-induced forgetting occurs when selective retrieval of a subset of information stored in memory causes forgetting for the non-retrieved rest. This phenomenon critically depends on the organised storage of separate categories of memory representations. In studies with participants from a Western culture (Germany), a categorization in left- and right-hand movements previously had been supported by letter stimuli based on a spatial mental representation of the Roman alphabet. The same assignment of letters from the beginning or end of the alphabet to motor sequences performed either with the left- or the right-hand did not entail retrieval-induced forgetting in the present study, however (Experiment 1). In Experiment 2, visual features of displaying to-be-learned sequences additionally supported a distinction into left and right. In Experiment 3, learning trials provided verbal category labels. The occurrence of retrieval-induced forgetting in the latter two experiments suggests language-dependent organisation of non-verbal items in memory.
cpm.4.CSE/IRT (compact process model for Competence Science Education based on IRT models) is a process model for competence measurement based on IRT models. It allows the efficient development of measuring instruments for computer science education. Cpm.4.CSE/IRT consists of four sub processes: B1 determine items, B2 test items, B3 analyze items according to Rasch model, and B4 interpret items by criteria. Cpm.4.CSE/IRT is modeled in IDEF0, a process modeling language that is standardized and widely used. It is implemented in R, an open-source software optimized for statistical calculations and graphics that allows users to interact using the web application framework Shiny. Through coordinated processes, cpm.4.CSE/IRT ensures the quality and comparability of test instruments in competence measurement. Cpm.4.CSE/IRT is demonstrated using an example from the competence area of Modeling.
Gamification and game-based learning have been established as powerful tools in education. Location-based games (geogames) have been established following mainly a ‘seek-and-find’ game-mechanic, challenging mechanics like simulations are rarely used. We describe an approach for creating an educational location-based game (geogame). The central design problem consists in integrating an ecological simulation into the location-based game flow. We show how to combine these two game mechanics by simplifying complex simulations while maintaining their validity. In an empirical study we evaluate our geogame with secondary school students (N = 329). Our quasi-experimental pre-post-test design focuses on the game-related enjoyment provided by a simplified simulation task within a geogame compared to a more complex desktop simulation and to a geogame without simulation. The results show that the players of the Geogame spend much less time on interacting with the simulation than on other tasks. Nevertheless, the simulation within the geogame contributes positively to the game playing experience. Player enjoyment is even found to be slightly higher in the simulation geogame than in the indoor simulation. A critical threshold of time for using simulations within location-based game mechanics is discussed and related design-pattern for geogames are presented to support educators and game developers in the co-design of challenging location-based games. This study contributes to locate ecological simulations in areas where they actually take place. Complex topics and competencies in education become “real” for players in an outdoor experience.
After a brief description of the four components of risk literacy and the tools for analyzing risky situations, decision strategies are introduced, These rules, which satisfy tenets of Bounded Rationality, are called fast and frugal trees. Fast and frugal trees serve as efficient heuristics for decision under risk. We describe the construction of fast and frugal trees and compare their robustness for prediction under risk with that of Bayesian networks. In particular, we analyze situations of risky decisions in the medical domain. We show that the performance of fast and frugal trees does not fall too far behind that of the more complex Bayesian networks.