This paper describes two empirical research studies that investigated how to improve naive users' mental models to support end-user development (EUD) of Internet-of-Things (IoT). Specifically, we intended to evalu...
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This paper describes two empirical research studies that investigated how to improve naive users' mental models to support end-user development (EUD) of Internet-of-Things (IoT). Specifically, we intended to evaluate the effectiveness of two different strategies, namely nudging and informing, to support trigger-action (TA) rule programming. To this aim, we analyzed non-expert users' performance and their verbal reports (Studies 1 and 2, respectively) in a task requiring the identification of the outcomes of the execution of specific sets of TA rules in different IoT scenarios. The triggering part of TA rules typically involves instantaneous and/or protracted events, and previous studies have shown that users' poor understanding of the distinction between these two types of events, as well as of the way in which the rules interact with each other, can result in poor TA programming performances. The first (experimental and quantitative) study shows that a nudging strategy (i.e., using two different temporal conjunctions, WHEN and WHILE, to introduce the rules' triggering conditions that refer to the two types of events instead of using the more common and generical IF) improves participants' understanding of the rules' behavior. It also provides some evidence that an informing strategy (i.e., providing participants with an explicit description of how the rules are evaluated and activated) can improve participants' accuracy in identifying the rules that did not realize the desired situation. The second (observational and qualitative) study suggests that the use of WHEN and WHILE in the triggering part of the rule helps participants distinguish the two types of events and understand their semantics. This work extends the current literature in EUD by providing both critical information about users' mental models in IoT and useful suggestions to make appropriate (linguistic and structural) choices when designing the interface that guides users in defining the rules.
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