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.

Fuzzy community structure detection by particle competition and cooperation

Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

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

Pós-graduação em Educação - FCT

Algoritmos de aprendizado semi-supervisionado baseados em grafos aplicados na bioinformática

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Graph Model for DynamicWaveband Switching in WDM Mesh Networks

We investigate the problem of waveband switching (WBS) in a wavelength-division multiplexing (WDM) mesh network with dynamic traffic requests. To solve the WBS problem in a homogeneous dynamic WBS network, where every node is a multi-granular optical cross-connect (MG-OXC), we construct an auxiliary graph. Based on the auxiliary graph, we develop two heuristic on-line WBS algorithms with different grouping policies, namely the wavelength-first WBS algorithm based on the auxiliary graph (WFAUG) and the waveband-first WBS algorithm based on the auxiliary graph (BFAUG). Our results show that the WFAUG algorithm outperforms the BFAUG algorithm.

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.