Pragmatic Intelligence in Argumentation: Towards an Analytical Model

يتناول هذا البحث الذكاء التداولي كما هو مستخدم في سياق الحجاج وهو نوع من التواصل ثنائي الاتجاه، على سبيل المثال، المناظرة السياسية، ولم يحظ مجال الدراسة هذا بالاهتمام اللازم، ولا سيما من المنظور العملي. وبناءً على ذلك، يهدف البحث الحالي إلى: تقديم تعريف للذكاء التداولي في سياق الحجاج؛ وتَتَبع الاستراتيجيات (المنطقية والجدلية والبلاغية) المستخدمة لتحقيق الذكاء التداولي؛ ودراسة المعايير التي يتم استيفاؤها عند استخدام مثل هذه الاستراتيجيات في عملية الحجاج؛ وتطوير نموذج تحليلي لتحليل الذكاء التداولي في المثال الذي تم اختياره.       يجب أن يكون لمثل هذا المسعى تأثيره على إثراء الأدبيات بمعلومات عن الذكاء التداولي والطريقة التي يدرك بها الناس ويقيمون ذكاءهم وذكاء الآخرين. يلقي البحث بعض الضوء على المناطق الغامضة التي يجب تحديدها وتوضيحها لأنها تتعارض مع فهم كيف تجذب مواقف الذكاء التداولي القراء والمستمعين، وكيف يمكن لهؤلاء القراء والمستمعين بدورهم معرفة الهدف وراءها. ومن بين الاستنتاجات أن المتحاججين يعتبرون أكثر ذكاءً من الناحية العملية عندما يستخدمون ثلاثة أنواع من الذكاءات وفقًا لهذا البحث: المنطقي والجدلي والبلاغي وهي مكونات لعملية الذكاء التداولي. وتعتبر هذه الأنواع من الذكاء مبهجة (أي انها حققت هدف المتحاججين) عندما تستوفي معايير معينة عند استخدام الاستراتيجيات (المنطقية والجدلية والبلاغية).         This research deals with pragmatic intelligence as used in the context of argumentation and the genre is a two-way kind of communication, say, a political debate. This area of study has not been given its due attention, especially from the pragmatic perspective. Accordingly, the current research aims at: providing a definition for pragmatic intelligence in the context of argumentation; tracing the strategies (logical, dialectical and rhetorical) that are employed to achieve pragmatic intelligence; examining the criteria that are fulfilled when such strategies are used throughout the process of argumentation; and developing an analytical model to analyze pragmatic intelligence in the genre of investigation.         Such an endeavor should have its impact and influence on enriching the literature with information about pragmatic intelligence and the way people pragmatically perceive and evaluate their own and others' intelligence. The research shed some light on fuzzy areas which must be delineated and made clear because they impinge upon understanding how pragmatic intelligence situations appeal themselves to the readers and listeners, and how in turn these readers could find out the impetus behind them. Among the conclusions is that arguers are considered pragmatically more intelligent when they utilize three kinds of intelligences according to this research: logical, dialectical and rhetorical as components of the process of pragmatic intelligence. These kinds of intelligences are considered happy (i.e. achieved the arguers' goal) when they meet certain criteria when the strategies (logical, dialectical and rhetorical) are used

A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Factors That Affect the Ability of Novice Science Teachers to Teach for NOS Understanding

Novice science teachers face a multitude of instructional, curricular, and institutional constraints that affect their classroom decisions. Emphases from their teacher training programs interact with and compete against the realities of professional teaching. This qualitative comparative case study attempts to look at the interactions of novice science teachers with the reform-based practice of nature of science (NOS) instruction. Teacher training programs teach their pre-service science teachers about NOS and try to emphasize the importance of NOS understanding on student scientific literacy, but little is known about how science teachers view and approach NOS once they are free from the constraints of their teacher training programs and are instead faced with the constraints of a real science classroom, administrators, state curricula, and other factors. This study examined seven novice science teachers over one school year in order to investigate how NOS instruction occurred and what factors affected that instruction. Interviews, classroom observations, and questionnaires were utilized in order to create rich descriptions and discussions concerning NOS. Motivational Systems Theory (Ford, 1992) provided a theoretical framework to describe the goal creation, motivation, and goal achievement concerning NOS for each novice science teacher and for cross-case analysis. Findings revealed complex interactions between many factors. Context beliefs concerning mandated state curriculum standards and high-stakes testing proved to have a great effect on NOS goal setting and NOS instruction, but overall positive capability beliefs, context beliefs, and emotional connection to NOS were demonstrated as requirements for appropriate NOS goal setting and motivation. A relationship between viewing NOS as a part of mandated science curriculum standards as opposed to an external institutional goal and increased NOS classroom instruction was noticed. Skill-related factors also affected NOS instruction, though their impact went largely unnoticed by participants. Views of NOS deduced from rubricated leading questions were shown to vary significantly from verbally articulated NOS understandings, suggesting the importance of discussion and explanation in NOS training. The vocalized understandings of NOS presented by the participants, which were often diminished, invented, and conflated, significantly affected the instruction of consensus NOS tenets. Implications and suggestions for further research are described

