Academic literature on the topic 'Evolutionary learning laboratory'

The integration of information and communication technology in learning through the application of virtual laboratory media can be an alternative solution to overcome the constraints of time, cost, and laboratory safety in biology labs. Virtual laboratories are very suitable to be applied to the mechanism of evolution because they involve the context of space and time, and can provide opportunities for students to learn independently and increase active student involvement in learning. These study aims are to determine the effect of the application of virtual laboratory media on the understanding of student concepts; knowing increasing students' understanding of the concept of evolutionary mechanisms, and knowing students' responses after learning with virtual laboratories.This type of research is a pre-experimental design (nondesign) with one group pretest-posttest design. The research subjects were 37 students. The media used as many as 8 web-based virtual laboratory programs (online) that can be freely accessed. Data collection techniques include tests (pretest and posttest) and questionnaires. The pretest and posttest values were analyzed by the T-test while the questionnaire data were analyzed descriptively. The application of web-based virtual laboratory media on the evolution mechanism material influences the understanding of student concepts (t arithmetic> t table then H0 is rejected and H1 is accepted) The application of these media increases students' understanding of the concept of evolutionary mechanisms. The mean value increased from pretest 23.65 to 56.08 at the posttest with an increase of 32.43. Student responses to the application of media in learning showed 91.89% of students felt helped in understanding the process of evolution while 100% of students felt helped in understanding the role of evolution in causing evolution.

In this design case, we describe our work to develop a gameful learning design for use in an introductory, under-graduate biology laboratory course for science majors. Our design team included three university-based mathematics and science educators and a biologist responsible for the management of curriculum and instruction in the course under study. The gameful learning design was employed during the four weeks of plant evolutionary life history in-struction. Key challenges to the design and implementation of gameful learning included the adaptation of instruction from teacher-centered to student-centered and establishing novel learning conditions in the eight laboratory sections so as to determine the value of two different elements of game design, repeat-testing and leaderboard with badges.

Evolutionary theory is the foundation of the biological sciences, yet conveying it to General Biology students often presents a challenge, especially at larger institutions where student numbers in foundation courses can exceed several hundred per lecture section. We present a pedagogically sound exercise that utilizes a series of simple and inexpensive simulations to convey the concept of evolution through mutation and natural selection. Questions after each simulation expand student comprehension; a class discussion encourages advanced thinking on mutation and speciation. A final paper requires students to synthesize their learning by summarizing selected papers on these topics. A grading rubric for the papers is included.

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behaviour, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyse an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behaviour, we require statistical methods that do not depend upon tight experimental control. Therefore, we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest that most participants employ a hierarchical strategy that uses both average observed pay-offs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.

Undergraduates gain a three-dimensional view of the structures of the sheep brain, first by following a standard dissection procedure and then by constructing a model using PlayDoh™. Both methods demonstrate evolutionary trends in brain development. Students reported that the lab was helpful for learning about neuroanatomy, and they recommended its use in future semesters.

Various shortcomings have been revealed in many development efforts using conventional supply-driven and/or top-down approaches with linear vision in developing countries, including labour saving initiatives for the disadvantaged and marginalised groups. Various failures, unintended consequences and even counterproductive outcomes have been evident. Solutions and interventions tend to ignore local contexts, affordability, participation and needs of targeted groups. The inability of traditional approaches to deal with complexities and uncertainties of socio-cultural contexts, interwoven with relationships of both environmental and human factors across regions have highlighted a high need for developing and embracing more holistic and participatory approaches and structured frameworks to address complex problems. In response to gender-biased labour hardship of women smallholders in the developing world, this study employed the systems-based Evolutionary Learning Laboratory (ELLab) approach, aiming at formulating the most economically, environmentally, culturally and socially appropriate systemic solutions to labour constraints. The latter is a prominent issue pre-determined by a funding body, for women small-scale farmers in rural areas of Haiphong, Vietnam. The first five steps of the ELLab were implemented with active participation of representatives of the target group and relevant stakeholders in the planning phase. This started from identifying issues, building local capacity, engagement and empowerment of the participants throughout problem structuring and decision making processes via a participative, interactive and co-learning environment towards developing a systemic management plan to address the real needs of the women farmers. In-depth analyses through a baseline survey and a number of interactive workshops helped to understand and frame the context through developing a big picture (systems model) of the current situation. The model depicts a complex life situation and interconnectedness of various factors influencing the quality of life of the women farmers. Increasing income turned out to be the most urgent need, followed by the needs for reducing work pressure and improving health. Labour hardship was found just part of many interrelated issues. The decision making process with the aid of systems and relevant management tools enabled the participants to define systemic interventions and develop an overall systemic management plan to address their real needs. The identified solutions support one another to address the labour hardship of the women and improve the quality of their lives as a whole. This study has clearly proven the value and validity of the systems-based participative ELLab as an effective and powerful problem-structuring and solving framework to deal with complex problem across contexts and regions. It embraces bottom-up and participatory approaches in practice, builds capacity of local people and changes the mindsets of stakeholders involved from traditional linear and silo thinking to a more holistic and interconnected way of thinking that leads to appropriate actions and mutual collaboration. The study has addressed drawbacks of other approaches and provided substantial theoretical and practical contributions to various disciplines. These include community development, operational research, gender studies, agricultural systems research and development, participatory action research, project stakeholder and knowledge management, and organisational learning. It has also laid a strong foundation for future research in the mentioned fields.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, Business School, 2016.

