An innovative approach to impacting student academic achievement and attitudes: Pilot study of the HEADS UP Virtual Molecular Biology Lab

Objective. To evaluate the HEADS UP Virtual Molecular Biology Lab, a computer-based simulated laboratory designed to teach advanced high school biology students how to create a mouse model. ^ Design. A randomized clinical control design of forty-four students from two science magnet high schools in Mercedes, Texas was utilized to assess knowledge and skills of molecular laboratory procedures, attitudes towards science and computers as a learning tool, and usability of the program. ^ Measurements. Data was collected using five paper-and-pencil formatted questionnaires and an internal "lab notebook." ^ Results. The Virtual Lab was found to significantly increase student knowledge over time (p<0.005) and with each use (p<0.001) as well as positively increase attitudes towards computers (p<0.001) and skills (p<0.005). No significant differences were seen in science attitude scores.^ Conclusion. These results provide evidence that the HEADS UP Virtual Molecular Biology Lab is a potentially effective educational tool for high school molecular biology education.^

Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V-structures in the predictor sub-graph, we are also able to prove that this family of polynomials does in- deed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure and we compare these bounds to the ones obtained using Vapnik-Chervonenkis dimension.

Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V-structures in the predictor sub-graph, we are also able to prove that this family of polynomials does in- deed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure and we compare these bounds to the ones obtained using Vapnik-Chervonenkis dimension.

PV power forecast using a nonparametric PV model

Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that daily production is predicted with an absolute cvMBE lower than 1.3%.

Especificaciones eMathTeacher, creación del Modelo Granular Lingüístico de la evaluación del aprendizaje humano, y diseño de sistemas que implementan estos conceptos