Innovator, 1974-05

The Innovator was a student newspaper published at Governors State University between March 1972 and October 2000. The newspaper featured student reporting, opinions, news, photos, poetry, and original graphics

Male Students of Color in STEM through the Lens of Intersectionality: A Transformative Mixed-Methods Exploration of Their Science Identities, Relevant Science Learning Experiences, and Decisions to Pursue Science Professions

The purpose of this 3-phase transformative mixed-methods study was to use intersectionality theoretical framework to explore the science identities and relevant science learning experiences of male students of color (MSOCs) in STEM and their decisions to pursue science professions after college. Phase 1 utilized a researcher-developed survey to analyze differences in science identity scores (SIS), science relevancy scores (SRS), and decisions to pursue science professions of 702 diverse college students enrolled in STEM-related courses at a state college in Southeast United States. While there were no statistically significant differences in SIS and SRS scores regarding race/ethnicity or socioeconomic factors, statistical differences in SRS were present regarding gender. Female students had higher SRS than male students. When considering gender and socioeconomic level, a statistically significant interaction occurred across racial/ethnicity groups in SIS and SRS. Black and Hispanic males had higher SIS and SRS when at least one parent had a bachelor\u27s degree. Phase 2 and 3 utilized interviews of five (MSOCs) from which these themes surfaced as largely shaping their decisions to pursue STEM fields: a) future-focus mindsets, b) connectedness to technology, engineering, and math, and c) science experiences and ideas. Students described the teacher\u27s personality, the classroom environment, and the foundational characteristics of science as being critical components of relevant formal science learning experiences. Implications regarding what social justice looks like in the science classroom include1) the need to confirm SIS and SRS construct reliability from this survey instrument with a different population of diverse college students, 2) the important role science teachers and other educational stakeholders play in developing purposeful interactive instruction that adequately connects and prepares male learners for science professions, and 3) the intentional integration of real-world technology, engineering, and mathematics processes and resources in science curriculum and professional development for teachers of science

The Inclusion of Cognitive Complexity: A Content Analysis of New Jersey\u27s Current and Past Intended Curriculum

Policy makers and educators have stated that the internationally benchmarked standards will place greater emphasis on 21st century skills including creativity, collaboration, critical thinking, presentation and demonstration, problem solving, research and inquiry, and career readiness. Many educators believe that if schools are “injected” with creativity, students will have a better chance at a prosperous and productive future. Are the current reform movements thwarting the opportunity for students to “find their own niche” and perhaps turning “them into disciples of ‘intellectual clones’ who will do ‘our thing’ rather their own?” (Sternberg, 2003, p. 335). In response to inquiry, this dissertation sought to examine the cognitive complexity of the nationally adopted Common Core State Standards in Grades 9-12 English Language Arts and Math as compared to the cognitive complexity of the New Jersey Core Curriculum Content Standards in Grades 9-12 English Language Arts and Math using Webb’s Depth of Knowledge framework. My study aimed to reveal the extent to which 21st century skills, such as creativity, critical thinking, strategizing, and problem solving are “infused” into the Common Core State Standards as compared to 21st century skills infused into the New Jersey Core Curriculum Content Standards. Webb’s Depth of Knowledge is directly linked to cognitive complexity, a measure of 21st century skills such as creativity and innovation. The present study employed a qualitative content analysis using Webb’s Depth of Knowledge methodology to code the standards. Deductive category application was used to connect Webb’s existing Depth of Knowledge framework to the existing CCSS and NJCCSS (Mayring, 2000). Each Depth of Knowledge level represents a specific level of cognitive complexity. The higher the DOK level of a standard, the more cognitively complex the standard. The higher the cognitive complexity of a standard, the more creativity and innovation embedded into the standard. Each standard was rated on a 1-4 Depth of Knowledge level based on Webb’s Depth of Knowledge methodology. The method used was a “double-rater read behind consensus model,” which proved to be an effective “reliability check” when coding standards (Miles, Huberman, & Saldaña, 2014, p. 84; Sato, Lagunoff, & Worth, 2011, p. 11). The major findings identified as the 9-12 Grade ELA and Math CCSS were compared to the NJCCCS, using the DOK framework, as follows: 1. When using DOK as an analytic framework, the findings indicate that overall both the Grades 9-12 ELA and Math NJCCCS (2008) were rated at a higher level of cognitive complexity as compared to the Grades 9-12 ELA and Math CCSS (2010). 2. The Grades 9-12 ELA NJCCCS were rated at an overall higher percentage of DOK Levels 3 and 4 than were the Grades 9-12 ELA CCSS. 3. The Grades 9-12 Math NJCCCS were rated at an overall higher percentage of DOK Levels 3 and 4 than were the Grades 9-12 Math CCSS. 4. The Grades 9-12 ELA and Math CCSS had a higher percentage of lower rated standards, DOK Levels 1 and 2, as compared to the Grades 9-12 ELA and Math NJCCCS. This study provides an evidence-based evaluation of the decision of adopting the Common Core State Standards and their effectiveness in preparing students with the academically creative skills necessary to compete in our globally complex 21st century work environment. In addition to contributing to the scant research and literature on creativity in education, policy makers and curriculum writers can use my methodology, as shown in this study, to assess future educational standards and assessments

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe