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In complex systems with stochastic components, systems laws often emerge that describe higher level behavior regardless of lower level component configurations. In this paper, emergent laws for describing mechanochemical systems are investigated for processive myosin-actin motility systems. On the basis of prior experimental evidence that longer processive lifetimes are enabled by larger myosin ensembles, it is hypothesized that emergent scaling laws could coincide with myosin-actin contact probability or system energy consumption. Because processivity is difficult to predict analytically and measure experimentally, agent-based computational techniques are developed to simulate processive myosin ensembles and produce novel processive lifetime measurements. It is demonstrated that only systems energy relationships hold regardless of isoform configurations or ensemble size, and a unified expression for predicting processive lifetime is revealed. The finding of such laws provides insight for how patterns emerge in stochastic mechanochemical systems, while also informing understanding and engineering of complex biological systems.

Science education is progressively more focused on employing inquiry-based learning methods in the classroom and increasing scientific literacy among students. However, due to time and resource constraints, many classroom science activities and laboratory experiments focus on simple inquiry, with a step-by-step approach to reach predetermined outcomes. The science classroom inquiry (SCI) simulations were designed to give students real life, authentic science experiences within the confines of a typical classroom. The SCI simulations allow students to engage with a science problem in a meaningful, inquiry-based manner. Three discrete SCI simulations were created as website applications for use with middle school and high school students. For each simulation, students were tasked with solving a scientific problem through investigation and hypothesis testing. After completion of the simulation, 67% of students reported a change in how they perceived authentic science practices, specifically related to the complex and dynamic nature of scientific research and how scientists approach problems. Moreover, 80% of the students who did not report a change in how they viewed the practice of science indicated that the simulation confirmed or strengthened their prior understanding. Additionally, we found a statistically significant positive correlation between students' self-reported changes in understanding of authentic science practices and the degree to which each simulation benefitted learning. Since SCI simulations were effective in promoting both student learning and student understanding of authentic science practices with both middle and high school students, we propose that SCI simulations are a valuable and versatile technology that can be used to educate and inspire a wide range of science students on the real-world complexities inherent in scientific study.

Research on innovation often highlights analogies from sources outside the current problem domain as a major source of novel concepts; however, the mechanisms underlying this relationship are not well understood. We analyzed the temporal interplay between far analogy use and creative concept generation in a professional design team's brainstorming conversations, investigating the hypothesis that far analogies lead directly to very novel concepts via large steps in conceptual spaces (jumps). Surprisingly, we found that concepts were more similar to their preceding concepts after far analogy use compared to baseline situations (i.e., without far analogy use). Yet far analogies increased the team's concept generation rate compared to baseline conditions. Overall, these results challenge the view that far analogies primarily lead to novel concepts via jumps in conceptual spaces and suggest alternative pathways from far analogies to novel concepts (e.g., iterative, deep exploration within a functional space).

Scientific literacy can also be described as a level of public understanding of science that encourages one to act in concert with scientific consensus. Investigating actions concerned with environmental conservation, we examine the context specificity of this form of scientifically literate action and the differential motivations that predict such action across contexts. We report on a large sample of employees of a mixed urban/rural county in the USA, representing a diverse range of careers, who completed an anonymous survey about their environmental conservation actions at home, at work and in the public sphere. Results indicate that individuals engage at different action levels overall and for different reasons across contexts; limited support was found for the importance of perceived knowledge attainment ability in predicting scientifically informed actions. Implications for policy and program designers and scholars interested in scientific literacy are discussed. © The Author(s) 2013.

This work lends insight into the meaning and impact of near and far analogies. A cognitive engineering design study is presented that examines the effect of the distance of analogical design stimuli on design solution generation, and places those findings in context of results from the literature. The work ultimately sheds new light on the impact of analogies in the design process and the significance of their distance from a design problem. In this work, the design repository from which analogical stimuli are chosen is the U.S. patent database, a natural choice, as it is one of the largest and easily accessed catalogued databases of inventions. The near and far analogical stimuli for this study were chosen based on a structure of patents, created using a combination of latent semantic analysis and a Bayesian based algorithm for discovering structural form, resulting in clusters of patents connected by their relative similarity. The findings of this engineering design study are juxtaposed with the findings of a previous study by the authors in design by analogy, which appear to be contradictory when viewed independently. However, by mapping the analogical stimuli used in the earlier work into similar structures along with the patents used in the current study, a relationship between all of the stimuli and their relative distance from the design problem is discovered. The results confirm that near and far are relative terms, and depend on the characteristics of the potential stimuli. Further, although the literature has shown that far analogical stimuli are more likely to lead to the generation of innovative solutions with novel characteristics, there is such a thing as too far. That is, if the stimuli are too distant, they then can become harmful to the design process. Importantly, as well, the data mapping approach to identify analogies works, and is able to impact the effectiveness of the design process. This work has implications not only in the area of finding i

