Essays on Machine Learning and Hedonic Models

Thesis (Ph.D.)--University of Washington, 2016-08

Applications of a Collaborative Open Object Oriented Learning Environment to Electronics, Computing and Mathematics Education

A lightweight Java application suite has been developed and deployed allowing collaborative learning between students and tutors at remote locations. Students can engage in group activities online and also collaborate with tutors. A generic Java framework has been developed and applied to electronics, computing and mathematics education. The applications are respectively: (a) a digital circuit simulator, which allows students to collaborate in building simple or complex electronic circuits; (b) a Java programming environment where the paradigm is behavioural-based robotics, and (c) a differential equation solver useful in modelling of any complex and nonlinear dynamic system. Each student sees a common shared window on which may be added text or graphical objects and which can then be shared online. A built-in chat room supports collaborative dialogue. Students can work either in collaborative groups or else in teams as directed by the tutor. This paper summarises the technical architecture of the system as well as the pedagogical implications of the suite. A report of student evaluation is also presented distilled from use over a period of twelve months. We intend this suite to facilitate learning between groups at one or many institutions and to facilitate international collaboration. We also intend to use the suite as a tool to research the establishment and behaviour of collaborative learning groups. We shall make our software freely available to interested researchers.

The professional developmental needs of higher education-based teacher educators: an international comparative needs analysis

A universal lack of attention to the professional learning needs of teacher educators is the driver for this study, which considers the most effective ways to support the professional learning of higher education-based teacher educators. At a time when many industrialised countries are engaged in systemic educational reform, this study provides an international and comparative needs analysis through a survey of 1,158 higher education-based teacher educators in the countries participating in the International Forum for Teacher Educator Development (InFo-TED): Belgium, Ireland, Israel, the Netherlands, Norway and the UK. Our results suggest that while teacher educators are only moderately satisfied with their professional development experiences, a strong desire exists for further professional learning. This desire, influenced by their professional context, relates to their current beliefs concerning ‘best practice’ in teacher education, the academic skills required to further their professional careers and knowledge of the curriculum associated with their fields of expertise.

Medication competence of nursing students in Finland

Lääkehoidon turvallinen toteuttaminen edellyttää sairaanhoitajalta hyvää lääkehoidon osaamisperustaa. Sairaanhoitajakoulutuksen tehtävänä on mahdollistaa tämän osaamisen kehittyminen. Kansainvälisissä tutkimuksissa on kuitenkin osoitettu, että lääkehoidon opetuksen laajuudessa, sisällössä ja toteutuksessa on vaihtelevuutta. Aikaisemmissa tutkimuksissa on raportoitu myös puutteita lääkehoidon osaamisessa sekä sairaanhoitajilla että sairaanhoitajaopiskelijoilla. Koulutuksen ja lääkehoidon osaamisen kehittämiseksi lääkehoidon opetuksen ja sairaanhoitajaopiskelijoiden lääkehoidon osaamisen monipuolinen arviointi ja osaamista selittävien tekijöiden tarkastelu on tarpeen. Tämän tutkimuksen tarkoituksena oli i) arvioida lääkehoidon opetusta suomalaisessa sairaanhoitajakoulutuksessa, ii) arvioida sairaanhoitajaopiskelijoiden lääkehoidon osaamista sekä iii) tunnistaa sairaanhoitajaopiskelijan lääkehoidon osaamiseen yhteydessä olevat tekijät. Tutkimus toteutettiin kolmessa vaiheessa. Ensimmäisessä vaiheessa kahden integroidun kirjallisuuskatsauksen kautta määriteltiin tutkimuksen kohteena oleva sairaanhoitajan lääkehoidon osaaminen ja aiemmin tunnistetut sairaanhoitajaopiskelijan lääkehoidon osaamiseen yhteydessä olevat tekijät. Toisessa vaiheessa toteutettiin valtakunnallinen lääkehoidon opetukseen liittyvä kysely hoitotyön koulutusohjelmasta vastaaville koulutuspäälliköille (n=22) ja opettajille (n=136). Tutkimuksen kolmannessa vaiheessa opintojensa alku‐ (n=328) ja loppuvaiheessa olevien sairaanhoitajaopiskelijoiden (n=338) lääkehoidon osaaminen arvioitiin ja osaamiseen yhteydessä olevat tekijät tunnistettiin. Aineistojen analyysissä käytettiin pääosin tilastollisia menetelmiä. Tulosten perusteella lääkehoidon opetuksen laajuus vaihteli eri ammattikorkeakouluissa, mutta opetuksen sisältö oli kuitenkin monipuolista. Lisää huomiota tulisi kiinnittää lääkehoidon teoreettiseen perustaan ja itsehoitoon sekä lääkehoidon ohjaukseen liittyviin sisältöalueisiin. Opiskelijoiden lääkehoidon osaamista arvioitiin säännöllisesti kaikissa ammattikorkeakouluissa. Sairaanhoitajaopiskelijan lääkehoidon osaamista arvioitiin tutkimuksessa tietotestillä, lääkelaskentatehtävillä ja lyhyiden potilastapausten ratkaisemisen avulla. Lääkehoidon osaamiseen yhteydessä olevia tekijöitä tarkasteltiin kolmesta näkökulmasta: 1) yksilölliset tekijät, 2) kliiniseen oppimisympäristöön ja 3) ammattikorkeakouluun liittyvät tekijät. Lääkehoidon teoreettista osaamista arvioivassa tietotestissä opiskelijat vastasivat keskimäärin 72 prosenttiin kysymyksistä täysin oikein; lääkelaskuista täysin oikein oli 74 % ja potilastapauksissa 57 % valitsi parhaan mahdollisen toimintatavan. Tulosten perusteella sairaanhoitajaopiskelijan osaamista selittivät eniten yksilölliset tekijät. Lääkehoidon osaamiseen yhteydessä olevien tekijöiden välillä oli eroa opintojen alussa ja lopussa. Opintojen alkuvaiheessa opiskelijan aikaisempi opintomenestys oli yhteydessä lääkehoidon osaamiseen, kun taas opintojen loppuvaiheessa siihen olivat yhteydessä opiskelijan kyky itseohjautuvaan oppimiseen sekä opiskelumotivaatio. Johtopäätöksenä voidaan todeta tutkimuksen tulosten olevan samansuuntaisia kuin aikaisemmissa tutkimuksissa. Lääkehoidon opetuksen laajuus vaihtelee opetussuunnitelmatasolla, mutta täsmällinen arviointi on vaikeaa opetuksen sisältöjen integroimisen takia. Sairaanhoitajaopiskelijoiden lääkehoidon osaaminen oli hieman parempaa kuin aikaisemmissa tutkimuksissa, mutta osaamisessa on edelleen puutteita. Lääkehoidon opetuksen ja osaamisen kehittäminen edellyttää kansallista ja kansainvälistä tutkimus‐ ja kehittämisyhteistyötä. Tutkimuksen tulokset tukevat lääkehoidon opetuksen sekä osaamisen tutkimusta ja kehittämistä.

Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

An exploratory study of orthoptic student satisfaction in a blended learning environment

Purpose: To describe orthoptic student satisfaction in a blended learning environment. Methods: Blended learning and teaching approaches that include a mix of sessions with elearning are being used since 2011/2012 involving final year (4th year) students from an orthoptic program. This approach is used in the module of research in orthoptics during the 1 semester. Students experienced different teaching approaches, which include seminars, tutorial group discussions and e-learning activities using the moodle platform. The Constructivist OnLine Learning Environment Survey (COLLES ) was applied at the end of the semester with 24 questions grouped in 6 dimensions with 4 items each: Relevance to professional practice, Reflection, Interactivity, Tutor support, Peer support and Interpretation. A 5-point Likert scale was used to score each individual item of the questionnaire (1 - almost never to 5 – almost always). The sum of items in each dimension ranged between 4 (negative perception) and 20 (positive perception). Results: Twenty-four students replied to the questionnaire. Positive points were related with Relevance (16.13±2.63), Reflection (16.46±2.45), Tutor support (16.29±2.10) and Interpretation (15.38±2.16). The majority of the students (n=18; 75%) think that the on-line learning is relevant to students’ professional practice. Critical reflections about learning contents were frequent (n=19; 79.17%). The tutor was able to stimulate critical thinking (n=21; 87.50%), encouraged students to participate (n=18; 75%) and understood well the student’s contributions (n=15; 62.50%). Less positive points were related with Interactivity (14.13±2.77) and Peer support (13.29±2.60). Response from the colleagues to ideas (n=11; 45.83%) and valorization of individual contributions (n=10; 41.67%) scored lower than other items. Conclusions: The flow back and forth between face-to-face and online learning situations helps the students to make critical reflections. The majority of the students are satisfied with a blended e-learning system environment. However, more work needs to be done to improve interactivity and peer support.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Assessment of faculty training program for the use of e-learning platforms: analysis of current use

5th International Conference on Education and New Learning Technologies (Barcelona, Spain. 1-3 July, 2013)

