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2016

Bernacki, M. L., Nokes-Malach, T. J., Richey, E. J., & Belenky, D. M. (2016). Science diaries: A brief writing intervention to improve motivation to learn science. Education Psychology, 36 (1), 26-46. DOI: 10.1080/01443410.2014.895293

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This study investigated the hypothesis that prompting students to self-assess their interest and understanding of science concepts and activities would increase their motivation in science classes. Students were randomly assigned to an experimental condition that wrote self-assessments of their competence and interest in science lessons or a control condition that wrote summaries of those same lessons. Writing activities were 10 minutes long and were given approximately once a week for eighteen weeks. Student motivation was assessed via self-report surveys for achievement goals and interest in science before and after the intervention. Students in the experimental condition showed higher endorsement of mastery goals and reported greater situational interest in science topics after the intervention compared to students who summarized the lessons. Increases in situational interest predicted higher individual interest in the domain. Results indicate an instructional practice requiring just three hours out of a semester of instruction was sufficient to achieve these effects on motivation in science classes.

Chan, J., & Nokes-Malach, T. J. (2016). Situative creativity: Larger physical spaces facilitate thinking of novel uses for everyday objects. Journal of Problem Solving, 9 (1), 29-45. DOI: 10.7771/1932-6246.1184

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People often use spatial metaphors (e.g., think “laterally,” “outside the box”) to describe exploration of the problem space during creative problem solving. In this paper, we probe the potential cognitive underpinnings of these spatial metaphors. Drawing on theories of situative cognition, semantic foraging theory, and environmental psychology, we formulate and test the hypothesis that larger physical spaces can facilitate divergent (but not convergent) processes in problem space exploration. Across two experiments, participants worked on a battery of problem solving tasks intended to represent divergent (alternative uses, shape invention) and convergent (remote associates, letter extrapolation) problem solving processes in either a large or a small room. In Experiment 1, participants in the larger room produced more novel alternative uses for everyday objects, and created more novel shape inventions, but generated less practical alternative uses, than participants in the smaller room. In Experiment 2, participants in the larger room (including a variant larger room) also produced more novel alternative uses for everyday objects, and less practical alternative uses, than participants in a small room, but did not create more novel shape inventions. These results suggest that spatial metaphors for problem space exploration may reflect meaningful cognitive phenomena: People may be able to search more broadly in a problem space if they are in an environment where broad physical search is a salient affordance; however, this effect appears to be relatively small and may depend on having sufficiently motivated participants.

Greeno, J. G., & Nokes-Malach, T. J. (2016). Some early contributions to the situative perspective on learning and cognition. In M. A. Evans, M J. Packer, and R. K. Sawyer (Eds.), Reflections on the Learning Sciences (pp. 59-75). Cambridge University Press. New York, NY.

 

2015

Bernacki, M. L., Nokes-Malach, T. J., & Aleven, V. (2015). Examining self-efficacy during learning: Variability and relations to performance, behavior, and learning. Metacognition and Learning, 10, 99-117. DOI: 10.1007/s11409-014-9127-x

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Self-regulated learning (SRL) theorists propose that learners’ motivations and cognitive and metacognitive processes interact dynamically during learning, yet researchers typically measure motivational constructs as stable factors. In this study, self-efficacy was assessed frequently to observe its variability during learning and how learners’ efficacy related to their problem-solving performance and behavior. Students responded to self-efficacy prompts after every fourth problem of an algebra unit completed in an intelligent tutoring system. The software logged students’ problem-solving behaviors and performance. The results of stability and change, path, and correlational analyses indicate that learners’ feelings of efficacy varied reliably over the learning task. Their prior performance (i.e., accuracy) predicted subsequent self-efficacy judgments, but this relationship diminished over time as judgments were decreasingly informed by accuracy and increasingly informed by fluency. Controlling for prior achievement and self-efficacy, increases in efficacy during one problemsolving period predicted help-seeking behavior, performance, and learning in the next period. Findings suggest that self-efficacy varies during learning, that students consider multiple aspects of performance to inform their efficacy judgments, and that changes in efficacy influence self-regulated learning processes and outcomes.

Nokes-Malach, T. J., Richey, J. E., & Gadgil, S. (2015). When is it better to learn together? Insights from research on collaborative learning. Educational Psychology Review, 27, 645-656. DOI: 10.1007/s10648-015-9312-8

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Although collaboration is often considered a beneficial learning strategy, research examining the claim suggests a much more complex picture. Critically, the question is not whether collaboration is beneficial to learning, but instead how and when collaboration improves outcomes. In this paper, we first discuss the mechanisms hypothesized to support and hinder group learning. We then review insights and illustrative findings from research in cognitive, social, and educational psychology. We conclude by proposing areas for future research to expand theories of collaboration while identifying important features for educators to consider when deciding when and how to include collaboration in instructional activities.

Nokes-Malach, T. J., & Richey, J. E. (2015). Knowledge transfer. In R. Scott and S. Kosslyn (Eds.), Emerging Trends in the Social and Behavioral Sciences. ,Hoboken, NJ: John Wiley and Sons.

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Controversy regarding the nature and frequency of knowledge transfer has received significant attention for more than a century, and this debate has sparked advances in our theoretical understanding of transfer as well as educational practices designed to promote it. We review the classical cognitive approach to studying transfer and highlight several important critiques of that approach regarding issues of context, assessment, and individual differences. These critiques have pushed research to improve understanding of the learning processes that facilitate transfer, the application processes that enact it, and the measurement of it. Research investigating the relationship between achievement goals and transfer serves as an example of the ways issues of context and individual differences are being integrated into the study of transfer. Future work on transfer should continue to refine and clarify how we define, assess, and promote it.

Richey, J. E., & Nokes-Malach, T. J. (2015). Comparing four instructional techniques for promoting robust learning. Educational Psychology Review , 27, 181-218.

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Robust knowledge serves as a common instructional target in academic settings. Past research identifying characteristics of experts’ knowledge across many domains can help clarify the features of robust knowledge as well as ways of assessing it. We review the expertise literature and identify three key features of robust knowledge (deep, connected, and coherent) and four means of assessing these features (perception, memory, problem solving, and transfer). Focusing on the domains of math and science learning, we examine how four instructional techniques—practice, worked examples, analogical comparison, and self-explanation—can promote key features of robust knowledge and how those features can be assessed. We conclude by discussing the implications of this framework for theory and practice.

Richey, J. E., Zepeda, C. D., & Nokes-Malach, T. J. (2015). Transfer effects of prompted and self-reported analogical comparison and self-explanation. In D. Noelle and R. Dale (Eds.) Proceedings of the 37th Annual Conference of Cognitive Science Society, Austin, Texas: Cognitive Science Society.

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We compared types of transfer facilitated by instructions to engage in analogical comparison or self-explanation. Participants received learning materials and worked examples with prompts supporting analogical comparison, self-explanation, or instructional explanation study. Learners also self-reported their use of analogical comparison and self-explanation on a series of questionnaires. We evaluated condition effects on self-reports and transfer, and the relations between self-reports and transfer. Receiving materials with analogical-comparison support and reporting greater levels of analogical comparison were both associated with worse transfer performance, while reporting greater levels of self-explanation was associated with better performance. Learners’ self-reports of analogical comparison and self-explanation were not related to condition assignment, suggesting that the questionnaires did not measure the same processes promoted by the intervention, or that individual differences are robust even when learners are instructed to engage in analogical comparison or self-explanation.

Zepeda, C., Richey, J. E., Ronevich, P., & Nokes-Malach, T. J. (2015). Direct instruction of metacognition benefits adolescent science learning, transfer, and motivation: An in vivo study. Journal of Educational Psychology , 107 (4), 954-970. DOI: 10.1037/edu0000022

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Prior studies have not tested whether an instructional intervention aimed at improving metacognitive skills results in changes to student metacognition, motivation, learning, and future learning in the classroom. We examined whether a 6-hr intervention designed to teach the declarative and procedural components of planning, monitoring, and evaluation could increase students’ metacognition, motivation, learning, and preparation for future learning for middle school science. Forty-six eighth-grade students were randomly assigned to either a control group, which received extensive problem-solving practice, or an experimental group, which received more limited problem-solving practice along with metacognitive instruction and training. Results revealed that those who received the metacognitive instruction and training were less biased when making metacognitive judgments, p=.03, d=0.65, endorsed higher levels of motivation after instruction (e.g., there was a large effect on task value, p=.006, d=0.87), performed better on a conceptual physics test, p=.03, d=0.64, and performed better on a novel self-guided learning activity, p=.007, d=0.87. This study demonstrates that metacognitive instruction can lead to better self-regulated learning outcomes during adolescence, a period in which students’ academic achievement and motivation often decline.

2014

Bernacki, M. L., Aleven, V., & Nokes-Malach, T. J. (2014). Stability and change in adolescents' task-specific achievement goals and implications for learning mathematics with intelligent tutors. Computers in Human Behavior, 37, 73-80.

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Individuals’ achievement goals are known to influence learning behaviors and academic achievement. However, prior research also indicates that undergraduates’ achievement goals for psychology coursework vary from assignment to assignment. The effect of stability of achievement goals on learning behaviors and outcomes has yet to be explored. This study examined how adolescents’ achievement goals varied over mathematics units completed in an intelligent tutoring system, and whether strength or variability in achievement goals influenced behavior or achievement. At the group level, achievement goals correlated significantly from unit to unit; mean scores were not significantly different over time. However, individuals’ goal scores changed reliably across units. No relationships were found between the strength of students’ achievement goal scores and learning behaviors or performance. However, students with stable mastery approach goals achieved better grades than those with more variable mastery-approach goals. Students with stable performance-approach goals engaged in fewer help-seeking behaviors than those with variable performance approach goals.

Fancsali, S. E.,Bernacki, M. L., Nokes-Malach, T. J., Yudelson, M., & Ritter, S. (2014). Goal orientation, self-efficacy, and “online measures” in intelligent tutoring systems. In P. Bello, M. Guarini, M. McShane, and B. Scassellati (Eds.) Proceedings of the 36th Annual Conference of Cognitive Science Society (pp. 2169-2174). Austin, Texas: Cognitive Science Society.

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While goal orientation and related factors like learner self-efficacy are of great interest to learning science researchers, some voice concerns regarding the measurement of such factors using self-report questionnaires. To address these concerns, recent work has explored the use of behavioral indicators like hint-seeking and glossary use in intelligent tutoring systems like Carnegie Learning’s Cognitive Tutor® (CT) as alternative, “online” measures of goal orientation. We re-examined this approach by measuring 273 CT users’ achievement goals and self-efficacy judgments via embedded questionnaires and their hint-seeking and glossary use via log data. Using graphical causal models and linear structural equation models to observe structural relationships among goal orientations, self-efficacy, behaviors, and learning outcomes, we found that tracing orientations via “online measures” is more nuanced than perhaps previously appreciated. We describe complex relations observed in the model among motivations, behaviors, and outcomes and discuss the implications for the online measurement of motivation.

Richey, J. E.,Bernacki, M. L., Belenky, D. B., & Nokes-Malach, T. J. (2014). Relating a task-based, behavioral measure of achievement goals to self-reported goals and performance in the classroom. In P. Bello, M. Guarini, M. McShane, and B. Scassellati (Eds.) Proceedings of the 36th Annual Conference of Cognitive Science Society (pp. 1287-1292). Austin, Texas: Cognitive Science Society.

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Achievement goals are a powerful construct for understanding students’ classroom experiences and performance, yet most work examining achievement goals relies on self-report measures gathered through questionnaires. The current work aims to assess achievement goals using a task choice embedded within a typical classroom activity. Results show the behavioral measure of achievement goals predicts performance on the task, while self-reported achievement goals do not. Self-reported achievement goals predict quarterly grades, while the behavioral measure of achievement goals does not. This work supports the viability of a behavioral measure and suggests the achievement goals that students adopt at a task level may be different from their general class achievement goals. Using complementary achievement goal measures may improve understanding of how achievement goals relate to student behaviors and academic achievement.

Richey, J. E.,Nokes-Malach, T. J., & Wallace, A. (2014). Achievement goals, observed behaviors, and performance: Testing a mediation model in a college classroom. In P. Bello, M. Guarini, M. McShane, and B. Scassellati (Eds.) Proceedings of the 36th Annual Conference of Cognitive Science Society (pp. 1293-1298). Austin, Texas: Cognitive Science Society.

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Achievement goals have been examined extensively in relationship to self-reported learning behaviors and achievement, yet very little work has observed the behaviors through which achievement goals might influence learning and performance. We collected fine-grained behavioral data to assess students’ activities throughout the semester in a college psychology course, and then used one learning behavior, access to course outlines, to explain the relationship between self-reported achievement goals and grades. Results suggest that downloading course outlines partially mediates the relationship between goals and grades. Identifying how goals influence achievement through observable behaviors contributes to the theoretical understanding of achievement goals while also suggesting practical implications for instructors.

2013

Alfieri, L., Nokes-Malach, T. J., & Schunn, C. D. (2013). Learning through case comparisons: A meta-analytic review. Educational Psychologist, 48 (2), 87-113.

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Over the past 20 years, there has been much research on how people learn from case comparisons. This work has implemented comparison activities in a variety of different ways across a wide range of laboratory and classroom contexts. In an effort to assess the overall effectiveness of case comparisons across this diversity of implementation and contexts and to explore what variables might moderate learning outcomes, we conducted a meta-analysis of 57 experiments with 336 tests. Random effects analyses of the 336 tests revealed that case comparison activities commonly led to greater learning outcomes than other forms of case study including sequential, single case, and nonanalogous, as well as traditional instruction and control (d = .50), 95% CI [.44, .56]. Of 15 potential moderators, four variables were found to reliably moderate the effectiveness of case comparisons: the objective of the comparison, the presentation of a principle, the content, and the lag between the comparison and testing. Asking learners to find similarities between cases, providing principles after the comparisons, using perceptual content, and testing learners immediately are all associated with greater learning.We conclude with a discussion of the theoretical and practical implications of these results for cognitive theory and classroom practice.

Belenky, D. M., & Nokes-Malach, T. J. (2013). Mastery-approach goals and knowledge transfer: An investigation into the effects of task structure and framing instructions. Learning and Individual Differences, 25, 21-34.

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Although prior work has shown that mastery-approach achievement goals are related to positive learning behaviors (e.g., more interest, perseverance, and self-regulation), less is known about how these goals interact with instruction to influence knowledge transfer. To address these issues we conducted a laboratory experiment investigating how two aspects of the instructional environment, the task structure (tell-and-practice direct instruction vs. minimally-guided open-ended invention activities) and the task framing (mastery vs. performance), affected students' task-based mastery goal adoption and transfer when learning statistics. The results showed that structure was more effective than framing in manipulating students' masteryapproach goal adoption. In addition, students' existing mastery-approach orientations for mathematics strongly predicted knowledge transfer for all of the instructional conditions except for students given invention activities with a performance framing. For these students, the relationship between mastery-approach orientation and transfer was not observed, indicating that this condition makes transfer more likely for those lower in mastery-approach orientation. The results are discussed in terms of the implications for theories of achievement goal motivation, knowledge transfer, and instruction

Belenky, D. M., & Nokes-Malach, T. J. (2013). The role of achievement goal motivation in self-explanation and knowledge transfer. In M. Knauff, M. Pauen, N. Sebanz, and I. Wachsmuth (Eds.) Proceedings of the Thirty-Fifth Annual Conference of Cognitive Science Society (pp. 1881-1886). Austin, Texas: Cognitive Science Society.

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Self-explanation is an important constructive cognitive process that helps students learn in such a way that they can flexibly transfer their knowledge to solve novel problems (Chi, Bassok, Lewis, Reimann, & Glaser, 1989). However, research has not addressed what leads students to spontaneously self-explain, in the absence of prompting. The present study experimentally manipulates student motivation (in terms of achievement goals) and measures what influence this has on self-explanation and transfer. Participants (N =140) received goal framings that reflected either a mastery-approach goal (striving to develop one’s understanding), a performance-approach goal (an aim to outperform others), a performance-avoidance goal (avoid doing worse than others) or a no-goal control. This framing was applied to a set of learning and test tasks on basic statistics, which participants completed while thinking aloud. Results showed a benefit for a performance-avoidance condition in terms of both higher levels of self-explanation and transfer. This unexpected result is discussed in terms of theories of motivation and learning, and their potential impact on educational practice.

Bernacki, M. L., Nokes-Malach, T. J., & Aleven, V. (2013). Fine-grained assessment of motivation over long periods of learning with an intelligent tutoring system: Methodology, advantages, and preliminary results. In R. Azevedo and V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies (pp. 629-644).

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Models of self-regulated learning (SRL) describe the complex and dynamic interplay of learners' cognitions, motivations, and behaviors when engaged in a learning activity. Recently, researchers have begun to use fine-grained behavioral data such as think aloud protocols and log-file data from educational software to test hypotheses regarding the cognitive and metacognitive processes underlying SRL. Motivational states, however, have been more difficult to trace through these methods and have primarily been studied via pre- and posttest questionnaires. This is problematic because motivation can change during an activity or unit and without fine-grained assessment, dynamic relations between motivation, cognitive, and meta-cognitive processes cannot be studied. In this chapter we describe a method for collecting fine-grained assessments of motivational variables and examine their association with cognitive and metacognitive behaviors for students learning mathematics with intelligent tutoring systems. Students completed questionnaires embedded in the tutoring software before and after a math course and at multiple time points during the course. We described the utility of this method for assessing motivation and use these assessments to test hypotheses of self-regulated learning and motivation. Learners' reports of their motivation varied across comain and unit-level assessments and were differently predictive of learning behaviors.

Li, M., Frieze, I. H., Nokes-Malach, T. J., & Cheong, J. (2013). Do friends help your study? Mediating processes between social relations and academic motivation. Social Psychology of Education, 16 (1), 129-149.

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Previous studies suggest that social relations can increase one’s motivation to learn in school. However, other evidence showed that having more friends may also distract from one’s academic involvement. To understand the mechanisms behind this apparent contradiction, this study identified and tested the effects of a potentially important positive and negative mediator between social relations and academic motivation.A total of 226 university studentswere used to test the hypothesized path model. Results showed that the impact of social relations on students’ academic motivation was negatively mediated by alcohol use, but positively mediated by their place attachment to the university, although the model fit differed for women and men. Implications for social relations, school policy and freshman orientation programs are discussed.

Nokes-Malach, T. J., VanLehn, K., Belenky, D., Lichtenstein, M., & Cox, G. (2013). Coordinating principles and examples through analogy and self-explanation. European Journal of Education of Psychology, 28(4), 1237-1263.

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Research on expertise suggests that a critical aspect of expert understanding is knowledge of the relations between domain principles and problem features. We investigated two instructional pathways hypothesized to facilitate students’ learning of these relations when studying worked examples. The first path is through self-explaining how worked examples instantiate domain principles and the second is through analogical comparison of worked examples. We compared both of these pathways to a third instructional path where students read worked examples and solved practice problems. Students in an introductory physics class were randomly assigned to one of three worked example conditions (reading, self-explanation, or analogy) when learning about rotational kinematics and then completed a set of problem solving and conceptual tests that measured near, intermediate, and far transfer. Students in the reading and self-explanation groups performed better than the analogy group on near transfer problems solved during the learning activities. However, this problem solving advantage was short lived as all three groups performed similarly on two intermediate transfer problems given at test. On the far transfer test, the self-explanation and analogy groups performed better than the reading group. These results are consistent with the idea that self-explanation and analogical comparison can facilitate conceptual learning without decrements to problem solving skills relative to a more traditional type of instruction in a classroom setting.

Nokes-Malach, T. J., & Mestre, J. (2013). Toward a model of transfer as sense-making. Educational Psychologist, 48(3), 184-207.

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We propose a novel theoretical framework for knowledge transfer that consists of constructing a representation of context, generating a solution, and evaluating whether the solution makes sense given that context. Sense-making and satisficing processes determine when the transfer cycle begins and ends, and the classical mechanisms of transfer (identical rules, analogy, knowledge compilation, and constraint violation) are triggered under different representations of context. We use this framework to interpret student transfer behaviors with examples from the literature in the domains of physics and mathematics. We view this framework as a research tool to explore the dynamic nature and complexity of transfer by highlighting critical features and issues that a complete model of transfer must address. We describe how the framework relates to the classical and recent alternative approaches to understanding transfer and discuss the implications for theory development and instruction.

Richey, J., E., & Nokes-Malach, T. J. (2013). How much is too much? Explanatory text effects on conceptual learning and motivation. Learning and Instruction, 25, 104-121.

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A central goal of the learning sciences is to discover principles that determine the optimal amount of instructional assistance to support robust learning (Koedinger & Aleven, 2007). We examined learning outcomes from providing and withholding stepwise instructional explanations as students studied worked examples and solved physics problems. We hypothesized that students would acquire more conceptual knowledge from withholding instructional explanations because they would be more likely to engage in constructive cognitive activities to understand the problem-solving steps, whereas providing instructional explanations might suppress such activities. Furthermore, we examined the roles of prior knowledge and student motivation in determining learning outcomes. Across three experiments, students in the withholding conditions showed greater conceptual learning than students in the providing conditions. Additionally, achievement goal orientations were more predictive of learning for the withholding conditions than the providing conditions. We discuss how the interactions between prior knowledge, motivation, and instruction can support learning and transfer.

2012

Belenky, D. M., & Nokes-Malach, T. J. (2012). Motivation and transfer: The role of mastery-approach goals in preparation for future learning. Journal of the Learning Sciences, 21(3), 399-432.

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The study of knowledge transfer rarely draws upon motivational constructs in empirical work. We investigated how students’ achievement goals interact with different forms of instruction to promote transfer, defined as preparation for future learning (Bransford & Schwartz, 1999). Students were given either invention or tell-and-practice activities when learning statistics concepts and their achievement goal orientations were measured at the beginning of the experiment. We also assessed students’ goals during the learning activity. We predicted that students who entered the experiment with a high mastery-approach goal orientation would lead to improved transfer regardless of instruction. We also hypothesized that invention activities would lead to higher mastery-approach goal adoption for the task, as students would focus on trying to understand the material. Finally, because we expected that invention activities would promote mastery goals in the task, we predicted a moderating effect of invention activities, such that there would be a smaller effect for students’ initial mastery-approach goal orientation on transfer for those who invent compared to those who received tell-and-practice instruction. All three hypotheses were supported. Results are discussed in terms of contributions to research on knowledge transfer, achievement goals, and educational practice.

