Simulation models in farming systems research : potential and challenges

Integrated simulation models can be useful tools in farming system research. This chapter reviews three commonly used approaches, i.e. linear programming, system dynamics and agent-based models. Applications of each approach are presented and strengths and drawbacks discussed. We argue that, despite some challenges, mainly related to the integration of different approaches, model validation and the representation of human agents, integrated simulation models contribute important insights to the analysis of farming systems. They help unravelling the complex and dynamic interactions and feedbacks among bio-physical, socio-economic, and institutional components across scales and levels in farming systems. In addition, they can provide a platform for integrative research, and can support transdisciplinary research by functioning as learning platforms in participatory processes.

Effectiveness of mobile apps in teaching field-based identification skills

It has been suggested that few students graduate with the skills required for many ecological careers, as field-based learning is said to be in decline in academic institutions. Here, we asked if mobile technology could improve field-based learning, using ability to identify birds as the study metric. We divided a class of ninety-one undergraduate students into two groups for field-based sessions where they were taught bird identification skills. The first group has access to a traditional identification book and the second group were provided with an identification app. We found no difference between the groups in the ability of students to identify birds after three field sessions. Furthermore, we found that students using the traditional book were significantly more likely to identify novel species. Therefore, we find no evidence that mobile technology improved students’ ability to retain what they experienced in the field; indeed, there is evidence that traditional field guides were more useful to students as they attempted to identify new species. Nevertheless, students felt positively about using their own smartphone devices for learning, highlighting that while apps did not lead to an improvement in bird identification ability, they gave greater accessibility to relevant information outside allocated teaching times.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Science is Primary - Children Thinking and Learning in theChemistry Laboratory

The goal of primary science education is to foster children’s interest, develop positive science attitudes and promote science process skills development. Learning by playing and discovering provides several opportunities for children to inquiry and understand science based on the first–hand experience. The current research was conducted in the children’s laboratory in Heureka, the Finnish science centre. Young children (aged 7 years) which came from 4 international schools did a set of chemistry experiments in the laboratory. From the results of the cognitive test, the pre-test, the post-test, supported by observation and interview, we could make the conclusion that children enjoyed studying in the laboratory. Chemistry science was interesting and fascinating for young children; no major gender differences were found between boys and girls learning in the science laboratory. Lab work not only encouraged children to explore and investigate science, but also stimulated children’s cognitive development.

Discourse strategies and power roles in student-led distance learning

The assertion of identity and power via computer-mediated communication in the context of distance or web-based learning presents challenges to both teachers and students. When regular, face-to-face classroom interaction is replaced by online chat or group discussion forums, participants must avail themselves of new techniques and tactics for contributing to and furthering interaction, discussion, and learning. During student-only chat sessions, the absence of teacher-led, face-to-face classroom activities requires the students to assume leadership roles and responsibilities normally associated with the teacher. This situation raises the questions of who teaches and who learns; how students discursively negotiate power roles; and whether power emerges as a function of displayed expertise and knowledge or rather the use of authoritative language. This descriptive study represents an examination of a corpus of task-based discussion logs among Vietnamese students of distance learning courses in English linguistics. The data reveal recurring discourse strategies for 1) negotiating the progression of the discussion sessions, 2) asserting and questioning knowledge, and 3) assuming or delegating responsibility. Power is defined ad hoc as the ability to successfully perform these strategies. The data analysis contributes to a better understanding of how working methods and materials can be tailored to students in distance learning courses, and how such students can be empowered by being afforded opportunities and effectively encouraged to assert their knowledge and authority.

Aprendizado por Reforço com Valores deInfluência em Sistemas Multi-Agente

We propose a new paradigm for collective learning in multi-agent systems (MAS) as a solution to the problem in which several agents acting over the same environment must learn how to perform tasks, simultaneously, based on feedbacks given by each one of the other agents. We introduce the proposed paradigm in the form of a reinforcement learning algorithm, nominating it as reinforcement learning with influence values. While learning by rewards, each agent evaluates the relation between the current state and/or action executed at this state (actual believe) together with the reward obtained after all agents that are interacting perform their actions. The reward is a result of the interference of others. The agent considers the opinions of all its colleagues in order to attempt to change the values of its states and/or actions. The idea is that the system, as a whole, must reach an equilibrium, where all agents get satisfied with the obtained results. This means that the values of the state/actions pairs match the reward obtained by each agent. This dynamical way of setting the values for states and/or actions makes this new reinforcement learning paradigm the first to include, naturally, the fact that the presence of other agents in the environment turns it a dynamical model. As a direct result, we implicitly include the internal state, the actions and the rewards obtained by all the other agents in the internal state of each agent. This makes our proposal the first complete solution to the conceptual problem that rises when applying reinforcement learning in multi-agent systems, which is caused by the difference existent between the environment and agent models. With basis on the proposed model, we create the IVQ-learning algorithm that is exhaustive tested in repetitive games with two, three and four agents and in stochastic games that need cooperation and in games that need collaboration. This algorithm shows to be a good option for obtaining solutions that guarantee convergence to the Nash optimum equilibrium in cooperative problems. Experiments performed clear shows that the proposed paradigm is theoretical and experimentally superior to the traditional approaches. Yet, with the creation of this new paradigm the set of reinforcement learning applications in MAS grows up. That is, besides the possibility of applying the algorithm in traditional learning problems in MAS, as for example coordination of tasks in multi-robot systems, it is possible to apply reinforcement learning in problems that are essentially collaborative