Evolution of Learning and Memory Mechanisms is an exploration of laboratory and field research on the many ways that evolution has influenced learning and memory processes, such as associative learning, social learning, and spatial, working, and episodic memory systems. This volume features research by both outstanding early-career scientists as well as familiar luminaries in the field. Learning and memory in a broad range of animals are explored, including numerous species of invertebrates (insects, worms, sea hares), as well as fish, amphibians, birds, rodents, bears, and human and nonhuman primates. Contributors discuss how the behavioral, cognitive, and neural mechanisms underlying learning and memory have been influenced by evolutionary pressures. They also draw connections between learning and memory and the specific selective factors that shaped their evolution. Evolution of Learning and Memory Mechanisms should be a valuable resource for those working in the areas of experimental and comparative psychology, comparative cognition, brain–behavior evolution, and animal behavior.

If groups are strange to each other and therefore fearful or hostile, why not bring them together so they can get to know each other and become friendly? This plausible approach is more complicated than it looks at first glance. Under what conditions will intergroup contact be helpful? Can it sometimes be harmful? A variety of field and laboratory experiments support the hypothesis that intergroup competition tends to strengthen social relations within each group and to disrupt relations between the groups. If the experiments are arranged in a way that deliberately fosters competition between the groups, these effects are heightened. But even in the absence of such direct instruction or arrangement, potent factors favor interpersonal attraction or mutual attachment within a group: frequency of social interaction, proximity to each other, familiarity, and similarity of attitudes and values. Almost any sort of interaction within a group tends to promote in-group favoritism. Actually, it seems rather difficult to avoid this effect even if one tries to do so. Humans are highly susceptible to invidious in-group/out-group distinctions. Extensive experimental work strongly confirms the rich variety of observations from fieldwork in many cultures over extended times and in a variety of societies. This does indeed seem to be a profound and pervasive human characteristic—one of great practical significance throughout history. We will return to this theme and examples throughout the book. Findings of this sort have led some psychologists to formulate a principle of social identity, which emphasizes the powerful effects of social categorization in its own right. Such categorization seems to highlight an important aspect of the individual self-concept (and self-esteem) based on group membership. Such membership has, from the evolutionary and historical record, been an important feature in human survival over the millennia. In contemporary people—at least, in those who participate in psychological experiments—the cognitive delineation into an in-group and out-group, even without invidious attributions, tends to set in motion a process by which there is an accentuation of similarities within groups and differences between groups. It seems very convenient, easy, and somehow natural for people to deal with these via simple schemas or stereotypes.

Abstract This chapter focuses on parrots, looking at three groups that exemplify convergent evolution—and parallel history—with humans: the cockatoos, the grey parrots, and the macaws. Like humans, they represent the extreme edge of what it means to be a parrot, the case where all the evolutionary forces combined to push a species or group of species towards an extreme version of its type. What is remarkable is that these two extreme groups of animals, the humans and the urparrots, have, in their extremity, become so remarkably similar. Rather than each being pushed to opposite ends of what it means to be an animal, they have ended up mirror images of each other, with parrots a whispered answer to the question: what if a different group of animals were to evolve like the humans? The chapter then considers the similarities that humans share with parrots, including longevity and monogamy. Where the parrots truly become our mirror image, however, is in their brains and their language. They share with us learning, fretfulness, personality, and artistry. Laboratory research has confirmed the expert lockpicking ability of these birds as well.

Abstract Nondestructive Evaluation (NDE) techniques such as acoustic emission, digital image correlation, infrared thermography, ultrasonic testing among others, have been used to monitor evolutionary failure processes, related to Structural Health Monitoring (SHM). Although such NDE sensing assists in understanding evolving material and structural states, machine learning methods capable of using real-time NDE data enable additional insights related to diagnostics and prognostics. In this context, this investigation presents a novel approach to enable the real time use of NDE data for damage detection. To achieve this goal, an Internet of Things (IoT) framework developed by the authors is used in conjunction with NDE datasets for near real-time diagnostics of crack initiation at the laboratory scale. Compact-tension specimens of an aerospace-grade aluminum alloy were used in accordance with ASTM standards. Acoustic emission NDE datasets were acquired and were subsequently used in an in-house built, scalable IoT system capable of edge and cloud computing with the purpose to reliably identify crack initiation. Once the signals are transmitted from the edge to the fog node, the trained information-entropy based model determines if the raw data is indicative of crack. The model used during live testing is trained offline on the Cloud. The model characterizes the disorder in signals using Shannon’s Entropy to ultimately determine the amount of information per signal. Then a statistical model is used to characterize such information. During the testing process, the signals are segmented based on file chunks to allow for real-time transmission. The main innovation of this approach is the fact that a combination of hardware, computing and machine learning analysis proves to be advantageous in implementing a data structure that can be used at the edge and which can successfully flag the incubation and subsequent initiation of fracture. The IoT system described can be applied to a variety of test setups and at various length scales. Extensions of this work to include forecasting are also discussed.