Spectral Frame Analysis and Learning through Graph Structure

This dissertation investigates the connection between spectral analysis and frame theory. When considering the spectral properties of a frame, we present a few novel results relating to the spectral decomposition. We first show that scalable frames have the property that the inner product of the scaling coefficients and the eigenvectors must equal the inverse eigenvalues. From this, we prove a similar result when an approximate scaling is obtained. We then focus on the optimization problems inherent to the scalable frames by first showing that there is an equivalence between scaling a frame and optimization problems with a non-restrictive objective function. Various objective functions are considered, and an analysis of the solution type is presented. For linear objectives, we can encourage sparse scalings, and with barrier objective functions, we force dense solutions. We further consider frames in high dimensions, and derive various solution techniques. From here, we restrict ourselves to various frame classes, to add more specificity to the results. Using frames generated from distributions allows for the placement of probabilistic bounds on scalability. For discrete distributions (Bernoulli and Rademacher), we bound the probability of encountering an ONB, and for continuous symmetric distributions (Uniform and Gaussian), we show that symmetry is retained in the transformed domain. We also prove several hyperplane-separation results. With the theory developed, we discuss graph applications of the scalability framework. We make a connection with graph conditioning, and show the in-feasibility of the problem in the general case. After a modification, we show that any complete graph can be conditioned. We then present a modification of standard PCA (robust PCA) developed by Cand\`es, and give some background into Electron Energy-Loss Spectroscopy (EELS). We design a novel scheme for the processing of EELS through robust PCA and least-squares regression, and test this scheme on biological samples. Finally, we take the idea of robust PCA and apply the technique of kernel PCA to perform robust manifold learning. We derive the problem and present an algorithm for its solution. There is also discussion of the differences with RPCA that make theoretical guarantees difficult.

EXAMINING THE IMPLEMENTATION CHALLENGES OF PROJECT-BASED LEARNING: A CASE STUDY

Title of Thesis: Thesis directed by: ABSTRACT EXAMINING THE IMPLEMENTATION CHALLENGES OF PROJECT-BASED LEARNING: A CASE STUDY Stefan Frederick Brooks, Master of Education, 2016 Professor and Chair Francine Hultgren Teaching and Learning, Policy and Leadership Department Project-based learning (PjBL) is a common instructional strategy to consider for educators, scholars, and advocates who focus on education reform. Previous research on PjBL has focused on its effectiveness, but a limited amount of research exists on the implementation challenges. This exploratory case study examines an attempted project- based learning implementation in one chemistry classroom at a private school that fully supports PjBL for most subjects with limited use in mathematics. During the course of the study, the teacher used a modified version of PjBL. Specifically, he implemented some of the elements of PjBL, such as a driving theme and a public presentation of projects, with the support of traditional instructional methods due to the context of the classroom. The findings of this study emphasize the teacher’s experience with implementing some of the PjBL components and how the inherent implementation challenges affected his practice.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Movement Sonification: Effects on Motor Learning beyond Rhythmic Adjustments

Motor learning is based on motor perception and emergent perceptual-motor representations. A lot of behavioral research is related to single perceptual modalities but during last two decades the contribution of multimodal perception on motor behavior was discovered more and more. A growing number of studies indicates an enhanced impact of multimodal stimuli on motor perception, motor control and motor learning in terms of better precision and higher reliability of the related actions. Behavioral research is supported by neurophysiological data, revealing that multisensory integration supports motor control and learning. But the overwhelming part of both research lines is dedicated to basic research. Besides research in the domains of music, dance and motor rehabilitation, there is almost no evidence for enhanced effectiveness of multisensory information on learning of gross motor skills. To reduce this gap, movement sonification is used here in applied research on motor learning in sports. Based on the current knowledge on the multimodal organization of the perceptual system, we generate additional real-time movement information being suitable for integration with perceptual feedback streams of visual and proprioceptive modality. With ongoing training, synchronously processed auditory information should be initially integrated into the emerging internal models, enhancing the efficacy of motor learning. This is achieved by a direct mapping of kinematic and dynamic motion parameters to electronic sounds, resulting in continuous auditory and convergent audiovisual or audio-proprioceptive stimulus arrays. In sharp contrast to other approaches using acoustic information as error-feedback in motor learning settings, we try to generate additional movement information suitable for acceleration and enhancement of adequate sensorimotor representations and processible below the level of consciousness. In the experimental setting, participants were asked to learn a closed motor skill (technique acquisition of indoor rowing). One group was treated with visual information and two groups with audiovisual information (sonification vs. natural sounds). For all three groups learning became evident and remained stable. Participants treated with additional movement sonification showed better performance compared to both other groups. Results indicate that movement sonification enhances motor learning of a complex gross motor skill-even exceeding usually expected acoustic rhythmic effects on motor learning.

Learning strategies implemented by the students of Advanced Intensive English I courses for improving oral discourse in the Bachelor of Arts in English Teaching, Department of Foreign Languages, University of El Salvador, year 2014.

There are many learning strategies some, more successful than others when they are applied in a correct way. “Strategies are most successful when they are implemented in a system that encourages collaboration among staff and students, and in which each is a part of a well-planned whole system” (Johns Hopkins, 2000). Additionally, Learning strategies have become an effective instrument in the field of education because students can make use of several strategies in order to enhance their English level in terms of communication. To communicate in a meaningful way, it is important to express ideas inside and outside the classroom; it is part of the development and improvement of speaking.