Machine Learning in Scientometrics

El aprendizaje automático y la cienciometría son las disciplinas científicas que se tratan en esta tesis. El aprendizaje automático trata sobre la construcción y el estudio de algoritmos que puedan aprender a partir de datos, mientras que la cienciometría se ocupa principalmente del análisis de la ciencia desde una perspectiva cuantitativa. Hoy en día, los avances en el aprendizaje automático proporcionan las herramientas matemáticas y estadísticas para trabajar correctamente con la gran cantidad de datos cienciométricos almacenados en bases de datos bibliográficas. En este contexto, el uso de nuevos métodos de aprendizaje automático en aplicaciones de cienciometría es el foco de atención de esta tesis doctoral. Esta tesis propone nuevas contribuciones en el aprendizaje automático que podrían arrojar luz sobre el área de la cienciometría. Estas contribuciones están divididas en tres partes: Varios modelos supervisados (in)sensibles al coste son aprendidos para predecir el éxito científico de los artículos y los investigadores. Los modelos sensibles al coste no están interesados en maximizar la precisión de clasificación, sino en la minimización del coste total esperado derivado de los errores ocasionados. En este contexto, los editores de revistas científicas podrían disponer de una herramienta capaz de predecir el número de citas de un artículo en el fututo antes de ser publicado, mientras que los comités de promoción podrían predecir el incremento anual del índice h de los investigadores en los primeros años. Estos modelos predictivos podrían allanar el camino hacia nuevos sistemas de evaluación. Varios modelos gráficos probabilísticos son aprendidos para explotar y descubrir nuevas relaciones entre el gran número de índices bibliométricos existentes. En este contexto, la comunidad científica podría medir cómo algunos índices influyen en otros en términos probabilísticos y realizar propagación de la evidencia e inferencia abductiva para responder a preguntas bibliométricas. Además, la comunidad científica podría descubrir qué índices bibliométricos tienen mayor poder predictivo. Este es un problema de regresión multi-respuesta en el que el papel de cada variable, predictiva o respuesta, es desconocido de antemano. Los índices resultantes podrían ser muy útiles para la predicción, es decir, cuando se conocen sus valores, el conocimiento de cualquier valor no proporciona información sobre la predicción de otros índices bibliométricos. Un estudio bibliométrico sobre la investigación española en informática ha sido realizado bajo la cultura de publicar o morir. Este estudio se basa en una metodología de análisis de clusters que caracteriza la actividad en la investigación en términos de productividad, visibilidad, calidad, prestigio y colaboración internacional. Este estudio también analiza los efectos de la colaboración en la productividad y la visibilidad bajo diferentes circunstancias. ABSTRACT Machine learning and scientometrics are the scientific disciplines which are covered in this dissertation. Machine learning deals with the construction and study of algorithms that can learn from data, whereas scientometrics is mainly concerned with the analysis of science from a quantitative perspective. Nowadays, advances in machine learning provide the mathematical and statistical tools for properly working with the vast amount of scientometrics data stored in bibliographic databases. In this context, the use of novel machine learning methods in scientometrics applications is the focus of attention of this dissertation. This dissertation proposes new machine learning contributions which would shed light on the scientometrics area. These contributions are divided in three parts: Several supervised cost-(in)sensitive models are learned to predict the scientific success of articles and researchers. Cost-sensitive models are not interested in maximizing classification accuracy, but in minimizing the expected total cost of the error derived from mistakes in the classification process. In this context, publishers of scientific journals could have a tool capable of predicting the citation count of an article in the future before it is published, whereas promotion committees could predict the annual increase of the h-index of researchers within the first few years. These predictive models would pave the way for new assessment systems. Several probabilistic graphical models are learned to exploit and discover new relationships among the vast number of existing bibliometric indices. In this context, scientific community could measure how some indices influence others in probabilistic terms and perform evidence propagation and abduction inference for answering bibliometric questions. Also, scientific community could uncover which bibliometric indices have a higher predictive power. This is a multi-output regression problem where the role of each variable, predictive or response, is unknown beforehand. The resulting indices could be very useful for prediction purposes, that is, when their index values are known, knowledge of any index value provides no information on the prediction of other bibliometric indices. A scientometric study of the Spanish computer science research is performed under the publish-or-perish culture. This study is based on a cluster analysis methodology which characterizes the research activity in terms of productivity, visibility, quality, prestige and international collaboration. This study also analyzes the effects of collaboration on productivity and visibility under different circumstances.