Effect of silica coating combined to a MDP-based primer on the resin bond to Y-TZP ceramic

The aim of this study was to evaluate the influence of silica coating and 10-methacryloyloxydecyl dihydrogen phosphate (MDP)-based primer applications upon the bonding durability of a MDP-based resin cement to a yttrium stabilized tetragonal zirconia (Y-TZP) ceramic. Ninety-six Y-TZP tabs were embedded in an acrylic resin (free surface for adhesion: 5 x 5 mm(2)), ground finished and randomly divided into four groups (N = 24) according to the ceramic surface conditioning: (1) cleaning with isopropanol (ALC); (2) ALC + phosphoric acid etching + MDP-based primer application (MDP-primer); (3) silica coating + 3-methacryloyloxypropyl trimethoxysilane (MPS)-based coupling agent application (SiO(2) + MPS-Sil); and (4) SiO(2) + MDP-primer. The MDP-based resin cement was applied on the treated surface using a cylindrical mold (diameter=3 mm). Half of the specimens from each surface conditioning were stored in distilled water (37 C, 24 h) before testing. Another half of the specimens were stored (90 days) and thermo-cycled (12,000x) during this period (90d/TC) before testing. A shear bond strength (SBS) test was performed at a crosshead speed of 0.5 mm/min. Two factors composed the experimental design: ceramic conditioning strategy (in four levels) and storage condition (in two levels), totaling eight groups. After 90d/TC (Tukey; p < 0.05), SiO(2) + MDP-primer (24.40 MPa) promoted the highest SBS. The ALC and MDP-primer groups debonded spontaneously during 90d/TC. Bonding values were higher and more stable in the SiO2 groups. The use of MDP-primer after silica coating increased the bond strength. (C) 2010 Wiley Periodicals, Inc. J Biomed Mater Res Part 8: Appl Biomater 95B: 69-74, 2010.

Abordagem para o desenvolvimento de um etiquetador de alta acurácia para o Português do Brasil

A etiquetagem morfossintática é uma tarefa básica requerida por muitas aplicações de processamento de linguagem natural, tais como análise gramatical e tradução automática, e por aplicações de processamento de fala, por exemplo, síntese de fala. Essa tarefa consiste em etiquetar palavras em uma sentença com as suas categorias gramaticais. Apesar dessas aplicações requererem etiquetadores que demandem maior precisão, os etiquetadores do estado da arte ainda alcançam acurácia de 96 a 97%. Nesta tese, são investigados recursos de corpus e de software para o desenvolvimento de um etiquetador com acurácia superior à do estado da arte para o português brasileiro. Centrada em uma solução híbrida que combina etiquetagem probabilística com etiquetagem baseada em regras, a proposta de tese se concentra em um estudo exploratório sobre o método de etiquetagem, o tamanho, a qualidade, o conjunto de etiquetas e o gênero dos corpora de treinamento e teste, além de avaliar a desambiguização de palavras novas ou desconhecidas presentes nos textos a serem etiquetados. Quatro corpora foram usados nos experimentos: CETENFolha, Bosque CF 7.4, Mac-Morpho e Selva Científica. O modelo de etiquetagem proposto partiu do uso do método de aprendizado baseado em transformação(TBL) ao qual foram adicionadas três estratégias, combinadas em uma arquitetura que integra as saídas (textos etiquetados) de duas ferramentas de uso livre, o TreeTagger e o -TBL, com os módulos adicionados ao modelo. No modelo de etiquetador treinado com o corpus Mac-Morpho, de gênero jornalístico, foram obtidas taxas de acurácia de 98,05% na etiquetagem de textos do Mac-Morpho e 98,27% em textos do Bosque CF 7.4, ambos de gênero jornalístico. Avaliou-se também o desempenho do modelo de etiquetador híbrido proposto na etiquetagem de textos do corpus Selva Científica, de gênero científico. Foram identificadas necessidades de ajustes no etiquetador e nos corpora e, como resultado, foram alcançadas taxas de acurácia de 98,07% no Selva Científica, 98,06% no conjunto de teste do Mac-Morpho e 98,30% em textos do Bosque CF 7.4. Esses resultados são significativos, pois as taxas de acurácia alcançadas são superiores às do estado da arte, validando o modelo proposto em busca de um etiquetador morfossintático mais confiável.

