Introduction to Smart Learning Analytics: Foundations and Developments in Video-Based Learning

Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Teaching with Technology in the Modern Classroom: A Learning Systems Model

Rapid advances in technology, an expanding non-traditional student body, and paradigm shifts are profoundly changing education. With Federal initiatives targeting ways to help students, educators feel the pressure to do more teaching with technology. However, educators may tend to adapt a piecemeal approach, without recognizing the wider implications for education as a total system. The Learning Systems Model expands and tailors a process model for teaching that identifies the interrelated components of education at the levels of individual, institution, and the wider society. In particular, elements within the model emphasize the needs of a multicultural and diverse student body, as well as the implications of utilizing technology as a tool in education. The discussion ends with specific teaching skills and techniques to help the educator adapt to the modern classroom

Aeronautical engineering: A continuing bibliography with indexes (supplement 322)

This bibliography lists 719 reports, articles, and other documents introduced into the NASA scientific and technical information system in Oct. 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Aplicación del modelo de diagnóstico de aprendizaje ECER en un curso universitario de bases de datos

1 CD-ROM : il.La sistematización de procesos educativos complejos que beneficien tanto al docente como al estudiante, puede considerarse como uno de los aportes de las NTICs a la educación. Es el caso de los sistemas de diagnóstico de aprendizaje, que permiten entregar reportes de estado de logro de objetivos de aprendizaje a los estudiantes de manera individualizada, facilitando el proceso de seguimiento por parte del docente, labor que llevada a cabo en forma manual sería muy dispendiosa. Uno de los modelos que permitió el desarrollo de un sistema de este tipo es el modelo ECER (Enhanced Concept Effect Relationship), el cual hasta el momento ha sido validado en la república de Taiwán, lugar de origen del modelo, en escuelas de básica primaria y secundaria. El objetivo de este proyecto fue contextualizar y validar el modelo de diagnóstico ECER en el contexto colombiano, y observar los aportes de sus reportes en el proceso de aprendizaje de estudiantes de un curso universitario de Bases de Datos. Este informe da cuenta de las etapas en que se desarrolló el proyecto, la aplicación y resultados del modelo ECER a través del software que lo implementa en un curso de Bases de Datos dirigido a estudiantes universitarios, y plantea las conclusiones sobre la factibilidad de uso de dicho software a nivel universitario.Abstract: The rapid progress in the New Technologies of Information and Communications has influenced multiple disciplines to benefit from this situation. This applies to education. The systematization of complex educational processes that benefit both the teacher and student is a contribution of ICTs to education. This is the case of learning diagnostic systems, which allows each student to receive their current status learning, work carried out by the teacher in manually form would be very wasteful. One model that allowed the development of a system of this type is the ECER model (Enhanced Concept Effect Relationship), which so far has been validated in the Republic of Taiwan, birthplace of the model, at basic primary and secondary schools. The objective of this project is to implement and validate the model ECER in an university scope in order to extend the context of it. This project describes the implementation of ECER model, through the software that implements it, in a Database course at technological level. This course is located on the fourth level of the technology and sought to establish whether it is feasible to use this model at the university level.Introducción -- 1. Planteamiento del problema -- 2. Estado del arte -- 3. Preguntas y objetivos de investigación -- 4. Marco de referencia -- 5. Experimentación -- Conclusiones finales -- 7. Referencias -- Anexos: 1. Formulario de pesos diligenciados -- 2. Ejemplo de diagnósticos de aprendizaje generados -- Indice de tablas -- Indice de gráfica