Cerebellar mechanisms for motor learning: Testing predictions from a large-scale computer simulation

The cerebellum is the major brain structure that contributes to our ability to improve movements through learning and experience. We have combined computer simulations with behavioral and lesion studies to investigate how modification of synaptic strength at two different sites within the cerebellum contributes to a simple form of motor learning—Pavlovian conditioning of the eyelid response. These studies are based on the wealth of knowledge about the intrinsic circuitry and physiology of the cerebellum and the straightforward manner in which this circuitry is engaged during eyelid conditioning. Thus, our simulations are constrained by the well-characterized synaptic organization of the cerebellum and further, the activity of cerebellar inputs during simulated eyelid conditioning is based on existing recording data. These simulations have allowed us to make two important predictions regarding the mechanisms underlying cerebellar function, which we have tested and confirmed with behavioral studies. The first prediction describes the mechanisms by which one of the sites of synaptic modification, the granule to Purkinje cell synapses (gr → Pkj) of the cerebellar cortex, could generate two time-dependent properties of eyelid conditioning—response timing and the ISI function. An empirical test of this prediction using small, electrolytic lesions of the cerebellar cortex revealed the pattern of results predicted by the simulations. The second prediction made by the simulations is that modification of synaptic strength at the other site of plasticity, the mossy fiber to deep nuclei synapses (mf → nuc), is under the control of Purkinje cell activity. The analysis predicts that this property should confer mf → nuc synapses with resistance to extinction. Thus, while extinction processes erase plasticity at the first site, residual plasticity at mf → nuc synapses remains. The residual plasticity at the mf → nuc site confers the cerebellum with the capability for rapid relearning long after the learned behavior has been extinguished. We confirmed this prediction using a lesion technique that reversibly disconnected the cerebellar cortex at various stages during extinction and reacquisition of eyelid responses. The results of these studies represent significant progress toward a complete understanding of how the cerebellum contributes to motor learning. ^

From Concrete to Abstract: Teaching for Transfer of Learning when Using Manipulatives

One of the most important uses of manipulatives in a classroom is to aid a learner to make connection from tangible concrete object to its abstraction. In this paper we discuss how teacher educators can foster deeper understanding of how manipulatives facilitate student learning of math concepts by emphasizing the connection between concrete objects and math symbolization with, preservice elementary teachers, the future implementers of knowledge. We provide an example and a model, with specific steps of how teacher educators can effectively demonstrate connections between concrete objects and abstract math concepts.

Learning about Self Far From Home: A Pre-service Teacher’s Intercultural Development During an International Program

Preparing teachers to effectively teach culturally diverse students, teacher educators advocate for the use of cross-cultural field experiences, including international study abroad programs. This paper reports on a qualitative case study of two pre-service teachers’ intercultural development during a semester-long teacher education study abroad program in London, England. Findings indicate that international experiences provide a catalyst to move pre-service teachers forward in their intercultural development. Implications include the need for multicultural teacher educators to take a developmental approach to pre-service teacher education informed by theories of intercultural development and cultural learning developed within intercultural communications.

A descriptive study of the sociodemographic, health and developmental factors associated with slow learning in children from upper to middle social class families in Texas

"Slow Learners" is a term used to describe children with an IQ range of 70-89 on a standardized individual intelligence test (i.e. with a standard deviation of either 15 or 16). They have above retarded, but below average intelligence and potential to learn. If the factors associated with the etiology of slow learning in children can be identified, it may be possible to hypothesize causal relationships which can be tested by intervention studies specifically designed to prevent slow learning. If effective, these may ultimately reduce the incidence of school dropouts and their cost to society. To date, there is little information about variables which may be etiologically significant. In an attempt to identify such etiologic factors this study examines the sociodemographic characteristics, prenatal history (hypertension, smoking, infections, medication, vaginal bleeding, etc.), natal history (length of delivery, Apgar score, birth trauma, resuscitation, etc.), neonatal history (infections, seizures, head trauma, etc.), developmental history (health problems, developmental milestones and growth during infancy and early childhood), and family history (educational level of the parents, occupation, history of similar condition in the family, etc.) of a series of children defined as slow learners. The study is limited to children from middle to high socioeconomic families in order to exclude the possible confounding variable of low socioeconomic status, and because a descriptive study of this group has not been previously reported. ^

