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  1. Mendez, G. ; Matsuda, N. ; Santos, O. C. ; Dimitrova, V. (Ed.)
    The dual mechanisms of control framework describes two modes of goal-directed behavior: proactive control (goal maintenance) and reactive control (goal activation on task demands). Although these mechanisms are relevant to learner behaviors during interaction with intelligent tutoring systems (ITS), their relation to ITSs is under-researched. We propose a manipulation to induce proactive or reactive control during interaction with an online tutoring system. We present two experiments where students solved problems using either proactive or reactive control. Study 1 validates the manipulation by investigating behavioral measures that reflect usage of the intended strategy and assesses whether either mode impacted learning. Study 2 investigates if alternating between control modes during problem solving affects student performance. 
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  2. ASEE (Ed.)
    The purpose of this study was to measure the neurocognitive effects of think aloud when engineering students were designing. Thinking aloud is a commonly applied protocol in engineering design education research. The process involves students verbalizing what they are thinking as they perform a task. Students are asked to say what comes into their mind. This often includes what they are looking at, thinking, doing, and feeling. It provides insight into the student’s mental state and their cognitive processes when developing design ideas. Think aloud provides a richer understanding about how, what and why students’ design compared to solely evaluating their final product or performance. The results show that Ericsson and Simon's claim that there is no interference due to think-aloud is not supported by this study and more research is required to untangle the effect of think-aloud. 
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  3. Mitrovic, A. ; & Bosch, N. (Ed.)
    Working collaboratively in groups can positively impact performance and student engagement. Intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines. 
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  4. Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work. 
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  5. null (Ed.)
    An intelligent system can provide sufficient collaborative opportunities and support yet fail to be pedagogically effective if the students are unwilling to participate. One of the common ways to assess motivation is using self-report questionnaires, which often do not take the context and the dynamic aspect of motivation into account. To address this, we propose personas, a user-centered design approach. We describe two design iterations where we: identify motivational factors related to students’ collaborative behaviors; and develop a set of representative personas. These personas could be embedded in an interface and be used as an alternative method to assess motivation within ITS. 
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  6. null (Ed.)
    Defects can be introduced within a 2-D periodic lattice to realize phononic cavities or phononic crystal (PnC) waveguides at the ultrasonic frequency range. The arrangement of these defects within a PnC lattice results in the modification of the Q factor of the cavity or the waveguide. In this work, cavity defects within a PnC formed using cylindrical stainless steel scatterers in water have been modified to control the propagation and Q factor of acoustic waveguides realized through defect channels. The defects channel based waveguides within the PnC were configured horizontally, vertically, and diagonally along the direction of the propagation of the acoustic waves. Numerical simulations supported by experimental demonstration indicate that the defect-based waveguide's Q factor is improved by over 15 times for the diagonal configuration compared to the horizontal configuration. It also increases due to an increase in the scatterers' radius, varied from 0.7 -0.95 mm. 
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  7. null (Ed.)
    Engagement is critical to learning, yet current research rarely explores its underlying contextual influences, such as differences across modalities and tasks. Accordingly we examine how patterns of behavioral engagement manifest in a diverse group of ten middle school girls participating in a synchronous virtual computer science camp. We form multimodal measures of behavioral engagement from learner chats and speech. We found that the function of modalities varies, and chats are useful for short responses, whereas speech is better for elaboration. We discuss implications of our work for the design of intelligent systems that support online educational experiences. 
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  8. Cognitive control and rule learning are two important mechanisms that explain how goals influence behavior and how knowledge is acquired. These mechanisms are studied heavily in cognitive science literature within highly controlled tasks to understand human cognition. Although they are closely linked to the student behaviors that are often studied within intelligent tutoring systems (ITS), their direct effects on learning have not been explored. Understanding these underlying cognitive mechanisms of beneficial and harmful student behaviors can provide deeper insight into detecting such behaviors and improve predictive models of student learning. In this paper, we present a thinkaloud study where we asked students to narrate their thought processes while solving probability problems in ASSISTments. Students are randomly assigned to one of two conditions that are designed to induce the two modes of cognitive control based on the Dual Mechanisms of Control framework. We also observe how the students go through the phases of rule learning as defined in a rule learning paradigm. We discuss the effects of these different mechanisms on learning, and how the information they provide can be used in student modeling. 
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  9. Response time has been used as an important predictor of student performance in various models. Much of this work is based on the hypothesis that if students respond to a problem step too quickly or too slowly, they are most likely to be unsuccessful in that step. However, something that is less explored is that students may cycle through different states within a single response time and the time spent in those states may have separate effects on students’ performance. The core hypothesis of this work is that identifying the different states and estimating how much time is devoted to them in a single response time period will help us predict student performance more accurately. In this work, we de-compose response time into meaningful subcategories that can be indicative of helpful or harmful cognitive states. We then show how a model that is using these subcategories as predictors instead of response time as a whole outperforms both a linear and a non-linear baseline model. 
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