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.

Teaching College Writing in a High School Setting: The Impact of Concurrent Enrollment on Teacher Learning and Practice

Thesis (Ph.D.)--University of Washington, 2016-06

Attention vs. emotion: Affective learning and the startle reflex

The eye-blink startle reflex can be modulated by attentional and emotional processes. The reflex is facilitated during stimuli that engage attention. A linear pattern of emotional modulation has also been consistently demonstrated: the reflex is facilitated during unpleasant stimuli and attenuated during pleasant stimuli. However, during anticipation of pleasant or unpleasant stimuli it is unclear whether emotion or attention drives startle reflex modulation. This study used a differential learning procedure to investigate whether startle modulation during anticipation of a salient stimulus reflected emotional or attentional processes. In acquisition, a CS+ was paired with a pleasant or unpleasant US and a CS- was presented alone. In extinction, blink startle magnitude was measured during CS+ and CS-. Post-acquisition valence ratings and affective priming showed that CS+ had acquired the same affective value as the pleasant or unpleasant US with which it was paired. No differences in modulation of blink startle reflexes during pleasant CS+ and unpleasant CS+ were found throughout extinction. Blink startle facilitation occurred during CS+ but not CS- across the first third of extinction. Thus, attentional rather than emotional processes appeared to facilitate blink startle during anticipation of salient stimuli.

Inspiring lecturers: sharing e-learning practice through european case studies

What motivates a university lecturer to consider introducing a new e-learning approach to their educational practice? Accounts of e-learning practice can invite discussion and reflection on the approaches taken, reinforcement of a particular model, connection with the experience of others, vicarious learning opportunities and glimpses into tacit knowledge. If these examples provoke thinking, could they have the ‘sticky qualities’, the memorable inspiration and ideas that move us to action, when we observe the practice of others? (Szulanski, 2003) “Case studies have the capacity to inspire but also to provoke and to challenge.” (JISC, 2004) This paper will discuss a process followed for sharing best practices of e-learning. It will explain how good practices were identified and gathered by the EUNIS E-Learning Task Force collaboration, using a database and a weblog (EUNIC, 2008). It will examine the methods used for the developing and compiling of the practices and the communication of these. Actual examples of some of the case studies gathered will be included in an appendix. Suggestions of ways to develop this process further and the tangible benefits identified will be examined to ask if effective practice can also become embedded practice.

Munkahelyi tanulást befolyásoló tényezők – humán-számítógép együttműködés vizsgálata (Influencing factors of on-the-job learning – Analysis of human-computer collaboration)

A komplex, dinamikus, tudásalapú társadalomban nemcsak a tanulás formái, hanem a tanulás helyszínei is módosulnak, s a munkahely tanulásban betöltött szerepe felértékelődött. A munkahelyi környezet is számos átalakuláson esett át, az információs és kommunikációs technológiák (IKT) fejlődésével egyidejűleg lehetővé vált többek között a távmunka, jelentősen átformálva a munkavégzés és a munkahelyi interakciók módját. A kutatók arra keresték a választ kutatásukban, hogy a szervezeten belül milyen tényezők támogatják vagy gátolják a munkahelyi tanulást. A kutatás fő üzenete, hogy a tanulás keretrendszere, az egyéni képességek és az észlelt tanulási szituáció együttesen határozza meg a munkahelyi tanulást. A kutatók eredményüket kvalitatív kutatással feltárt három esettanulmányon keresztül ismertetik. ____ In a knowledge-based society not only the forms of learning have been changed but also the places of learning. The role of workplace in the learning process is becoming more important. Meantime, there has been a substantial change in the working conditions as the development in information and communication technologies (ICTs) makes it possible to telecommute transforming remarkably the way of working and the interactions at the workplace. The central question of the research is which intra-organizational factors support or hinder onthe- job learning. The main message of the research is that the learning framework, the individual cognitive competences and the perceived learning situation influence collectively on-the-job learning. Authors present the results of the qualitative research though three case studies.

Theological reflection at work: A phenomenological study of learning processes

Using the learning descriptions of graduates of a graduate ministry program, the mechanisms of interactions between the knowledge facets in learning processes were explored and described. The intent of the study was to explore how explicit, implicit, and emancipatory knowledge facets interacted in the learning processes at or about work. The study provided empirical research on Yang's (2003) holistic learning theory. ^ A phenomenological research design was used to explore the essence of knowledge facet interactions. I achieved epoche through the disclosure of assumptions and a written self-experience to bracket biases. A criterion based, stratified sampling strategy was used to identify participants. The sample was stratified by graduation date. The sample consisted of 11 participants and was composed primarily of married (n = 9), white, non-Hispanic (n = 10), females (n = 9), who were Roman Catholic (n = 9). Professionally, the majority of the group were teachers or professors (n = 5). ^ A semi-structured interview guide with scheduled and unscheduled probes was used. Each approximately 1-hour long interview was digitally recorded and transcribed. The transcripts were coded using a priori codes from holistic learning theory and one emergent code. The coded data were analyzed by identifying patterns, similarities, and differences under each code and then between codes. Steps to increase the trustworthiness of the study included member checks, coding checks, and thick descriptions of the data. ^ Five themes were discovered including (a) the difficulty in describing interactions between knowledge facets; (b) actual mechanisms of interactions between knowledge facets; (c) knowledge facets initiating learning and dominating learning processes; (d) the dangers of one-dimensional learning or using only one knowledge facet to learn; and (e) the role of community in learning. The interpretation confirmed, extended, and challenged holistic learning theory. Mechanisms of interaction included knowledge facets expressing, informing, changing, and guiding one another. Implications included the need for a more complex model of learning and the value of seeing spirituality in the learning process. The study raised questions for future research including exploring learning processes with people from non-Christian faith traditions or other academic disciplines and the role of spiritual identity in learning. ^