El concepto de algoritmo es básico en informática, por lo que es crucial que los alumnos profundicen en él desde el inicio de su formación. Por tanto, contar con una herramienta que guíe a los estudiantes en su aprendizaje puede suponer una gran ayuda en su formación. La mayoría de los autores coinciden en que, para determinar la eficacia de una herramienta de visualización de algoritmos, es esencial cómo se utiliza. Así, los estudiantes que participan activamente en la visualización superan claramente a los que la contemplan de forma pasiva. Por ello, pensamos que uno de los mejores ejercicios para un alumno consiste en simular la ejecución del algoritmo que desea aprender mediante el uso de una herramienta de visualización, i. e. consiste en realizar una simulación visual de dicho algoritmo. La primera parte de esta tesis presenta los resultados de una profunda investigación sobre las características que debe reunir una herramienta de ayuda al aprendizaje de algoritmos y conceptos matemáticos para optimizar su efectividad: el conjunto de especificaciones eMathTeacher, además de un entorno de aprendizaje que integra herramientas que las cumplen: GRAPHs. Hemos estudiado cuáles son las cualidades esenciales para potenciar la eficacia de un sistema e-learning de este tipo. Esto nos ha llevado a la definición del concepto eMathTeacher, que se ha materializado en el conjunto de especificaciones eMathTeacher. Una herramienta e-learning cumple las especificaciones eMathTeacher si actúa como un profesor virtual de matemáticas, i. e. si es una herramienta de autoevaluación que ayuda a los alumnos a aprender de forma activa y autónoma conceptos o algoritmos matemáticos, corrigiendo sus errores y proporcionando pistas para encontrar la respuesta correcta, pero sin dársela explícitamente. En estas herramientas, la simulación del algoritmo no continúa hasta que el usuario introduce la respuesta correcta. Para poder reunir en un único entorno una colección de herramientas que cumplan las especificaciones eMathTeacher hemos creado GRAPHs, un entorno ampliable, basado en simulación visual, diseñado para el aprendizaje activo e independiente de los algoritmos de grafos y creado para que en él se integren simuladores de diferentes algoritmos. Además de las opciones de creación y edición del grafo y la visualización de los cambios producidos en él durante la simulación, el entorno incluye corrección paso a paso, animación del pseudocódigo del algoritmo, preguntas emergentes, manejo de las estructuras de datos del algoritmo y creación de un log de interacción en XML. Otro problema que nos planteamos en este trabajo, por su importancia en el proceso de aprendizaje, es el de la evaluación formativa. El uso de ciertos entornos e-learning genera gran cantidad de datos que deben ser interpretados para llegar a una evaluación que no se limite a un recuento de errores. Esto incluye el establecimiento de relaciones entre los datos disponibles y la generación de descripciones lingüísticas que informen al alumno sobre la evolución de su aprendizaje. Hasta ahora sólo un experto humano era capaz de hacer este tipo de evaluación. Nuestro objetivo ha sido crear un modelo computacional que simule el razonamiento del profesor y genere un informe sobre la evolución del aprendizaje que especifique el nivel de logro de cada uno de los objetivos definidos por el profesor. Como resultado del trabajo realizado, la segunda parte de esta tesis presenta el modelo granular lingüístico de la evaluación del aprendizaje, capaz de modelizar la evaluación y generar automáticamente informes de evaluación formativa. Este modelo es una particularización del modelo granular lingüístico de un fenómeno (GLMP), en cuyo desarrollo y formalización colaboramos, basado en la lógica borrosa y en la teoría computacional de las percepciones. Esta técnica, que utiliza sistemas de inferencia basados en reglas lingüísticas y es capaz de implementar criterios de evaluación complejos, se ha aplicado a dos casos: la evaluación, basada en criterios, de logs de interacción generados por GRAPHs y de cuestionarios de Moodle. Como consecuencia, se han implementado, probado y utilizado en el aula sistemas expertos que evalúan ambos tipos de ejercicios. Además de la calificación numérica, los sistemas generan informes de evaluación, en lenguaje natural, sobre los niveles de competencia alcanzados, usando sólo datos objetivos de respuestas correctas e incorrectas. Además, se han desarrollado dos aplicaciones capaces de ser configuradas para implementar los sistemas expertos mencionados. Una procesa los archivos producidos por GRAPHs y la otra, integrable en Moodle, evalúa basándose en los resultados de los cuestionarios. ABSTRACT The concept of algorithm is one of the core subjects in computer science. It is extremely important, then, for students to get a good grasp of this concept from the very start of their training. In this respect, having a tool that helps and shepherds students through the process of learning this concept can make a huge difference to their instruction. Much has been written about how helpful algorithm visualization tools can be. Most authors agree that the most important part of the learning process is how students use the visualization tool. Learners who are actively involved in visualization consistently outperform other learners who view the algorithms passively. Therefore we think that one of the best exercises to learn an algorithm is for the user to simulate the algorithm execution while using a visualization tool, thus performing a visual algorithm simulation. The first part of this thesis presents the eMathTeacher set of requirements together with an eMathTeacher-compliant tool called GRAPHs. For some years, we have been developing a theory about what the key features of an effective e-learning system for teaching mathematical concepts and algorithms are. This led to the definition of eMathTeacher concept, which has materialized in the eMathTeacher set of requirements. An e-learning tool is eMathTeacher compliant if it works as a virtual math trainer. In other words, it has to be an on-line self-assessment tool that helps students to actively and autonomously learn math concepts or algorithms, correcting their mistakes and providing them with clues to find the right answer. In an eMathTeacher-compliant tool, algorithm simulation does not continue until the user enters the correct answer. GRAPHs is an extendible environment designed for active and independent visual simulation-based learning of graph algorithms, set up to integrate tools to help the user simulate the execution of different algorithms. Apart from the options of creating and editing the graph, and visualizing the changes made to the graph during simulation, the environment also includes step-by-step correction, algorithm pseudo-code animation, pop-up questions, data structure handling and XML-based interaction log creation features. On the other hand, assessment is a key part of any learning process. Through the use of e-learning environments huge amounts of data can be output about this process. Nevertheless, this information has to be interpreted and represented in a practical way to arrive at a sound assessment that is not confined to merely counting mistakes. This includes establishing relationships between the available data and also providing instructive linguistic descriptions about learning evolution. Additionally, formative assessment should specify the level of attainment of the learning goals defined by the instructor. Till now, only human experts were capable of making such assessments. While facing this problem, our goal has been to create a computational model that simulates the instructor’s reasoning and generates an enlightening learning evolution report in natural language. The second part of this thesis presents the granular linguistic model of learning assessment to model the assessment of the learning process and implement the automated generation of a formative assessment report. The model is a particularization of the granular linguistic model of a phenomenon (GLMP) paradigm, based on fuzzy logic and the computational theory of perceptions, to the assessment phenomenon. This technique, useful for implementing complex assessment criteria using inference systems based on linguistic rules, has been applied to two particular cases: the assessment of the interaction logs generated by GRAPHs and the criterion-based assessment of Moodle quizzes. As a consequence, several expert systems to assess different algorithm simulations and Moodle quizzes have been implemented, tested and used in the classroom. Apart from the grade, the designed expert systems also generate natural language progress reports on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. In addition, two applications, capable of being configured to implement the expert systems, have been developed. One is geared up to process the files output by GRAPHs and the other one is a Moodle plug-in set up to perform the assessment based on the quizzes results.

Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure.

A Computer-Assisted Personalized Approach in an Undergraduate Plant Physiology Class1

We used Computer-Assisted Personalized Approach (CAPA), a networked teaching and learning tool that generates computer individualized homework problem sets, in our large-enrollment introductory plant physiology course. We saw significant improvement in student examination performance with regular homework assignments, with CAPA being an effective and efficient substitute for hand-graded homework. Using CAPA, each student received a printed set of similar but individualized problems of a conceptual (qualitative) and/or quantitative nature with quality graphics. Because each set of problems is unique, students were encouraged to work together to clarify concepts but were required to do their own work for credit. Students could enter answers multiple times without penalty, and they were able to obtain immediate feedback and hints until the due date. These features increased student time on task, allowing higher course standards and student achievement in a diverse student population. CAPA handles routine tasks such as grading, recording, summarizing, and posting grades. In anonymous surveys, students indicated an overwhelming preference for homework in CAPA format, citing several features such as immediate feedback, multiple tries, and on-line accessibility as reasons for their preference. We wrote and used more than 170 problems on 17 topics in introductory plant physiology, cataloging them in a computer library for general access. Representative problems are compared and discussed.

Análisis de la satisfacción del alumnado de L2 con respecto a la adquisición de terminología especializada por medio de videojuegos: estudio de caso

Los videojuegos permiten enseñar contenidos y destrezas de forma eficiente, posibilitando un aprendizaje duradero (Rama et al., 2012), y aumentan la motivación y la implicación del alumnado (Martens et al., 2004). En esta línea, el presente estudio pretende medir tanto el grado de satisfacción de dos grupos de estudiantes de L2 de la Universidad de Alicante con respecto a la adquisición de terminología especializada por medio de un videojuego como la percepción sobre el propio grado de aprendizaje. Tras un periodo de práctica, se ha medido y analizado tanto el grado de aprendizaje alcanzado como la satisfacción con la herramienta empleada y, muy especialmente, las diferencias en el grado de aprendizaje percibido por cada uno de estos grupos.

Noves (i velles) didàctiques per a ensenyar llengua i literatura amb les TIC

Aquest article parteix de la hipòtesi inicial que les eines TIC presenten un gran potencial per millorar els processos d'ensenyament i aprenentatge de la llengua i la literatura, però que aquesta potencialitat (COLL: 2008) només es pot fer efectiva quan es fonamenta en una sòlida formació del professorat i quan l'ús de les TIC s'integra en metodologies actives que atorguen el protagonisme a l'estudiant i que se centren en l'acompanyament al llarg del procés, com els treballs per projectes o les seqüències didàctiques (SD) (CAMPS: 1994). Per desenvolupar aquest plantejament, primerament argumentaré la necessitat d'una formació dels docents que ha d'incloure tant la integració de les eines i dels recursos tecnològics que tenim al nostre abast, com els coneixements sobre els continguts que volem ensenyar i els coneixements pedagògics sobre com s'ensenya i com s'aprèn. I, tot seguit, em centraré en les característiques de les SD i en les possibilitats que obren per integrar els avenços que la recerca en didàctica de la llengua i de la literatura ha posat de manifest, i defensaré que són el millor marc per a un ús significatiu de les tecnologies de la informació i de la comunicació.