Gadgil, S., Nokes-Malach, T. J., & Chi, M. T. H. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22 (1), 47-61.

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Prior research on conceptual change has identified multiple kinds of misconceptions at different levels of representational complexity including false beliefs, flawed mental models, and incorrect ontological categories. We hypothesized that conceptual change of a mental model requires change in the system of relations between the features of the prior model. To test this hypothesis, we compared instruction aimed at revising knowledge at the mental model level called holistic confrontation e in which the learner compares and contrasts a diagram of his or her flawed mental model to an expert model e to instruction aimed at revising knowledge at the false belief level e in which the learner is prompted to self-explain the expert model alone. We found evidence that participants who engaged in holistic confrontation were more likely to acquire a correct mental model, and a deeper understanding of the systems of relations in the model than those who were prompted to self-explain the expert model. The results are discussed in terms of their implications for science instruction.

Nokes-Malach, T. J., Meade, M. L., & Morrow, D. G. (2012). The effect of expertise on collaborative problem solving. Thinking & Reasoning, 18(1), 32-58.

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Why do some groups succeed where others fail? We hypothesise that collaborative success is achieved when the relationship between the dyad’s prior expertise and the complexity of the task creates a situation that affords constructive and interactive processes between group members. We call this state the zone of proximal facilitation in which the dyad’s prior knowledge and experience enables them to benefit from both knowledge-based problem-solving processes (e.g., elaboration, explanation, and error correction) and collaborative skills (e.g., creating common ground, maintaining joint attention to the task). To test this hypothesis we conducted an experiment in which participants with different levels of aviation expertise, experts (flight instructors), novices (student pilots), and non-pilots, read flight problem scenarios of varying complexity and had to identify the problem and generate a solution with either another participant of the same level of expertise or alone. The non-pilots showed collaborative inhibition on problem identification in which dyads performed worse than their predicted potential for both simple and complex scenarios, whereas the novices and experts did not. On solution generation the non-pilot and novice dyads performed at their predicted potential with no collaborative inhibition on either simple or complex scenarios. In contrast, expert dyads showed collaborative gains, with dyads performing above their predicted potential, but only for the complex scenarios. On simple scenarios the expert dyads showed collaborative inhibition and performed worse than their predicted potential. We discuss the implications of these results for theories of collaborative problem solving.

2011

Nokes, T. J., Hausmann, R. G. M., VanLehn, K., & Gershman, S. (2011). Testing the instructional fit hypothesis: The case of self-explanation prompts. Instructional Science, 39 (5), 645-666.

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Cognitive science principles should have implications for the design of effective learning environments. The self-explanation principle was chosen for the current work because it has developed significantly over the last 20 years. Early formulations hypothesized that self-explanation facilitated inference generation to supply missing information about a concept or target skill, whereas later work hypothesized that self-explanation facilitated mental-model revision (Chi, Handbook of research on conceptual change, 2000). To better understand the complex relationship between prior knowledge, cognitive processing, and changes to a learner’s representation, two classes of self-explanation prompts (gap-filling and mental-model revision) were tested in the domain of physics problem solving. Prompts designed to focus the learner on gap-filling led to greater learning and a reduction in the amount of tutoring assistance required to solve physics problems. The results are interpreted as support for the instructional fit hypothesis—the idea that the efficacy of instruction is contingent on the match between the cognitive processing that the instruction elicits, how those processes modify the underlying knowledge representations for the task, and the utility of those representations for the task or problem.

Jang, J., Schunn, C. D., & Nokes, T. J. (2011). Spatially distributed instructions reduce load to improve learning outcomes and efficiency. Journal of Educational Psychology, 103 (1), 60-72.

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Learning requires applying limited working memory and attentional resources to intrinsic, germane, and extraneous aspects of the learning task. To reduce the especially undesirable extraneous load aspects of learning environments, cognitive load theorists suggest that spatially integrated learning materials should be used instead of spatially separated materials, thereby reducing the split-attention effect (Sweller & Chandler, 1994). Recent work, however, has suggested a new distinction between two common formats of spatially separated displays: spatially distributed versus spatially stacked (Jang & Schunn, 2010). Moreover, a distinction between instructions and learning task materials has rarely been made. Across two studies with 106 college students (56 in Study 1 and 50 in Study 2), we compared spatially distributed (multiple sources of information are placed side by side) versus spatially stacked (only one at the top is visible) instructions, without changing the learning task materials, on both task performance and learning. With materials more typical of practice, Study 1 showed that the distributed-display instructions led learners to more efficient learning; learners finished the task faster and scored higher in the overall learning test. With materials more tightly controlled for spatial format per se, Study 2 replicated the effect and found that the benefit of the distributed instructions appeared to be associated with changes in cognitive load. Implications for educational practice are discussed.

Nokes, T. J. & Belenky, D. M. (2011). Incorporating motivation into a theoretical framework for knowledge transfer. In J. P. Mestre and B. H. Ross (Eds.), Cognition and Education: The Psychology of Learning and Motivation: Advances in Research and Theory. Volume 55, pp. 109-135.

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Knowledge transfer is critical to successfully solving novel problems and performing new tasks. Several theories have been proposed to account for how, when, and why transfer occurs. These include both classical cognitive theories such as identical rules, analogy, and schemas, as well as more recent views such as situated transfer and preparation for future learning. Although much progress has been made in understanding specific aspects of transfer phenomena, important challenges remain in developing a framework that can account for both transfer successes and failures. Surprisingly, few of these approaches have integrated motivational constructs into their theories to address these challenges. In this chapter, we propose a theoretical framework that builds on the classical cognitive approaches and incorporates aspects of competence motivation. In the first part of the chapter we review the classical and alternative views of transfer and discuss their successes and limitations. We then describe our transfer framework that begins to address some of the issues and questions that are raised by the alternative views. In the second part, we describe how our proposed framework can incorporate aspects of competence motivation—specifically, students’ achievement goals. We then describe an initial test of the framework and the implications for both psychological theory and educational practice.

2010

Asterhan S. C., Schwarz, B. B., Butera, F., Darnon, C., Nokes, T. J., Levine, J. M., Belenky, D. M., Gadgil, S., Resnick, L. B., & Sinatra, G. (2010). Motivation and affect in peer argumentation and socio-cognitive conflict. In S. Goldman and J. Pellegrino (Eds.), Proceedings of the International Conference for the Learning Sciences ICLS 2010, Volume 2 (pp. 211-218). ISLS, USA

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Whereas the cognitive processes and effects of collaborative learning have been intensively studied within the Learning Sciences, little attention has been paid to the way motivational and emotional factors may affect them. In this symposium, we present recent findings from three independent lines of research that focus on the way motivation and affect shape the interaction between peer learners and how this, in turn, affects cognitive gains from this interaction. All three presentations focus on learning within a socio-cognitive conflict task design, while drawing on different data sources, each highlighting different aspects of the interaction process: (1) Students self-reported perceptions of the self, the other and the interaction; (2) Epistemic and motivational features of verbal dialogue content; and (3) Interactants’ emotional reactions using facial signals and content-free vocal parameters of speech. The findings shed new light on how motivational and affective factors may promote or inhibit productive interactions in the face of socio-cognitive conflict.