Learning kinds for students of technical high school : An analysis from the David Ausubel's theory

This article presents the findings of a field research, not experimental, observational, correlating, basic, of mixed data, micro sociologic, leading to a study of surveys.The object of study is to find learning kinds, and the unit of analysis were 529 high school students between 16 and 21 years old. Its purpose is to understand the impact of learning by rote, guided, self learned and meaningful learning and its achievement degree besides the learning outcomes of differentiated curriculum based on David Ausubel's thoughts, associated with different economic specialties (MINEDUC, 1998) where the population of the study is trained. To collect data, the test TADA - DO2 was used, this test has a reliability index of 0.911 according to Cronbach. From the hits it can be stated from the null hypothesis that there is a significant association (a = 0,05) between the learning kinds and the learning expected of differentiated training plan for both, male and female. It is complex to state that the training of the middle-level technicians leads to a successful employment.

Learning kinds for students of technical high school : An analysis from the David Ausubel's theory

This article presents the findings of a field research, not experimental, observational, correlating, basic, of mixed data, micro sociologic, leading to a study of surveys.The object of study is to find learning kinds, and the unit of analysis were 529 high school students between 16 and 21 years old. Its purpose is to understand the impact of learning by rote, guided, self learned and meaningful learning and its achievement degree besides the learning outcomes of differentiated curriculum based on David Ausubel's thoughts, associated with different economic specialties (MINEDUC, 1998) where the population of the study is trained. To collect data, the test TADA - DO2 was used, this test has a reliability index of 0.911 according to Cronbach. From the hits it can be stated from the null hypothesis that there is a significant association (a = 0,05) between the learning kinds and the learning expected of differentiated training plan for both, male and female. It is complex to state that the training of the middle-level technicians leads to a successful employment.

Learning kinds for students of technical high school : An analysis from the David Ausubel's theory

This article presents the findings of a field research, not experimental, observational, correlating, basic, of mixed data, micro sociologic, leading to a study of surveys.The object of study is to find learning kinds, and the unit of analysis were 529 high school students between 16 and 21 years old. Its purpose is to understand the impact of learning by rote, guided, self learned and meaningful learning and its achievement degree besides the learning outcomes of differentiated curriculum based on David Ausubel's thoughts, associated with different economic specialties (MINEDUC, 1998) where the population of the study is trained. To collect data, the test TADA - DO2 was used, this test has a reliability index of 0.911 according to Cronbach. From the hits it can be stated from the null hypothesis that there is a significant association (a = 0,05) between the learning kinds and the learning expected of differentiated training plan for both, male and female. It is complex to state that the training of the middle-level technicians leads to a successful employment.

Is Learning by Migrating to a Megalopolis Really Important? Evidence from Thailand

We examine the effects of learning by migrating on the productivity of migrants who move to a "megalopolis" from rural areas using the Thailand Labor Force Survey. The main contribution is to the development a simple framework to test for self-selection on migration decisions and learning by migrating into the urban labor market, focusing on experimental evidence in the observational data. The role of the urban labor market is examined. In conclusion, we find significant evidence for sorting: the self-selection effects test (1) is positive among new entrants from rural areas to the urban labor market; and (2) is negative among new exits that move to rural areas from the urban labor market. Further, estimated effects of learning by migrating into a "megalopolis" have a less significant impact. These results suggest the existence of a natural selection (i.e. survival of the fittest) mechanism in the urban labor market in a developing economy.

