Herre van Oostendorp

Editorial: Adaptivity in Serious Games Through Cognition-Based Analytics

This paper presents the results of a game study, comparing Powersaver Game including a competition feature versus the same game excluding a competition feature with respect to energy conservation in the household. In a pretest–posttest design, we tested whether change in attitude, knowledge and behavior with respect to energy conservation in the household was different for participants playing Powersaver Game with or without competition. All energy conservation activities that the application provides (e.g., washing clothes at low temperatures) take place in the real world and feedback is based on real-time energy consumption. This so-called reality-enhanced game approach aims to optimize the transfer between the game world and the real world. Household energy consumption changed significantly and positively in the long term due to competition. A significant difference of 8% in energy consumption between both conditions after the intervention was detected. Besides energy conservation, no further differences were detected between conditions. The chain of events, that an increase in knowledge leads to attitude change, which in turn results in behavior change in the long term is confirmed by means of a path analysis. We conclude that Powersaver Game is effective in the transfer of energy conservation knowledge, which leads to energy saving behavior in the long term while competition additionally contributes to more change in behavior.

This paper presents the results of a study, comparing a game versus a dashboard with respect to energy conservation in the household. In a pretest-posttest design, an empirical study tested whether change in attitude, knowledge, engagement and behaviour with respect to energy conservation in the household was different for participants playing Powersaver Game compared to a control condition where participants used an energy dashboard with the same content, but excluding game features. The aim of this game (developed using an iterative user-centered game design methodology) is to influence household energy consumption by means of electricity and gas usage in the long-term. The intervention time was at least 5 weeks and pre and post measures were based on 21 days intervals. All energy conservation activities that the application provides (e.g. washing clothes on low temperatures) take place in the real world and feedback is based on real time energy consumption. This real data into the game approach aims to optimize the transfer between the game world and the real world. Energy consumption significantly changed in the game condition compared to the control condition, and the difference between both conditions is more than 33% after the intervention. In the game condition, knowledge about energy conservation was significantly increased, although no significant differences in increase of attitude and engagement were found. We conclude that Powersaver Game is effective in transfer of energy conservation knowledge, which leads to energy saving behaviour on the long term. However, it cannot be concluded that playing the game leads to a greater change in attitude, probably because attitude scores of the participants were high from the start.

In this study, we evaluated the ability of computational cognitive models of web‐navigation like CoLiDeS and CoLiDeS+ to model i) user interactions with search engines and ii) individual differences in search behavior due to variations in cognitive factors such as aging. CoLiDeS and CoLiDeS+ were extended to predict user clicks on search engine result pages. Their performance was evaluated using actual behavioral data from an experiment in which 2 types of information search tasks (simple vs. difficult), were presented to younger and older participants. The results showed that the model predictions matched significantly better with the actual user behavior on difficult tasks compared to simple tasks and with younger participants compared to older participants, especially for difficult tasks. Also, the matches were significantly better with CoLiDeS+ compared to CoLiDeS, especially for difficult tasks. We conclude that the advanced capabilities of CoLiDeS+, such as incorporating contextual information and implementing backtracking strategies enable it to predict user behavior significantly better than CoLiDeS, especially on difficult tasks. The usefulness of these modeling outcomes for the design of support systems for older adults is discussed.