Belenky, D.M., & Nokes, T.J. (2010). Optimizing learning environments: An individual differences approach to learning and transfer. In S. Ohlsson, and R. Catrambone (Eds.), Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society (pp. 459-464). Portland, OR: Cognitive Science Society.

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Prior work has found that the type of learning activity (direct instruction or invention) interacts with achievement goals (mastery or performance-oriented) such that invention tasks can help facilitate mastery goal adoption and knowledge transfer (Belenky & Nokes, 2009). In the current study, we investigated how robust the effect is, and whether explicit manipulations of the task goals can produce a similar effect. We conducted an experiment with 98 college students in which achievement goals were measured, while task goals and task structure were manipulated between subjects. Results indicated that task structure was generally a more effective way of influencing which achievement goals are adopted within a learning activity. However, task goals that promoted an evaluative context interfered with transfer for mastery-oriented learners from invention activities. The results are interpreted in relation to theories of regulatory fit and multiple goal hierarchies.

Gadgil, S. M. & Nokes, T. J. (2010). Collaborative facilitation through error-detection: A classroom experiment. In S. Ohlsson, and R. Catrambone (Eds.), Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society (pp. 2583-2588). Portland, OR: Cognitive Science Society.

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Prior work has shown that individuals working in groups often perform worse than individuals working alone, a finding commonly referred to as collaborative inhibition. In the current work we examine whether engaging in error correction processes can mitigate or eliminate the collaborative inhibition effect and perhaps even facilitate collaborative facilitation. Participants engaged in a writing error-detection and revision task while working either with a partner or individually. On the error-detection task, dyads found more structural flaws in the text, whereas individuals found more surface flaws. Moreover, when comparing dyads nominal groups the dyads did not show the collaborative inhibition effect. A similar pattern of results was found on the revision task. The results are discussed in terms of the underlying cognitive and social processes that support successful collaboration.

Nokes, T. J., & Ash, I. K. (2010). Investigating the role of instructional focus in incidental pattern learning. The Journal of General Psychology, 137 (1), 84-113.

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The authors used a novel dual-component training procedure that combined a serial reaction time task and an artificial grammar learning task to investigate the role of instructional focus in incidental pattern learning. In Experiment 1, participants either memorized letter strings as a primary task and reacted to the stimuli locations as a secondary task or vice versa. In Experiment 2, participants were given the same dual-component stimuli but performed only one of the two training tasks. Instructional focus affected the amount of learning and the likelihood of acquiring explicit knowledge of the underlying pattern. However, the effect of instructional focus varied for the different types of stimuli. These results are discussed in terms of the role of focused attention in incidental learning.

Nokes, T. J., Schunn, C. D., & Chi, M. T. H. (2010). Problem solving and human expertise. In P. Peterson, E. Baker, and B. McGraw (Eds.) International Encyclopedia of Education, Volume 5 (pp. 265-272). Oxford: Elsevier.

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Developing high-level problem solving skill is critical to successfully performing a variety of tasks in both formal (e.g., school and work) and informal (e.g., home) settings. One way to understand how people acquire such skills is to examine research on expertise in problem solving. In this chapter we provide an integrative review of the psychological research on expert problem solving describing in detail what it is, how it is acquired, and the implications for education and instruction.

2009

Belenky, D. M., & Nokes, T. J. (2009). Examining the role of manipulatives and metacognition on engagement, learning, and transfer. Journal of Problem Solving, 2 (2), 102-129.

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How does the type of learning material impact what is learned? The current research investigates the nature of students’ learning of math concepts when using manipulatives (Uttal, Scudder, & DeLoache, 1997). We examined how the type of manipulative (concrete, abstract, none) and problem-solving prompt (metacognitive or problem-focused) affect student learning, engagement, and knowledge transfer. Students who were given concrete manipulatives with metacognitive prompts showed better transfer of a procedural skill than students given abstract manipulatives or those given concrete manipulatives with problem-focused prompts. Overall, students who reported low levels of engagement showed better learning and transfer when getting metacognitive prompts, whereas students who reported high levels of engagement showed better learning and transfer when getting the problem-focused prompts. The results are discussed in regards to their implications for education and instruction.

Belenky, D. M., & Nokes, T. J. (2009). Motivation and Transfer: The role of achievement goals in preparation for future learning. In N. Taatgen, H. van Rijn, L., Shoemaker, and J. Nerbonne (Eds.), Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (pp. 1163-1168). Amsterdam, Netherlands: Cognitive Science Society.

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Knowledge transfer is critical for solving novel problems and performing new tasks. Recent work has shown that invention activities can promote flexible learning, leading to better transfer after instruction (Schwartz & Martin, 2004). The current project examines the role of achievement goals in promoting transfer. Results indicate that engaging in invention activities before being shown the correct method is beneficial for transfer, regardless of initial goal orientation (mastery versus performance), while a mastery orientation must be present for students to transfer from a direct instruction activity. Implications of these results for theories of learning and transfer are discussed

Gadgil, S., & Nokes, T. J. (2009). Analogical scaffolding in collaborative learning. In N. Taatgen, H. van Rijn, L., Shoemaker, and J. Nerbonne (Eds.), Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (pp. 3115-3120). Amsterdam, Netherlands: Cognitive Science Society.

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Past research has shown that collaboration can facilitate learning and problem solving (e.g., Azmitia, 1988; Barron, 2000). In the current work, we compared the effects of three collaborative learning conditions: prompts that encourage analogical comparison between examples, prompts that guide sequentially studying single examples, and traditional instruction (practicing problem solving), as students learned to solve physics problems in the domain of rotational kinematics. Preliminary results showed a significant problem type by condition interaction effect.

Hausmann, R. G. M., Nokes, T. J., VanLehn, K., & Gershman, S. (2009). The design of self-explanation prompts: The fit hypothesis. Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (pp. 2626-2631). Amsterdam, Netherlands: Cognitive Science Society.

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Cognitive science principles should have implications for the design of effective learning environments. The self explanation principle was chosen for the current project because it has developed significantly over the past few years. Early formulations suggested that self-explanation facilitated inference generation to supply missing information about a concept or target skill, whereas later work suggested that selfexplanation facilitated mental-model revision (Chi, 2000). To better understand the complex interaction between prior knowledge, cognitive processing, and changes to a learner’s representation, three different types of self-explanation prompts were designed and tested in the domain of physics problem solving. The results suggest that prompts designed to focus on problem-solving steps led to a sustained level of engagement with the examples and a reduction in the number of hints needed to solve the physics problems.

Hausmann, R. G. M., Nokes, T. J., VanLehn, K., & Van de Sande, B. (2009). Collaborative dialog while studying worked-out examples. Proceedings of the Fourteenth International Conference on Artificial Intelligence in Education.

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Self-explaining is a beneficial learning strategy for studying worked-out examples because it either supplies missing information through the generation of inferences or because it provides a mechanism for repairing flawed mental models. Although self-explanation is generated with the purpose of helping the individual, is it also helpful to produce explanations in a collaborative setting? Can individuals help each other infer missing information or repair their flawed mental models collaboratively? To find out, we coded the dialog from dyads collaboratively studying examples and contrasted it with individuals studying examples alone. The results suggest that dyads were more likely to attempt to reconcile the examples with their attempted solutions, and avoid shallow processing of examples through paraphrasing.

Meade, M. L., Nokes, T. J., & Morrow, D. G. (2009). Expertise promotes facilitation on a collaborative memory task. Memory, 17 (1), 39-48.

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The effect of expertise on collaborative memory was examined by comparing expert pilots, novice pilots, and non- pilots. Participants were presented with aviation scenarios and asked to recall the scenarios alone or in collaboration with a fellow participant of the same expertise level. Performance in the collaborative condition was compared to nominal group conditions (i.e., pooled individual performance). Results suggest that expertise differentially impacts collaborative memory performance. Non-experts (non-pilots and novices) were relatively disrupted by collaboration, while experts showed a benefit of collaboration. Verbal protocol analyses identified mechanisms related to collaborative skill and domain knowledge that may underlie experts’ collaborative success. Specifically, experts were more likely than non-experts to explicitly acknowledge partner contributions by repeating back previously made statements, as well as to further elaborate on concepts in those contributions. The findings areinterpreted according to the retrieval strategy disruption theory of collaborative memory and theories of grounding in communication.

Mestre, J. P., Ross, B. H., Brookes, D. T., Smith, A. D., & Nokes, T. J. (2009). How cognitive science can promote conceptual understanding in physics classrooms. In I. M. Saleh and M. S. Khine (Eds.), Fostering scientific habits of mind: Pedagogical knowledge and best practices in science education (pp. 145-171). Rotterdam, Netherlands: Sense Publishers.

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Cognitive science research focuses on how the mind works, including topics such as thinking, problem solving, learning and transfer. Much of this research remains unknown in science education circles, yet is relevant for the design of instructional strategies in the sciences. We outline some difficulties in learning science, along with a discussion of some relevant cognitive science research. We then present a cognitive science-based intervention in physics education aimed at promoting conceptual understanding within a problem solving context. In addition, we present assessments of problem solving and conceptual understanding to better examine the differences between knowledge learned from this approach compared to traditional instruction. Finally, we present some pilot data on an initial implementation of the approach.

Nokes, T. J. (2009). Mechanisms of knowledge transfer. Thinking & Reasoning, 15 (1), 1-36.

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A central goal of cognitive science is to develop a general theory of transfer to explain how people use and apply their prior knowledge to solve new problems. Previous work has identified multiple mechanisms of transfer including (but not limited to) analogy, knowledge compilation, and constraint violation. The central hypothesis investigated in the current work is that the particular profile of transfer processes activated for a given situation depends on both (a) the type of knowledge to be transferred and how it is represented, and (b) the processing demands of the transfer task. This hypothesis was investigated in two laboratory training studies. The results from Experiment 1 show that each mechanism predicts specific behavioural patterns of performance across a common set of transfer tasks. The results from Experiment 2 show that people can adaptively shift between transfer mechanisms depending on their prior knowledge and the characteristics of the task environment. A framework for the development of a general theory of transfer based on multiple mechanisms is proposed and implications of the theory are discussed for measuring and understanding knowledge transfer.

2008

Nokes, T. J., & VanLehn, K. (2008). Bridging principles and examples through analogy and explanation. In P. A. Kirschner, F. Prins, V. Jonker, G. Kanselaar, G. (Eds.), Proceedings of the International Conference for the Learning Sciences ICLS 2008, Volume 3 (pp. 100-102). ISLS, The Netherlands.

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Previous research in cognitive science has shown that analogical comparison and selfexplanation are two powerful learning activities that can improve conceptual learning in laboratory settings. The current work examines whether these results generalize to students learning physics in a classroom setting. Students were randomly assigned to one of three worked example learning conditions (reading, self-explanation, or analogical comparison) and then took a test assessing conceptual understanding and problem solving transfer. Students in the selfexplanation and analogy conditions showed improved conceptual understanding compared to students in the more traditional worked example condition.

Ross, B. H., Taylor, E. G., Middleton, E. L., & Nokes, T. J. (2008). Concept and category learning in humans. In H. L. Roediger III (Ed.), Cognitive Psychology of Memory (pp. 535-556). Volume 2 of Learning and memory: A comprehensive reference-Cognitive Psychology (J. Byrne Editor). Oxford, UK: Elsevier.

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Concept and category learning is critical for intelligent thought and action. However, much of the laboratory work has focused only on classification learning, how people learn to assign category membership. This chapter attempts to integrate concept and category learning to the many cases in which it is critical in three ways. First, we examine other ways of learning, in which the categories are used to accomplish a goal. Second, we investigate more complex concepts and the use of prior knowledge. Third, we briefly consider the role of concept and category learning in problem solving and language use.

2007

Nokes, T. J., & Ross, B. H. (2007). Facilitating conceptual learning through analogy and explanation. In L. Hsu, C. Henderson, and L. McCullough (Eds.), Physics Education Research Conference, Vol. 951 (pp. 7-10). Melville, NY: American Institute of Physics Conference Proceedings.

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Research in cognitive science has shown that students typically have a difficult time acquiring deep conceptual understanding in domains like mathematics and physics and often rely on textbook examples to solve new problems. The use of prior examples facilitates learning, but the advantage is often limited to very similar problems. One reason students rely so heavily on using prior examples is that they lack a deep understanding for how the principles are instantiated in the examples. We review and present research aimed at helping students learn the relations between principles and examples through generating explanations and making analogies.

2006-2000

Nokes, T. J. (2005). An investigation into adaptive shifting in knowledge transfer. In B. Bara, L. Barsalou, M. Bucciarelli (Eds.), Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society (pp. 1660-1665). Mahaw, N. J.: Erlbaum.

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Participants were trained on three knowledge types -- exemplars, tactics, and constraints -- and then thought aloud while solving a series of transfer problems. Results show that participants shift between the transfer mechanisms of analogy, knowledge compilation, and constraint violation depending on their prior knowledge and the characteristics of the transfer tasks.

Nokes, T. J., & Ohlsson, S. (2005). Comparing multiple paths to mastery: What is learned? Cognitive Science, 29 (5), 769-796.

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Contemporary theories of learning postulate one or at most a small number of different learning mechanisms. However, people are capable of mastering a given task through qualitatively different learning paths such as learning by instruction and learning by doing.We hypothesize that the knowledge acquired through such alternative paths differs with respect to the level of abstraction and the balance between declarative and procedural knowledge. In a laboratory experiment we investigated what was learned about patterned letter sequences via either direct instruction in the relevant patterns or practice in solving letter-sequence extrapolation problems. Results showed that both types of learning led to mastery of the target task as measured by accuracy performance. However, behavioral differences emerged in how participants applied their knowledge. Participants given instruction showed more variability in the types of strategies they used to articulate their knowledge as well as longer solution times for generating the action implications of that knowledge as compared to the participants given practice. Results are discussed regarding the implications for transfer, generalization, and procedural application. Learning theories that claim generality should be tested against cross-scenario phenomena, not just parametric variations of a single learning scenario.

Nokes, T. J. (2004). Testing three theories of knowledge transfer. In D. Gentner, K. Forbus, and T. Regier (Eds.), Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society (pp. 1029-1034). Mahaw, N.J.: Erlbaum.

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Three theories of knowledge transfer -- analogy, knowledge compilation, and constraint violation -- were tested across three transfer scenarios. Each theory was shown to predict human performance in distinct and identifiable ways on a variety of transfer tasks. Results support the hypothesis that there are multiple mechanisms of transfer and that a general theory of transfer must incorporate each mechanism in principled ways.