Blended Online POPBL: uma Abordagem Blended Learning para uma Aprendizagem Baseada em Problemas e Organizada em Projetos

Pós-graduação em Educação - FCT

PBL and B-Learning for Civil Engineering Students in a Transportation Course

The results of a pedagogical strategy implemented at the University of Sao Paulo at Sao Carlos are presented and discussed. The initiative was conducted in a transportation course offered to Civil Engineering students. The approach is a combination of problem-based learning and project-based learning (PBL) and blended-learning (B-learning). Starting in 2006, a different problem was introduced every year. From 2009 on, however, the problem-based learning concept was expanded to project-based learning. The performance of the students was analyzed using the following elements: (1) grades in course activities; (2) answers from a questionnaire designed for course evaluation; and (3) cognitive maps made to assess the effects of PBL through the comparison of the responses provided by the students involved and those not involved in the experiment. The results showed positive aspects of the method, such as a strong involvement of several students with the subject. A gradual increase in the average scores obtained by the students in the project activities (from 6.77 in 2006 to 8.24 in 2009) was concomitant with a better evaluation of these activities and of the course as a whole (90 and 97% of options "Good" or "Very good" in 2009, respectively). A growing interest in the field of transportation engineering as an alternative for further studies was also noticed. DOI: 10.1061/(ASCE)EI.1943-5541.0000115. (C) 2012 American Society of Civil Engineers.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-Temporal Credit Assignment in Neuronal Population Learning

n learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.

How Porous are the Walls that Separate Us?: Transformative Service-Learning, Women’s Incarceration, and the Unsettled Self

In this article, we refine a politics of thinking from the margins by exploring a pedagogical model that advances transformative notions of service learning as social justice teaching. Drawing on a recent course we taught involving both incarcerated women and traditional college students, we contend that when communication among differentiated and stratified parties occurs, one possible result is not just a view of the other but also a transformation of the self and other. More specifically, we suggest that an engaged feminist praxis of teaching incarcerated women together with college students helps illuminate the porous nature of fixed markers that purport to reveal our identities (e.g., race and gender), to emplace our bodies (e.g., within institutions, prison gates, and walls), and to specify our locations (e.g., cultural, geographic, socialeconomic). One crucial theoretical insight our work makes clear is that the model of social justice teaching to which we aspired necessitates re-conceptualizing ourselves as students and professors whose subjectivities are necessarily relational and emergent.

The Repairing of Misconceptions through the Use of Inquiry-Based Activities

Misconceptions exist in all fields of learning and develop through a person’s preconception of how the world works. Students with misconceptions in chemical engineering are not capable of correctly transferring knowledge to a new situation and will likely arrive at an incorrect solution. The purpose of this thesis was to repair misconceptions in thermodynamics by using inquiry-based activities. Inquiry-based learning is a method of teaching that involves hands-on learning and self-discovery. Previous work has shown inquiry-based methods result in better conceptual understanding by students relative to traditional lectures. The thermodynamics activities were designed to guide students towards the correct conceptual understanding through observing a preconception fail to hold up through an experiment or simulation. The developed activities focus on the following topics in thermodynamics: “internal energy versus enthalpy”, “equilibrium versus steady state”, and “entropy”. For each topic, two activities were designed to clarify the concept and assure it was properly grasped. Each activity was coupled with an instructions packet containing experimental procedure as well as pre- and post-analysis questions, which were used to analyze the effect of the activities on the students’ responses. Concept inventories were used to monitor students’ conceptual understanding at the beginning and end of the semester. The results did not show a statistically significant increase in the overall concept inventory scores for students who performed the activities compared to traditional learning. There was a statistically significant increase in concept area scores for “internal energy versus enthalpy” and “equilibrium versus steady state”. Although there was not a significant increase in concept inventory scores for “entropy”, written analyses showed most students’ misconceptions were repaired. Students transferred knowledge effectively and retained most of the information in the concept areas of “internal energy versus enthalpy” and “equilibrium versus steady state”.

The Impact of Gender and Pedagogy on the Development of Self-Regulated Learning Strategies in Undergraduate Engineering Classrooms

The purpose of this paper is to examine ways in which pedagogy and gender of instructor impact the development of self-regulated learning strategies as assessed by the Motivated Strategies for Learning Questionnaire (MSLQ) in male and female undergraduate engineering students. Pedagogy was operationalized as two general formats: lecture plus active learning techniques or problem-base/project-based learning. One hundred seventy-six students from four universities participated in the study. Within-group analyses found significant differences with regard to pedagogy, instructors’ gender, and student gender on the learning strategies and motivation subscales as operationalized by the MSLQ. Male and females students reported significant post-test differences with regard to the gender of instructor and the style of pedagogy. The results of this study showed a pattern where more positive responses for students of both genders were found with the same-gendered instructor. The results also suggested that male students responded more positively to project and problem-based courses with changes evidenced in motivation strategies and resource management. Female students showed decreases in resource management in these two types of courses. Further, female students reported increases in the lecture with active learning courses.