155,702 research outputs found
Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment
This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM
Rich environments for active learning: a definition
Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action
Rich environments for active learning in action: Problemâbased learning
Rich Environments for Active Learning (REALs) are comprehensive instructional systems that are consistent with constructivist theories. They promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higherâorder thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learningâtoâlearn within authentic contexts using realistic tasks and performances. ProblemâBased Learning (PBL) is an instructional methodology that can be used to create REALs. PBL's studentâcentred approach engages students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, PBL and REALs are a response to teacherâcentred educational practices that promote the development of inert knowledge, such as conventional teacherâtoâstudent knowledge dissemination activities. In this article, we compare existing assumptions underlying teacherâdirected educational practice with new assumptions that promote problem solving and higherâlevel thinking by putting students at the centre of learning activities. We also examine the theoretical foundation that supports these new assumptions and the need for REALs. Finally, we describe each REAL characteristic and provide supporting examples of REALs in action using PB
Adaptive Guidance: Enhancing Self-Regulation, Knowledge, and Performance in Technology-Based Training
Considerable research has examined the effects of giving trainees control over their learning (Steinberg, 1977, 1989; Williams, 1993). The most consistent finding of this research has been that trainees do not make good instructional use of the control they are given. Yet, todayâs technologically based training systems often provide individuals with significant control over their learning (Brown, 2001). This creates a dilemma that must be addressed if technology is going to be used to create more effective training systems. The current study extended past research that has examined the effects of providing trainees with some form of advisement or guidance in addition to learner control and examined the impact of an instructional strategy, adaptive guidance, on learning and performance in a complex training environment. Overall, it was found that adaptive guidance had a substantial effect on the nature of traineesâ study and practice, self-regulation, knowledge acquired, and performance
Variability of worked examples and transfer of geometrical problem-solving skills : a cognitive-load approach
Four computer-based training strategies for geometrical problem solving in the domain of computer numerically controlled machinery programming were studied with regard to their effects on training performance, transfer performance, and cognitive load. A low- and a high-variability conventional condition, in which conventional practice problems had to be solved (followed by worked examples), were compared with a low- and a high-variability worked condition, in which worked examples had to be studied. Results showed that students who studied worked examples gained most from high-variability examples, invested less time and mental effort in practice, and attained better and less effort-demanding transfer performance than students who first attempted to solve conventional problems and then studied work examples
Know-how, intellectualism, and memory systems
ABSTRACTA longstanding tradition in philosophy distinguishes between knowthatand know-how. This traditional âanti-intellectualistâ view is soentrenched in folk psychology that it is often invoked in supportof an allegedly equivalent distinction between explicit and implicitmemory, derived from the so-called âstandard model of memory.âIn the last two decades, the received philosophical view has beenchallenged by an âintellectualistâ view of know-how. Surprisingly, defenders of the anti-intellectualist view have turned to the cognitivescience of memory, and to the standard model in particular, todefend their view. Here, I argue that this strategy is a mistake. As it turns out, upon closer scrutiny, the evidence from cognitivepsychology and neuroscience of memory does not support theanti-intellectualist approach, mainly because the standard modelof memory is likely wrong. However, this need not be interpretedas good news for the intellectualist, for it is not clear that theempirical evidence necessarily supp..
Applying science of learning in education: Infusing psychological science into the curriculum
The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the âscientific study of how people learnâ (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the âscientific study of how to help people learnâ (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the âscientific study of how to determine what people knowâ (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings
Metacognition and Reflection by Interdisciplinary Experts: Insights from Cognitive Science and Philosophy
Interdisciplinary understanding requires integration of insights from
different perspectives, yet it appears questionable whether disciplinary experts
are well prepared for this. Indeed, psychological and cognitive scientific studies
suggest that expertise can be disadvantageous because experts are often more biased
than non-experts, for example, or fixed on certain approaches, and less flexible in
novel situations or situations outside their domain of expertise. An explanation is
that expertsâ conscious and unconscious cognition and behavior depend upon their
learning and acquisition of a set of mental representations or knowledge structures.
Compared to beginners in a field, experts have assembled a much larger set of
representations that are also more complex, facilitating fast and adequate perception
in responding to relevant situations. This article argues how metacognition should be
employed in order to mitigate such disadvantages of expertise: By metacognitively
monitoring and regulating their own cognitive processes and representations,
experts can prepare themselves for interdisciplinary understanding. Interdisciplinary
collaboration is further facilitated by team metacognition about the team, tasks,
process, goals, and representations developed in the team. Drawing attention to
the need for metacognition, the article explains how philosophical reflection on the
assumptions involved in different disciplinary perspectives must also be considered
in a process complementary to metacognition and not completely overlapping with
it. (Disciplinary assumptions are here understood as determining and constraining
how the complex mental representations of experts are chunked and structured.) The
article concludes with a brief reflection on how the process of Reflective Equilibrium
should be added to the processes of metacognition and philosophical reflection in
order for experts involved in interdisciplinary collaboration to reach a justifiable
and coherent form of interdisciplinary integration. An Appendix of âPrompts or
Questions for Metacognitionâ that can elicit metacognitive knowledge, monitoring,
or regulation in individuals or teams is included at the end of the article
Assessing schematic knowledge of introductory probability theory
[Abstract]: The ability to identify schematic knowledge is an important goal for both assessment
and instruction. In the current paper, schematic knowledge of statistical probability theory is
explored from the declarative-procedural framework using multiple methods of assessment.
A sample of 90 undergraduate introductory statistics students was required to classify 10
pairs of probability problems as similar or different; to identify whether 15 problems
contained sufficient, irrelevant, or missing information (text-edit); and to solve 10 additional
problems. The complexity of the schema on which the problems were based was also
manipulated. Detailed analyses compared text-editing and solution accuracy as a function of
text-editing category and schema complexity. Results showed that text-editing tends to be
easier than solution and differentially sensitive to schema complexity. While text-editing and
classification were correlated with solution, only text-editing problems with missing
information uniquely predicted success. In light of previous research these results suggest
that text-editing is suitable for supplementing the assessment of schematic knowledge in
development
On perceptual expertise
Expertise is a cognitive achievement that clearly involves experience and learning, and often requires explicit, time-consuming training specific to the relevant domain. It is also intuitive that this kind of achievement is, in a rich sense, genuinely perceptual. Many expertsâbe they radiologists, bird watchers, or fingerprint examinersâare better perceivers in the domain(s) of their expertise. The goal of this paper is to motivate three related claims, by substantial appeal to recent empirical research on perceptual expertise: Perceptual expertise is genuinely perceptual and genuinely cognitive, and this phenomenon reveals how we can become epistemically better perceivers. These claims are defended against sceptical opponents that deny significant top-down or cognitive effects on perception, and opponents who maintain that any such effects on perception are epistemically pernicious
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