Nokes, T. J., & Ohlsson, S. (2003). Declarative transfer from a memory task to a problem solving task. Cognitive Science Quarterly, 3 (3), 259-296.

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Knowledge transfer is the key to understanding the relation between learning and problem solving. In order for declarative knowledge acquired from a learning scenario to transfer to a problem solving situation the knowledge much be both abstract and generative. In a laboratory experiment we examined whether knowledge acquired from a memorization task could facilitate performance on a complex problem solving task. Participants memorized several instances of a pattern and then solved a sequence extrapolation problem that required the detection and generation of novel instances of that same pattern. We observed large transfer effects from memorization to problem solving for extrapolation problems instantiated within the same symbol domain (near transfer) but not across symbol domains (far transfer). This indicated that the knowledge representation acquired from the memorization training was of intermediate abstraction, such that knowledge is abstract in regards to the individual objects in the pattern but domain-specific in regards to the relations between objects. Results are explained via the notion of intermediate abstraction in conjunction with two transfer mechanisms of relational bias and reconstructive recall; the latter is novel.

Ash, I. K., & Nokes, T. J. (2003). Instructional focus does not affect implicit pattern learning. In R. Alterman and D. Krish (Eds.), Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society (pp. 103-108). Mahaw, N.J.: Erlbaum.

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Participants performed a dual-component training procedure that combined a serial reaction time task and an artificial grammar learning task under two instructional conditions. Participants given memory-focused instructions performed at the same level as participants given motor-focused instructions on the serial reaction time test and grammar sorting task. Both groups performed better than a control (no training) group. Results suggest that only a minimal amount of attentional focus on aspects of the stimuli relevant to the pattern is needed to acquire implicit pattern knowledge.

Nokes, T. J., & Ohlsson, S. (2001). How is abstract generative knowledge acquired? A comparison of three learning scenarios. In J. D. Moore and K. Stenning (Eds.), Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society (pp. 710-715). Mahaw, N.J.: Erlbaum.

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Several theories of learning have been proposed to account for the acquisition of abstract, generative knowledge including schema theory, analogical learning and implicit learning. However, past research has not compared these three theories directly. In the present studies we instantiated each theory as a learning scenario (i.e., direct instruction, analogy training and implicit training) and then tested all three training groups on a common problem. Results show that the analogy training groups and one of the direct instruction groups performed significantly better than the other groups on problem solving performance. The findings are interpreted in terms of opportunity to practice generating a response of the relevant type.

Nokes, T. J., & Ohlsson, S. (2000). An inquiry into the function of implicit knowledge and its role in problem solving. In L. R. Gleitman & A. K. Joshi (Eds.), Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society (pp. 829-834). Mahaw, N.J.: Erlbaum.

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Research on implicit learning has shown that the knowledge generated from memorizing patterned symbol sequences can be used to make familiarity judgements of novel sequences with similar structure. However, the degree to which these knowledge representations can be used for subsequent cognitive processing is not known. In this study, participants memorized either patterned number strings (patterned training) or random number strings (random training) and then solved either a number or letter sequence extrapolation problem. Patterned training participants performed significantly better on number problems than on letter problems, thus implying that patterned training influences performance, but only on near transfer problems.

Conference Presentations

2015

Zepeda, C. D., & Nokes-Malach, T. J. (2015, July). Capturing the relations between metacognition, self-explanation, and analogical comparison: An exploration of two methodologies. Poster presented at the Thirty-Seventh Annual Conference of the Cognitive Science Society, Pasadena, CA.

2014

Nokes-Malach, T. J. (2014, October). Knowledge transfer: New approaches to a controversial phenomenon. Beckman Institute for Advanced Science and Technology 25th Anniversary Symposium. University of Illinois at Urbana-Champaign, Urbana, IL.

Chan, J., & Nokes-Malach, T. J. (2014, July). The impact of physical spaces on divergent and convergent problem-solving performance. Poster presented at the Thirty-Sixth Annual Conference of the Cognitive Science Society, Quebec City, Quebec, Canada.

Ferrara, A. M., Zepeda, C., & Nokes-Malach, T. J. (2014, July). Investigating the relationship between mindfulness and learning. Poster presented at the Thirty-Sixth Annual Conference of the Cognitive Science Society, Quebec City, Quebec, Canada.

Wallace, A., Richey, J. E., & Nokes-Malach, T. J. (2014, July). Changing achievement goals and grades. Poster presented at the Thirty-Sixth Annual Conference of the Cognitive Science Society, Quebec City, Quebec, Canada.

Bernacki, M. L., Aleven, V., & Nokes-Malach, T. J. (2014, April). An examination of self-efficacy during a learning episode: Initial levels, changes and associations with learning. Poster presented at the annual meeting of the American Educational Research Association, Philadelphia, PA.

Bernacki, M. L., Nokes-Malach, T. J., Aleven, V., & Glick, J. (2014, April). Intelligent tutoring systems promote achievement in middle school mathematics, especially for students with low interest. Paper presented at the annual meeting of the American Educational Research Association, Philadelphia, PA.

Nokes-Malach, T. J., Mestre, J. P., & G Belenky, D. M. (2013, April). A theoretical framework for transfer as sense-making: Applications and examples. Poster presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

2013

Nokes-Malach, T. J., Mestre, J. P., & Belenky, D. M. (2013, April). A theoretical framework for transfer as sense-making: Applications and examples. Poster presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

Belenky, D. M., & Nokes-Malach, T. J. (2013, April). Task-based vs. course-level achievement goals: An experimental investigation of mastery-approach goals and knowledge transfer. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

Zepeda, C., Richey, J. E., Ronevich, P., Nokes-Malach, T. J. (2013, April). Explicit instruction of metacognition in a middle school science class leads to metacognitive, academic and motivational benefits. Poster presented at the biennial meeting of the Society for Research on Child Development, Seattle, WA.

2012

Zepeda, C., Richey, J. E., Ronevich, P., & Nokes-Malach, T. J. (2012, October). Explicit instruction of metacognition and its benefits to motivation and science learning. Poster to be presented at the 2012 Annual Meeting of the Advancing Hispanics/Chicanos & Native Americans in Science, Seattle, WA.

Belenky, D. M., & Nokes-Malach, T. J. (2012, April). How mastery-approach goal motivations interact with discovery by contrasting cases to facilitate transfer. Paper to be presented at the annual meeting of the American Education Research Association: Vancouver, CA

Bernacki, M. L., Nokes-Malach, T. J., & Aleven, V. (2012, April). Investigating stability and change in task-specific achievement goals and their effects on math learning with intelligent tutors. Paper to be presented to the annual meeting of theAmerican Education Research Association: Vancouver, CA.

Richey, J. E., P Bernacki, M. L., G Belenky, D. M., & Nokes-Malach, T. J. (2012, April) Predicting performance with a task-based behavioral measure of achievement goals. Paper to be presented the annual meeting of the American Education Research Association: Vancouver, CA.

Li, M., Frieze, I., & Nokes-Malach, T. (2012, January). Place matters: The influence of place attachment in motivation for learning. Poster presented to the Thirteenth Annual Meeting of the Society for Personality and Social Psychology. San Diego, CA

2011

Alfieri, L. Nokes, T. J., & Schunn, C. D. (2011, September). Aligning the structural components across learning tasks of case comparisons. Paper presented at the Annual Meeting of the Society for Research on Educational Effectiveness: Washington, DC.

Belenky, D. M., Nokes, T. J., & Bernacki, M. L. (2011, August). Achievement goals over time: How changes in mastery and performance-approach predict deep knowledge. Paper presented at the 14th Biennial Conference European Association for Research on Learning and Instruction Conference. Exeter, UK.