Prática de ensino supervisionada em Educação Pré-escolar e Ensino do 1.º Ciclo do Ensino Básico

O presente relatório pretende dar a conhecer parte do trabalho desenvolvido no âmbito de Unidade Curricular de Prática de Ensino Supervisionada (PES), integrada no Mestrado em Educação Pré-Escolar (EPE) e Ensino do 1.º Ciclo do Ensino Básico (1.º CEB) e visa aprofundar a reflexão acerca da ação educativa. No âmbito da EPE, a ação desenvolveu-se numa Instituição Particular de Solidariedade Social com um grupo de 16 crianças, com idades de 3 e 4 anos. No âmbito do 1.º CEB, a ação educativa ocorreu numa instituição da rede pública com uma turma de crianças do 1.º ano de escolaridade, constituída por 19 crianças, com idades de 6 e 7 anos. A ação educativa nos dois contextos foi desenvolvida no sentido de responder aos interesses e necessidades das crianças, sendo que as atividades propostas visaram uma aprendizagem realizada através da pesquisa, reflexão e descoberta, proporcionando às mesmas momentos de aprendizagens significativas, ativas e socializadoras. Com o decurso da prática, as atividades que desenvolvemos foram pensadas no sentido de darmos resposta a uma questão-problema: A criança e as expressões artísticas e físico-motoras: que relação de aprendizagem com outros saberes? Considerando esta interpelação estabelecemos como objetivos: (i) Compreender o contributo das Expressões Artísticas e Físico-Motoras no desenvolvimento social, emocional e cognitivo da criança e (ii) Utilizar as expressões artísticas como ferramenta de aprendizagem para a construção de conhecimentos noutras áreas do saber. O estudo enquadra-se numa abordagem mista (qualitativa/quantitativa). Para que fosse possível recolhermos a informação para a presente investigação, foi necessário selecionarmos um conjunto de técnicas e de instrumentos de recolha de dados. Para tal, recorremos à observação, aos registos fotográficos, às notas de campo e a um inquérito por questionário aos educadores/professores cooperantes. Salientamos que durante a apresentação das experiências de ensino-aprendizagem pretendemos ter sempre em conta as diferentes áreas de conteúdo/curriculares. Os dados decorrentes do quadro teórico e da ação educativa desenvolvida permitem relevar a importância das Expressões Artísticas e Físico-Motoras como um processo de ensino-aprendizagem para ajudar as crianças a aprenderem e a desenvolverem-se, constituindo uma mais-valia ao nível do enriquecimento das atividades e da concretização dos objetivos e metas de aprendizagem previstos para estas etapas. Palavras-chave:

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Induction by a Hilbert hypercube representation

This thesis describes a novel connectionist machine utilizing induction by a Hilbert hypercube representation. This representation offers a number of distinct advantages which are described. We construct a theoretical and practical learning machine which lies in an area of overlap between three disciplines - neural nets, machine learning and knowledge acquisition - hence it is refered to as a "coalesced" machine. To this unifying aspect is added the various advantages of its orthogonal lattice structure as against less structured nets. We discuss the case for such a fundamental and low level empirical learning tool and the assumptions behind the machine are clearly outlined. Our theory of an orthogonal lattice structure the Hilbert hypercube of an n-dimensional space using a complemented distributed lattice as a basis for supervised learning is derived from first principles on clearly laid out scientific principles. The resulting "subhypercube theory" was implemented in a development machine which was then used to test the theoretical predictions again under strict scientific guidelines. The scope, advantages and limitations of this machine were tested in a series of experiments. Novel and seminal properties of the machine include: the "metrical", deterministic and global nature of its search; complete convergence invariably producing minimum polynomial solutions for both disjuncts and conjuncts even with moderate levels of noise present; a learning engine which is mathematically analysable in depth based upon the "complexity range" of the function concerned; a strong bias towards the simplest possible globally (rather than locally) derived "balanced" explanation of the data; the ability to cope with variables in the network; and new ways of reducing the exponential explosion. Performance issues were addressed and comparative studies with other learning machines indicates that our novel approach has definite value and should be further researched.

Reflective journals:can they be used to identify struggling students?

