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Emotions embody the value in tourism experiences and drive essential outcomes such as intent to recommend. Current models do not explain how the ebb and flow of emotional arousal during an experience relate to outcomes, however. We analyzed 15 participants’ experiences at the Vincentre museum and guided village tour in Nuenen, the Netherlands. This Vincent van Gogh-themed experience led to a wide range of intent to recommend and emotional arousal, measured as continuous phasic skin conductance, across participants and exhibits. Mixed-effects analyses modeled emotional arousal as a function of proximity to exhibits and intent to recommend. Experiences with the best outcomes featured moments of both high and low emotional arousal, not one continuous “high,” with more emotion during the middle of the experience. Tourist experience models should account for a complex relationship between emotions experienced and outcomes such as intent to recommend. Simply put, more emotion is not always better.

Abstract. In this study we tested whether external regulation provided by artificial pedagogical agents (PAs) was effective in facilitating learners' self-regulated learning (SRL) and can therefore foster complex learning with a hypermedia-based intelligent tutoring system. One hundred twenty (N = 120) college students learned about the human circulatory system with MetaTutor during a 2-hour session under one of two conditions: adaptive scaffolding (AS) or a control (C) condition. The AS condition received timely prompts from four PAs to deploy various cognitive and metacognitive SRL processes, and received immediate directive feedback concerning the deployment of the processes. By contrast, the C condition learned without assistance from the PAs. Results indicated that those in the AS condition gained significantly more knowledge about the science topic than those in the C condition. In addition, log-file data provided evidence of the effectiveness of the PAs' scaffolding and feedback in facilitating learners' (in the AS condition) metacognitive monitoring and regulation during learning. We discuss implications for the design of external regulation by PAs necessary to accurately detect, track, model, and foster learners' SRL by providing more accurate and intelligent prompting, scaffolding, and feedback regarding SRL processes.Keywords: Self-regulated learning Á Metacognition Á Pedagogical agents Á Externally regulated learning Á ITS Á Scaffolding Á Learning Á Product data Á Process data 1 Objectives, Theoretical Framework, and Related Work Self-regulated learning (SRL) is a hallmark of human learning and a key factor in problem solving, reasoning, and understanding complex instructional and training materials with advanced learning technologies (ALTs) such as intelligent tutoring systems (ITSs) [1,2]. For example, when learning about complex STEM topics, research indicates that individuals can gain deep conceptual understanding through the effective use of cognitive, affective, metacognitive, and motivational (CAMM) self-regulatory processes [1,[3][4][5][6]. The successful use of cognitive and metacognitive SRL processes involves setting meaningful goals for one's learning, planning a course of action for

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