Belenky, D. M., Nokes, T. J., & Bernacki, M. L. (2011, August). Achievement goals and learning in a lecture course: Moving towards mastery goals predicts deeper learning. Poster presented at the Thirty-Third Annual Conference of the Cognitive Science Society, Boston, MA.

Belenky, D. M., Potter, S. J., & Nokes, T. J. (2011, March). The effect of expected test pressure on learning. Poster presented at the Fourth Annual Inter-Science of Learning Center Student and Post-Doc Conference. Washington, DC.

2010

Gadgil, S., & Nokes, T. J. (2010, August). Collaborative facilitation through error-detection: A classroom experiment. Paper presented at the Thirty-Second Annual Conference of the Cognitive Science Society: Portland, OR.

Belenky, D. M., & Nokes, T. J. (2010, August). Optimizing learning environments: An individual differences approach to learning and transfer. Paper presented at the Thirty-Second Annual Conference of the Cognitive Science Society.Portland, OR.

Richey, J. E., Chang, A., Nokes, T. J., & Schunn, C. D. (2010, August). Using Analogical Learning in Science to Improve Conceptual Understanding. Poster presented at the Thirty-Second Annual Conference of the Cognitive Science Society: Portland, OR.

Mestre, J., Docktor, J., Strand, N., Ross, B., Nokes, T., Richey, E. (2010, July). A conceptual analysis approach to physics problem solving. Paper presented to the American Association of Physics Teachers Conference: Portland, OR.

Nokes, T. J., Levine, J. M., Belenky, D. M., Gadgil, S. (2010, July). Investigating the impact of dialectical interaction on engagement, affect, and robust learning. Paper presented at the 2010 International Conference of the Learning Sciences: Chicago, IL.

Nokes, T. J., Mestre, J. P., Ross, B. H., Richey, J. E. (2010, June). Conceptual analysis and student learning in physics. Poster presented at the 2010 Institute for Education Sciences Research Conference: Washington, DC.

Nokes, T. J., & Gadgil, S. (2010, May). Analogical comparison supports collaborative learning in physics. In the symposium on Collaborative Learning and Remembering Part 1. Paper presented at the 22nd Annual Convention for the Association for Psychological Science: Boston, MA.

Nokes, T. J., Mestre, J., Ross, B. H., & Richey, J. E. (2010, May). Conceptual analysis and student learning in physics. In the symposium on Solving Problems in School: Concepts, Procedures and Instruction to Support Learning. Paper presented at the 22nd Annual Conference for the Association for Psychological Science: Boston, MA.

Belenky, D. M., Gadgil, S., Nokes, T. J., & Levine, J. (2010, May). Dialectical interaction, Arousal, and Learning. Paper presented at the Third Annual Inter-Science of Learning Center Student and Post-Doc Conference. Boston, MA.

Gadgil, S., Belenky, D. M., Nokes, T. J., & Levine, J. (2010, May). Poster presented at Third Annual Inter-Science of Learning Center Student and Post-Doc Conference. Boston, MA.

Schunn, C. D., Merlino, J., Cromley, J., Massey, C. Newcombe, N., & Nokes, T. J. (2010, April). Translational science of cognitive science in middle school science curricula. Paper presented at the annual meeting of the American Education Research Association: Denver, CO.

2009

Nokes, T. J., Hausmann, R. G. M., VanLehn, K., & Gershman, S. (2009, November). The design of self-explanation prompts: The fit hypothesis. Talk given at the Science of Learning Centers PI Meeting: Washington, D. C.

Belenky, D. M., & Nokes, T. J. (2009, November). How achievement goals and instructional activities interact to promote or hinder transfer of knowledge. Poster presented at the 50th Annual Meeting of the Psychonomic Society: Boston, MA.

Chang, A., Strohm, E., Nokes, T. J., & Schunn, C. D. (2009, November). Using cognitive science to improve middle school science learning. Poster presented at the 50th Annual Meeting of the Psychonomic Society: Boston, MA.

Nokes, T. J., Ross, B. H. Mestre, J. P., Strohm, E., Brookes, D. T., & Feil, A. (2009, November). Conceptual analysis facilitates learning and transfer in both laboratory and classroom settings. Poster presented at the 50th Annual Meeting of the Psychonomic Society: Boston, MA.

Nokes, T. J. (2009, October). Using cognitive science to improve student learning. Invited speaker at the Brain, Mind, and Learning: Research at the Science of Learning Centers at the 2009 Annual Meeting of the Advancing Hispanics/Chicanos & Native Americans in Science: Dallas, TX.

Nokes, T. J. (2009, October). Robust Learning. Keynote speaker in the Learn-a-Palooza symposium at the 2009 Annual Meeting of the Advancing Hispanics/Chicanos & Native Americans in Science: Dallas, TX

Hausmann, R. G. M., Nokes, T. J., VanLehn, K., & Gershman, S. (2009, July). Revising models or filling gaps? The impact of prompting on self-explanation and robust learning. Paper presented at the 13th Biennial European Association for Research on Learning and Instruction Conference. Amsterdam, Netherlands.

Gadgil, S., & Nokes, T. J. (2009, July). Analogical scaffolding in collaborative learning. Poster presented at the Thirty-First Annual Conference of the Cognitive Science Society. Amsterdam, Netherlands.

Belenky, D. M., & Nokes, T. J. (2009, July). Motivation and transfer: The role of achievement goals in preparation for future learning. Paper presented at the Thirty-First Annual Conference of the Cognitive Science Society. Amsterdam, Netherlands.

Hausmann, R. G. M., Nokes, T. J., VanLehn, K., & Van De Sande, B. (2009, July). The design of self-explanation prompts: The fit hypothesis. Paper presented at the Thirty-First Annual Conference of the Cognitive Science Society. Amsterdam, Netherlands.

Hausmann, R. G. M., Nokes, T. J., VanLehn, K., & Van de Sande, B. (2009, July). Collaborative dialog while studying worked-out examples. Poster presented at the Fourteenth International Conference on Artificial Intelligence in Education. Brighton, England.

Ross, B. H., Mestre, J. P., Nokes, T. J., Brookes, D. T., Feil, A., & Smith A. D. (2009, June). Conceptual analysis and student learning in physics. Poster presented at the 2009 Institute for Education Sciences Research Conference: Washington, DC.

Nokes, T. J. (2009, April). Taking cognitive science to school: Improving cognitive science and student learning. Invited speaker at the Research for Practice Conference. Learning Research and Development Center: Pittsburgh, PA.

Nokes, T. J., Mestre, J. P., Ross, B. H., Feil, A., Brookes, D., & Smith, A. (2009, April). Conceptual analysis promotes learning and transfer in physics. Poster presented at the annual meeting of the American Education Research Association: San Diego, CA.

Nokes, T. J. (2009, March). Taking cognitive science to school: How cognitive science can improve conceptual learning in physics classrooms. Invited speaker at the Presidential Symposium at the Annual Meeting of the Eastern Psychological Association Conference: Pittsburgh, PA.

Gadgil. S., & Nokes, T. J. (2009, February). Analogical scaffolding in collaborative learning. Poster presented at the Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. Seattle, WA.

Hausmann, R. G. M. , & Nokes, T. J. (2009, February). Evidence of transfer in a Physics 1 Course: An educational data-mining project. Poster presented at the Second Annual Inter-Science of Learning Center Student and Post-Doc Conference. Seattle, WA.