Educational Badges are touted as an alternative assessment that can increase learner motivation. We considered two distinct models for educational badges; merit badges and videogame achievements. To begin unpacking the relationship between badges and motivation, we conducted a study using badges within an intelligent-tutor system for teaching applied mathematics to middle-school students. Our findings indicate that badge earning could be driven by learner motivations and that systems with badges could have a positive effect on critical learner motivations. However, badge acquisition patterns were different across learners with different levels of prior knowledge. Different badge types also affected different learners motivation. Additionally, we believe that our findings are compatible with the research finding that extrinsic motivators have a negative influence on learning. The implication for educational badge designers is that they must consider the ability and motivations of learners when choosing what badges to include in their curricula. We believe our findings exist as one piece of the large research base needed to understand educational badges. © 2013 Association for Educational Communications and Technology.

The process of designing integrated biological systems across scales is difficult, with challenges arising from the modeling, understanding, and search of complex system design spaces. This paper explores these challenges through consideration of how stochastic nanoscale phenomenon relate to higher level systems functioning across many scales. A domain-independent methodology is introduced which uses multi-agent simulations to predict emergent system behavior and structure-behavior-function (SBF) representations to facilitate design space navigation. The methodology is validated through a nanoscale design application of synthetic myosin motor systems. In the multi-agent simulation, myosins are independent computational agents with varied structural inputs that enable differently tuned mechanochemical behaviors. Four synthetic myosins were designed and replicated as agent populations, and their simulated behavior was consistent with empirical studies of individual myosins and the macroscopic performance of myosin-powered muscle contractions. However, in order to configure high performance technologies, designers must effectively reason about simulation inputs and outputs; we find that counter-intuitive relations arise when linking system performance to individual myosin structures. For instance, one myosin population had a lower system force even though more myosins contributed to system-level force. This relationship is elucidated with SBF by considering the distribution of structural states and behaviors in agent populations. For the lower system force population, it is found that although more myosins are producing force, a greater percentage of the population produces negative force. The success of employing SBF for understanding system interactions demonstrates how the methodology may aid designers in complex systems embodiment. The methodology's domain-independence promotes its extendibility to similar complex systems, and in the myosin test case the approach e

Development of design-by-analogy tools is a promising design innovation research avenue. Previously, a method for computationally structuring patent databases as a basis for an automated design-by-analogy tool was introduced. To demonstrate its strengths and weaknesses, a computationally-generated structure is compared to four expert designers' mental models of the domain. Results indicate that, compared to experts, the computationally-generated structure is sensible in clustering of patents and organization of clusters. The computationally-generated structure represents a space in which experts can find common ground/consensus - making it promising to be intuitive/accessible to broad cohorts of designers. The computational method offers a resource-efficient way of usefully conceptualizing the space that is sensible to expert designers, while maintaining an element of unexpected representation of the space. © 2013 Elsevier Ltd. All rights reserved.

Peer review has been viewed as a promising solution for improving studennts' writing, which still remains a great challenge for educators. However, one core problem with peer review of writing is that potentially useful feedbback from peers is not always presented in ways that lead to revision. Our prior investigations found that whether students implement feedback is significantly correlated with two feedback features: localization information and concrete solutions. But focusing on feedback features is time-intensive for researchers and instructors. We apply data mining and Natural Languagee Processing techniques to automatically code reviews for these feedback features. Our results show that it is feasible to provide intelligent suppport to peer review systems to automatically assess students' reviewing performance with respect to problem localization and solution. We also show that similar research conclusions about helpfulness perceptions of feedback across students and different expert types can be drawn from automatically coded data and from hand-coded data. © Earli.

Achieving expertise in any area requires extensive practice and engagement with the subject one desires to master. As not all practice yields good progress, methods must be found that lead learners to practice effectively. Many experts employ highly tailored practice involving metacognitive processes, but novices rarely engage in frequent and explicit metacognitive strategies during practice. As a result, novice progress may be impeded through repetition of systematic errors and ineffective techniques. Our study provides evidence of the effectiveness of teaching metacognition to novice music students through weekly lessons. Thirty-five adolescent students of six instructors were randomly assigned to metacognitive focus or existing practice teaching conditions. Students receiving metacognitive teaching achieved higher performance ratings when compared with students receiving control instruction, even though practice time did not vary between groups. These results suggest that having students explicitly verbalize and reflect on their learning process produces more efficient practice and greater end performance. © 2011 John Wiley & Sons, Ltd.

