Mastering the Hard Stuff: The History of College Concrete-Canoe Races and the Growth of Engineering Competition Culture

This article details the history of college engineering competitions, originating with student concrete-canoe racing in the 1970s, through today’s multi-million-dollar international multiplicity of challenges. Despite initial differences between engineering educators and industry supporters over the ultimate purpose of undergraduate competitions, these events thrived because they evolved to suit many needs of students, professors, schools, corporations, professional associations, and the engineering profession itself. The twenty-first-century proliferation of university-level competitions in turn encouraged a trickling-down of technical contests to elementary-age children and high schools, fostering the institutionalization of what might be called a competition culture in engineering

MetaTutor: Analyzing self-regulated learning in a tutoring system for biology

We report preliminary data of an initial laboratory study examining the effectiveness of self-regulated learning (SRL) training versus no training on learners\u27 ability to deploy SRL processes and learn about the circulatory system with MetaTutor. MetaTutor is an intelligent tutoring system (ITS) designed to train and foster learners\u27 SRL processes while learning about several complex human body systems. We used a mixed methodology approach and include the results of a subset of the participants (N=30) whose product and process data we have analyzed. Overall, the results indicate that the SRL training group significantly outperformed the control group. © 2009 The authors and IOS Press. All rights reserved

Computational aspects of the intelligent tutoring system metaTutor

We present in this chapter the architecture of the intelligent tutoring system MetaTutor that trains students to use metacognitive strategies while learning about complex science topics. The emphasis of this chapter is on the natural language components. In particular, we present in detail the natural language input assessment component used to detect students\u27 mental models during prior knowledge activation, a metacognitive strategy, and the micro-dialogue component used during sub-goal generation, another metacognitive strategy in MetaTutor. Sub-goal generation involves sub-goal assessment and feedback provided by the system. For mental model detection from prior knowledge activation paragraphs, we have experimented with three benchmark methods and six machine learning algorithms. Bayes Nets, in combination with a word-weighting method, provided the best accuracy (76.31%) and best humancomputer agreement scores (kappa=0.63). For sub-goal assessment and feedback, a taxonomy-driven micro-dialogue mechanism yields very good to excellent human-computer agreement scores for sub-goal assessment (average kappa=0.77). © 2012, IGI Global

Emotions during learning: The first steps toward an affect sensitive intelligent tutoring system.In

Abstract. In an attempt to discover links between learning and emotions, this study adopted an emote-aloud procedure where participants were recorded as they verbalized their affective states while interacting with an intelligent tutoring system, AutoTutor. Participants ’ facial expressions were coding using the Facial Action Coding System and analyzed using association rule mining techniques. The resulting rules are discussed along with implications to the larger project of improving the AutoTutor system into a nonintrusive affect sensitive intelligent tutoring system. While the 20 th century has been ripe with learning theory, these theories have mostly ignored the importance of the link between a persons emotions or affective states and learning (Meyer, & Turner, 2002). However, toward the end of the twentieth century, emotions started to get more attention. Some seminal contributions to the literature include the facial action coding system by Ekman & Friesen (1978), Stein and Levine’s (1991) theory of goals and emotion, Cognitive theory of Emotion (Ortony, Clore, & Collins, 1988), and Russell’s (2003) theory of emotion. Ekman and Friesen (1978) highlighted the expressive aspects of emotions with their Facial Action Coding System that allowed for “basic emotions ” to be identified by coding specific facial behaviors based on the muscles that produce them. Each movement in the face is referred to as an action unit. There are approximately 58 action units. These prototypical facial patterns were used to identify the emotions of happiness, sadness, surprise, disgust, anger, and fear (Ekman & Friesen, 1978; Elfenbein & Ambady, 2002). The coding system was tested primarily on static pictures rather than on changing expressions over time. Unfortunately, for those researchers interesting in the role of emotions in learning, it is doubtful whether these 6 emotions are frequent and functionally significant in the learning process (Kapoor,Mota, & Picard, 2001). More generally, some researchers have challenged the adequacy of basing a complete theory of emotions on thes