The ability to identify early failure in knowledge accquisition amongst students is important because it enables tutors to put in place suitable interventions to help struggling students. We hypothesised that if a reflective learning journal is a useful learning tool, there ought to be relationship between the type of journal entries and the depth of knowledge acquisition. Our research question is: can reflectiuve journals be used to identify struggling students? Previous work with reflective journals has not related the level of reflection with module outcomes obtained by the student. In our study, we have classified journal entries written by first year students in a foundationalprogramming module based on the SOLO taxonomy and compared this against the outcomes of two module assessments. Our results suggest that there is potential for using reflective journals to identify struggling stuidents in first year programming.

Electrocardiogram breathing: A study of electrocardiogram derived respiration plus multiresolution wavelet analysis of heartbeat interval variability in sleep study patients

This dissertation presents a unique research opportunity by using recordings which provide electrocardiogram (ECG) plus a reference breathing signal (RBS). ECG derived breathing (EDR) is measured and correlated against RBS. Standard deviations of multiresolution wavelet analysis coefficients (SDMW) are obtained from heart rate and classified using RBS. Prior works by others used select patients for sleep apnea scoring with EDR but no RBS. Another prior work classified select heart disease patients with SDMW but no RBS. This study used randomly chosen sleep disorder patient recordings; central and obstructive apneas, with and without heart disease.^ Implementation required creating an application because existing systems were limited in power and scope. A review survey was created to choose a development environment. The survey is presented as a learning tool and teaching resource. Development objectives were rapid development using limited resources (manpower and money). Open Source resources were used exclusively for implementation. ^ Results show: (1) Three groups of patients exist in the study. Grouping RBS correlations shows a response with either ECG interval or amplitude variation. A third group exists where neither ECG intervals nor amplitude variation correlate with breathing. (2) Previous work done by other groups analyzed SDMW. Similar results were found in this study but some subjects had higher SDMW, attributed to a large number of apneas, arousals and/or disconnects. SDMW does not need RBS to show apneic conditions exist within ECG recordings. (3) Results in this study support the assertion that autonomic nervous system variation was measured with SDMW. Measurements using RBS are not corrupted due to breathing even though respiration overlaps the same frequency band.^ Overall, this work becomes an Open Source resource which can be reused, modified and/or expanded. It might fast track additional research. In the future the system could also be used for public domain data. Prerecorded data exist in similar formats in public databases which could provide additional research opportunities. ^

Ensino e aprendizagem de violão na UFRN

The instrumental teaching in Brazil has been more widely studied by music education in the last years, becoming a significative scientific investigation field. This work is focusing on analysis of the instrument teaching reality, considering more specifically the guitar teaching. In a timely manner, approaches the reality of the guitar teaching from the Universidade Federal do Rio Grande do Norte (Federal University of Rio Grande do Norte – UFRN), considering specific instrument courses in this institution. Thus, the present work was guided by the aim of understanding conceptions, contents and didactic and methodological perspectives that constitute the teaching and learning of the guitar at UFRN. To reach this main aim, we sought to verify, in general: 1) the theoretical, methodological and content perspectives that underlie guitar formation in the music courses of the UFRN; 2) the functions and the aims of the guitar disciplines in the Courses; 3) the profile of the guitar disciplines' professors; 4) the demands of the subjects directly involved in these proposals of musical education; and, 5) the conceptions about formation practices and teaching methodologies that characterize the docent action. In accordance with the aims, the methodological guidance was based on the qualitative paradigm. Thus, data collection procedures involved were: bibliographical and documentary research and interviews with three professors who taught in guitar disciplines during the semester of 2014.1 on the mentioned courses. Then, these data were systematically organized and analyzed in line with the research characteristics presented here. The results of the research evidenced the concepts, contents and didactic and methodological perspectives in the context of teaching and learning guitar in the studied empirical field. In this context, we perceive a relation between the demands of teaching and learning tool in the study field and the ones evidenced in the literature. The results demonstrated that the professors’ professional tragetory is associated with their influences from the initial musical formation and their guitar learning since they related conceptions that reflected aspects formation, sometimes converging sometimes divergente. The contentes, methodologies and general aspects are made based on parameters that demonstrate alignment with the perspectives of guitar teaching and learning in the contemporary world to the instrumental player formation. Thus, the study gave a broader diagnosis about the guitar teaching reality, demonstrating the diferences and the singularities between the Technical Course and the Course of Bachelor in Music at UFRN. Thereby, this study provides importante contribution to music education, mainly, through its reflections about the guitar pedagogy.