Cognitive Science Learning Laboratory
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Abstract: 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.

Abstract: 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.

Abstract: Research in classrooms has shown mixed evidence for benefits of collaborative learning compared with learning individually. Moreover, laboratory research has shown that individuals working in dyads or groups often perform worse than individuals working alone — a robust finding called the collaborative inhibition effect. Despite these findings, we hypothesize that some classroom activities may afford benefits for collaborative learning over individual learning. We created a classroom writing activity that incorporated features such as shared prior knowledge and error-correction processes, which have been hypothesized to eliminate collaborative inhibition and to support constructive collaboration. Students participated in this activity either individually or in dyads. Results showed that the individuals who worked collaboratively performed equally well as those who worked individually and also showed better learning as measured by performance on a future writing assignment.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.