Low-status monkeys “play dumb” when learning in mixed social groups

Many primates, including humans, live in complex hierarchical societies where social context and status affect daily life. Nevertheless, primate learning studies typically test single animals in limited laboratory settings where the important effects of social interactions and relationships cannot be studied. To investigate the impact of sociality on associative learning, we compared the individual performances of group-tested rhesus monkeys (Macaca mulatta) across various social contexts. We used a traditional discrimination paradigm that measures an animal’s ability to form associations between cues and the obtaining of food in choice situations; but we adapted the task for group testing. After training a 55-member colony to separate on command into two subgroups, composed of either high- or low-status families, we exposed animals to two color discrimination problems, one with all monkeys present (combined condition), the other in their “dominant” and “subordinate” cohorts (split condition). Next, we manipulated learning history by testing animals on the same problems, but with the social contexts reversed. Monkeys from dominant families excelled in all conditions, but subordinates performed well in the split condition only, regardless of learning history. Subordinate animals had learned the associations, but expressed their knowledge only when segregated from higher-ranking animals. Because aggressive behavior was rare, performance deficits probably reflected voluntary inhibition. This experimental evidence of rank-related, social modulation of performance calls for greater consideration of social factors when assessing learning and may also have relevance for the evaluation of human scholastic achievement.

Material matters for learning in virtual networks : A case study of a professional learning programme hosted in a Google+ online community

Date of Acceptance: 13/07/2015

Perceptual learning in motion discrimination that generalizes across motion directions

When human subjects discriminate motion directions of two visual stimuli, their discrimination improves with practice. This improved performance has been found to be specific to the practiced directions and does not transfer to new motion directions. Indeed, such stimulus-specific learning has become a trademark finding in almost all perceptual learning studies and has been used to infer the loci of learning in the brain. For example, learning in motion discrimination has been inferred to occur in the visual area MT (medial temporal cortex) of primates, where neurons are selectively tuned to motion directions. However, such motion discrimination task is extremely difficult, as is typical of most perceptual learning tasks. When the difficulty is moderately reduced, learning transfers to new motion directions. This result challenges the idea of using simple visual stimuli to infer the locus of learning in low-level visual processes and suggests that higher-level processing is essential even in “simple” perceptual learning tasks.

Effect of reduced myristoylated alanine-rich C kinase substrate expression on hippocampal mossy fiber development and spatial learning in mutant mice: Transgenic rescue and interactions with gene background

The myristoylated alanine-rich C kinase substrate (MARCKS) is a prominent protein kinase C (PKC) substrate in brain that is expressed highly in hippocampal granule cells and their axons, the mossy fibers. Here, we examined hippocampal infrapyramidal mossy fiber (IP-MF) limb length and spatial learning in heterozygous Macs mutant mice that exhibit an ≈50% reduction in MARCKS expression relative to wild-type controls. On a 129B6(N3) background, the Macs mutation produced IP-MF hyperplasia, a significant increase in hippocampal PKCɛ expression, and proficient spatial learning relative to wild-type controls. However, wild-type 129B6(N3) mice exhibited phenotypic characteristics resembling inbred 129Sv mice, including IP-MF hypoplasia relative to inbred C57BL/6J mice and impaired spatial-reversal learning, suggesting a significant contribution of 129Sv background genes to wild-type and possibly mutant phenotypes. Indeed, when these mice were backcrossed with inbred C57BL/6J mice for nine generations to reduce 129Sv background genes, the Macs mutation did not effect IP-MF length or hippocampal PKCɛ expression and impaired spatial learning relative to wild-type controls, which now showed proficient spatial learning. Moreover, in a different strain (B6SJL(N1), the Macs mutation also produced a significant impairment in spatial learning that was reversed by transgenic expression of MARCKS. Collectively, these data indicate that the heterozygous Macs mutation modifies the expression of linked 129Sv gene(s), affecting hippocampal mossy fiber development and spatial learning performance, and that MARCKS plays a significant role in spatial learning processes.