Ultra-large-scale interactive systems on the Internet have begun to change how teachers prepare for instruction, particularly in regards to resource selection. Consequently, it is important to look at how teachers are currently selecting resources beyond content or keyword search. We conducted a two-part observational study of an existing popular system called TeachersPayTeachers hypothesizing that 'evaluative metadata' (i.e. comments, ratings, and popularity measures) would drive selection of resources. The first part examined patterns in tens of thousands of sales overall, and the second part focused on patterns of sales in one focal topic that could be expert coded. We find that there are significant gaps in available metadata, that some aspects of metadata are closely associated with sales, and that metadata are weak correlates of expert-determined quality. We conclude by making suggestions for additional research and suggesting how ultra-large scale-interactive systems such as TeachersPayTeachers could be used to improve teacher education. © 2011 Elsevier Ltd. All rights reserved.

Many everyday skills are learned by binding otherwise independent actions into a unified sequence of responses across days or weeks of practice. Here we looked at how the dynamics of action planning and response binding change across such long timescales. Subjects (N = 23) were trained on a bimanual version of the serial reaction time task (32-item sequence) for two weeks (10 days total). Response times and accuracy both showed improvement with time, but appeared to be learned at different rates. Changes in response speed across training were associated with dynamic changes in response time variability, with faster learners expanding their variability during the early training days and then contracting response variability late in training. Using a novel measure of response chunking, we found that individual responses became temporally correlated across trials and asymptoted to set sizes of approximately 7 bound responses at the end of the first week of training. Finally, we used a state-space model of the response planning process to look at how predictive (i.e., response anticipation) and error-corrective (i.e., post-error slowing) processes correlated with learning rates for speed, accuracy and chunking. This analysis yielded non-monotonic association patterns between the state-space model parameters and learning rates, suggesting that different parts of the response planning process are relevant at different stages of long-term learning. These findings highlight the dynamic modulation of response speed, variability, accuracy and chunking as multiple movements become bound together into a larger set of responses during sequence learning. © 2012 Verstynen et al.

Drawing inspiration from examples by analogy can be a powerful tool for innovative design during conceptual ideation but also carries the risk of negative design outcomes (e.g., design fixation), depending on key properties of examples. Understanding these properties is critical for effectively harnessing the power of analogy. The current research explores how variations in analogical distance, commonness, and representation modality influence the effects of examples on conceptual ideation. Senior-level engineering students generated solution concepts for an engineering design problem with or without provided examples drawn from the U.S. Patent database. Examples were crossed by analogical distance (near-field vs. far-field), commonness (more vs. less-common), and modality (picture vs. text). A control group that received no examples was included for comparison. Effects were examined on a mixture of ideation process and product variables. Our results show positive effects of far-field and less-common examples on novelty and variability in quality of solution concepts. These effects are not modulated by modality. However, detailed analyses of process variables suggest divergent inspiration pathways for far-field vs. less-common examples. Additionally, the combination of far-field, less-common examples resulted in more novel concepts than in the control group. These findings suggest guidelines for the effective design and implementation of design-by-analogy methods, particularly a focus on far-field, less-common examples during the ideation process. © 2011 American Society of Mechanical Engineers.

The bridge between engineering design and cognitive science research is critical to understand the effectiveness of design methods as implemented by human designers. The study reported in this paper evaluates the effects of design fixation in a group of engineering design faculty, and also provides evidence for approaches to overcome design fixation. Three conditions are compared, a control, a fixation group whom were provided with an example solution, and a defixation group whom were also given materials to mitigate their design fixation. Measures include indicators of design fixation and participant perceptions. The study demonstrates that the engineering design faculty show statistically significant evidence of design fixation, but only partially perceive its effects. This study also indicates that design fixation can be mitigated. The group of participants in this study, due to their background in engineering design research and experience with student design teams, was expected to have more accurate perceptions or awareness of design fixation than the typical participant. Understanding the incongruities between participant perceptions and quantitative design outcomes are particularly of interest to researchers of design methods. For this study, clear evidence exists that designers, even those that study and teach design on a regular basis, do not know when they are being influenced or fixated by misleading or poor information. Copyright © 2010 by ASME.

Current peer-review software lacks intelligence for responding to students' reviewing performance. As an example of an additional intelligent assessment component to such software, we propose an evaluation system that generates assessment on reviewers' reviewing skills regarding the issue of problem localization. We take a data mining approach, using standard supervised machine learning to build classifiers based on attributes extracted from peer-review data via Natural Language Processing techniques. Our work successfully shows it is feasible to provide intelligent support for peer-review systems to assess students' reviewing performance fully automatically.

Writing skills are considered to be critical for academic and professional success (National Commission on Writing, 2004, The neglected r the need for a writing revolution. The College Board). However, a large number of students are not writing well. According to National Assessment of Educational Progress (2002, Writing report card for the nation and the states: National Center for Education Statistics, U.S. Department of Education), 69% of 8th graders and 77% of 12th graders have only basic writing skills. Moreover, 50% of college students cannot produce texts that are relatively free of errors (ICAS, 2002). This unfortunate situation also permeates government and industry sectors. State government employees are found to have weak writing skills (NCW, 2004). Salaried employees in major US firms also lack writing skills (NCW, 2004). © 2010 Springer-Verlag US.