Integrated learning of computer applications for production engineering

A high productivity rate in Engineering is related to an efficient management of the flow of the large quantities of information and associated decision making activities that are consubstantial to the Engineering processes both in design and production contexts. Dealing with such problems from an integrated point of view and mimicking real scenarios is not given much attention in Engineering degrees. In the context of Engineering Education, there are a number of courses designed for developing specific competencies, as required by the academic curricula, but not that many in which integration competencies are the main target. In this paper, a course devoted to that aim is discussed. The course is taught in a Marine Engineering degree but the philosophy could be used in any Engineering field. All the lessons are given in a computer room in which every student can use each all the treated software applications. The first part of the course is dedicated to Project Management: the students acquire skills in defining, using Ms-PROJECT, the work breakdown structure (WBS), and the organization breakdown structure (OBS) in Engineering projects, through a series of examples of increasing complexity, ending up with the case of vessel construction. The second part of the course is dedicated to the use of a database manager, Ms-ACCESS, for managing production related information. A series of increasing complexity examples is treated ending up with the management of the pipe database of a real vessel. This database consists of a few thousand of pipes, for which a production timing frame is defined, which connects this part of the course with the first one. Finally, the third part of the course is devoted to the work with FORAN, an Engineering Production package of widespread use in the shipbuilding industry. With this package, the frames and plates where all the outfitting will be carried out are defined through cooperative work by the studens, working simultaneously in the same 3D model. In the paper, specific details about the learning process are given. Surveys have been posed to the students in order to get feed-back from their experience as well as to assess their satisfaction with the learning process. Results from these surveys are discussed in the paper

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Integrated learning of production engineering software applications in a shipbuilding context

Acourse focused on the acquisition of integration competencies in ship production engineering, organized in collaboration with selected industry partners, is presented in this paper. The first part of the course is dedicated to Project Management: the students acquire skills in defining, using MS-PROJECT, the work breakdown structure (WBS), and the organization breakdown structure (OBS) in Engineering projects, through a series of examples of increasing complexity with the final one being the construction planning of a vessel. The second part of the course is dedicated to the use of a database manager, MS-ACCESS, in managing production related information.Aseries of increasing complexity examples is treated, the final one being the management of the piping database of a real vessel. This database consists of several thousand pipes, for which a production timing frame is defined connecting this part of the course with the first one. Finally, the third part of the course is devoted to working withFORAN,an Engineering Production application developed bySENERand widely used in the shipbuilding industry. With this application, the structural elements where all the outfittings will be located are defined through cooperative work by the students, working simultaneously in the same 3D model. In this paper, specific details about the learning process are given. Surveys have been posed to the students in order to get feedback from their experience as well as to assess their satisfaction with the learning process, compared to more traditional ones. Results from these surveys are discussed in the paper.

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Learning Bayesian networks from data by the incremental compilation of new network polynomials

Probabilistic graphical models are a huge research field in artificial intelligence nowadays. The scope of this work is the study of directed graphical models for the representation of discrete distributions. Two of the main research topics related to this area focus on performing inference over graphical models and on learning graphical models from data. Traditionally, the inference process and the learning process have been treated separately, but given that the learned models structure marks the inference complexity, this kind of strategies will sometimes produce very inefficient models. With the purpose of learning thinner models, in this master thesis we propose a new model for the representation of network polynomials, which we call polynomial trees. Polynomial trees are a complementary representation for Bayesian networks that allows an efficient evaluation of the inference complexity and provides a framework for exact inference. We also propose a set of methods for the incremental compilation of polynomial trees and an algorithm for learning polynomial trees from data using a greedy score+search method that includes the inference complexity as a penalization in the scoring function.

The usefulness of Usability and User Experience evaluation methods on an e-Learning platform development from a developer's perspective: A case study

The development of a web platform is a complex and interdisciplinary task, where people with different roles such as project manager, designer or developer participate. Different usability and User Experience evaluation methods can be used in each stage of the development life cycle, but not all of them have the same influence in the software development and in the final product or system. This article presents the study of the impact of these methods applied in the context of an e-Learning platform development. The results show that the impact has been strong from a developer's perspective. Developer team members considered that usability and User Experience evaluation allowed them mainly to identify design mistakes, improve the platform's usability and understand the end users and their needs in a better way. Interviews with potential users, clickmaps and scrollmaps were rated as the most useful methods. Finally, these methods were considered unanimously very useful in the context of the entire software development, only comparable to SCRUM meetings and overcoming the rest of involved factors.