A preference for own-subspecies' song guides vocal learning in a song bird

In many song birds, males develop their songs as adults by imitating the songs of one or more tutors, memorized previously during a sensitive phase early in life. Previous work using two assays, the production of imitations by adult males and playback-induced calling by young birds during the sensitive phase for memorization, has shown that song birds can discriminate between their own and other species' songs. Herein I use both assays to show that male mountain white-crowned sparrows, Zonotrichia leucophrys oriantha, must learn to sing but have a genetic predisposition to memorize and learn the songs of their own subspecies. Playback tests to young naive birds before they even begin to sing reveal that birds give begging calls more in response to oriantha song than to songs of another species. After 10 days of tutoring with songs of either their own or another subspecies, birds continue to give stronger call responses to songs of their own subspecies, irrespective of whether they were tutored with them, and are more discriminating in distinguishing between different dialects of their own subspecies. The memory processes that facilitate recognition and discrimination of own-subspecies' song may also mediate the preferential imitation of song of a bird's own subspecies. Such perceptual biases could constrain the direction and rate of cultural evolution of learned songs.

Traces of learning in the auditory localization pathway

One of the fascinating properties of the central nervous system is its ability to learn: the ability to alter its functional properties adaptively as a consequence of the interactions of an animal with the environment. The auditory localization pathway provides an opportunity to observe such adaptive changes and to study the cellular mechanisms that underlie them. The midbrain localization pathway creates a multimodal map of space that represents the nervous system's associations of auditory cues with locations in visual space. Various manipulations of auditory or visual experience, especially during early life, that change the relationship between auditory cues and locations in space lead to adaptive changes in auditory localization behavior and to corresponding changes in the functional and anatomical properties of this pathway. Traces of this early learning persist into adulthood, enabling adults to reacquire patterns of connectivity that were learned initially during the juvenile period.

Distance Education and the Evolution of Online Learning in the United States

Online education is no longer a trend, rather it is mainstream. In the Fall of 2012, 69.1% of chief academic leaders indicated online learning was critical to their long-term strategy and of the 20.6 million students enrolled in higher education, 6.7 million were enrolled in an online course (Allen & Seaman, 2013; United States Department of Education, 2013). The advent of online education and its rapid growth has forced academic institutions and faculty to question the current styles and techniques for teaching and learning. As developments in educational technology continue to advance, the ways in which we deliver and receive knowledge in both the traditional and online classrooms will further evolve. It is necessary to investigate and understand the progression and advancements in educational technology and the variety of methods used to deliver knowledge to improve the quality of education we provide today and motivate, inspire, and educate the students of the 21st century. This paper explores the atioevolution of distance education beginning with correspondence and the use of parcel post, to radio, then to television, and finally to online education.

Educating a New Cadre of Experts Specializing in Digital Collections and Digital Curation: Experiential Learning in Digital Library Curriculum

Integration of experiential learning into the library and information science (LIS) courses has been a theme in LIS education, but the topic deserves renewed attention with an increasing demand for professionals in the digital library field and in light of the new initiative announced by the Library of Congress (LC) and the Institution of Museum and Library Services (IMLS) for national residency program in digital curation. The balance between theory and practice in digital library curricula, the challenges of incorporating practical projects into LIS coursework, and the current practice of teaching with hands on activities represent the primary areas of this panel discussion.

Is video-case an efficient tool to complement the practical learning in real contexts? Effects on satisfaction and performance in Social Work Degree students

Este documento es un artículo inédito que ha sido aceptado para su publicación. Como un servicio a sus autores y lectores, Alternativas. Cuadernos de trabajo social proporciona online esta edición preliminar. El manuscrito puede sufrir alteraciones tras la edición y corrección de pruebas, antes de su publicación definitiva. Los posibles cambios no afectarán en ningún caso a la información contenida en esta hoja, ni a lo esencial del contenido del artículo.

Economics of Policy Errors and Learning in the European Union. Bruges European Economic Policy (BEEP) Briefing 31/2013

Policy errors occur regularly in EU Member States. Learning from these errors can be beneficial. This paper explains how the European Union can facilitate this learning. At present, much attention is given to “best practices”. But learning from mistakes is also valuable. The paper develops the concept of “avoidable error” and examines evidence from infringement proceedings and special reports of the European Court of Auditors which indicate that Member States do indeed commit avoidable errors. The paper considers how Member States may take measures not to repeat avoidable or predictable errors and makes appropriate proposals.