With the shift from conventional to green buildings a need emerges to train staff on how to work within them. Building occupants control many of the green building technologies, which makes it necessary to educate occupants on the differences between using a green building versus a conventional building in order to secure the green building's success. The breadth of information that is necessary for an occupant to know in order to change their behaviors to be in accord with the high performing building they occupy makes it necessary to use a systematic method to reduce the information provided in trainings. This study employs a decision matrix approach as an objective means to narrow the focus of the training. A case study is used to implement the methods developed in this study. A focus group evaluated the effectiveness of the decision matrix. Results from the focus group showed that staff was active in waste reduction behaviors, but not in energy efficient actions. This supported the outcome of the decision matrix in finding relevant, necessary information for the training.

Nonexpert peer-based knowledge refinement is as much helpful as expert-centric knowledge refinement for improving the quality of results. A computer-support system is helpful for facilitating peer-based knowledge refinement, since more peers than experts are required for peer-based refinement to be effective. An experimental study was conducted, which allowed to observe the relative impact of experts versus peers on the quality of codified knowledge intended for use by nonexperts. Quality improvement in technical reports refined by feedback from a subject-matter expert, a nonexpert peer, or multiple nonexpert peers was compared. Individual participants wrote and revised drafts that fulfilled the requirements of the course. The expert and nonexpert reviewers assessed the drafts on the three evaluation dimensions, including flow, logic and insight. The result revealed that flow in multiple peers was significantly better than in single peer, but the logic was same in all conditions.

In the standard form of Wason’s (1960) 2-4-6 task, participants must discover a rule that governs the production of sequences of three numbers. Studies typically show success rates of approximately 20%, which Wason attributed to a cognitive deficit that he labeled ‘confirmation bias’. In Tweney et al.’s (1980) formally equivalent Dual Goal (DG) form of the task, however, success rates are at least double to those seen on the standard task. If this facilitated performance could be accounted for, then this would go some way toward explaining the normally low performance on the standard problem. The present experiment examined two competing accounts of the DG superiority effect: Evans’ (1989) positivity bias explanation, and Wharton, Cheng and Wickens’ (1993) goal complementarity theory. The experiment independently manipulated the number of goals that participants had to explore (a single goal vs. two complementary goals) and the linguistic labels used to provide feedback (DAX and MED vs. ‘fits the rule’ and ‘does not fit the rule’). Results supported the goal complementarity account in that facilitation was evident in both DG conditions irrespective of the polarity of the feedback provided. We also discuss a novel finding: that it is the production of at least a single ‘negative’ triple that is most closely associated with task success.

This paper describes technology and tools for intelligent human-computer interaction (IHCI) where human cognitive, perceptual, motor, and affective factors are modeled and used to adapt the H-C interface. IHCI emphasizes that human behavior encompasses both apparent human behavior and the hidden mental state behind behavioral performance. IHCI expands on the interpretation of human activities, known as W4 (what, where, when, who). While W4 only addresses the apparent perceptual aspect of human behavior, the W5+ technology for IHCI described in this paper addresses also the why and how questions, whose solution requires recognizing specific cognitive states. IHCI integrates parsing and interpretation of nonverbal information with a computational cognitive model of the user, which, in turn, feeds into processes that adapt the interface to enhance operator performance and provide for rational decision-making. The technology proposed is based on a general four-stage interactive framework, which moves from parsing the raw sensory-motor input, to interpreting the user's motions and emotions, to building an understanding of the user's current cognitive state. It then diagnoses various problems in the situation and adapts the interface appropriately. The interactive component of the system improves processing at each stage. Examples of perceptual, behavioral, and cognitive tools are described throughout the paper. Adaptive and intelligent HCI are important for novel applications of computing, including ubiquitous and human-centered computing. © 2002 IEEE.

Students in psychology need to learn to design and analyze their own experiments. However, software that allows students to build experiments on their own has been limited in a variety of ways. The shipping of the first full release of the E-Prime system later this year will open up a new opportunity for addressing this problem. Because E-Prime promises to become the standard for building experiments in psychology, it is now possible to construct a Web-based resource that uses E-Prime as the delivery engine for a wide variety of instructional materials. This new system, funded by the National Science Foundation, is called STEP (System for the Teaching of Experimental Psychology). The goal of the STEP Project is to provide instructional materials that will facilitate the use of E-Prime in various learning contexts. We are now compiling a large set of classic experiments implemented in E-Prime and available over the Internet from http://step.psy.cmu.edu. The Web site also distributes instructional materials for building courses in experimental psychology based on E-Prime.