Redes Bayesianas para detección de roles de equipos en aprendizaje colaborativo soportado por computadoras

El trabajo colaborativo soportado por computadoras permite a los estudiantes que se encuentran en lugares remotos trabajar de manera conjunta en el mismo entorno virtual y permite la comunicación de ideas e información entre los integrantes del grupo. Sin embargo, como no todos los estudiantes son iguales, es importante estudiar las características de éstos para construir grupos de trabajo más productivos. La teoría de roles de equipo posibilita obtener buen desempeño en los equipos de trabajo considerando habilidades individuales, combinando las falencias de cada rol con las fortalezas de los otros. Generalmente, las personas tienen que completar extensos cuestionarios para poder determinar sus roles de equipo. En este trabajo, se propone un método alternativo para realizar esta detección a través de un sistema de aprendizaje colaborativo y a partir de la utilización de la técnica de Redes Bayesianas.Computer-supported collaborative learning allows students who are in different places to work together in the same virtual space, and supports the communication of ideas and information among learners. However, as not all students are identical, it is important to study users' characteristics to build more productive teams. Team Roles Theory allows obtaining very good team performance taking into account individual skills, combining the weaknesses of each role with the strengths of others. Originally, people have to complete extensive questionnaires to determine their team role. In this work we propose an alternative method to make this detection through a collaborative learning system and by using a Bayesian Network.Fil: Balmaceda, José María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Designing electronic collaborative learning environments

Electronic collaborative learning environments for learning and working are in vogue. Designers design them according to their own constructivist interpretations of what collaborative learning is and what it should achieve. Educators employ them with different educational approaches and in diverse situations to achieve different ends. Students use them, sometimes very enthusiastically, but often in a perfunctory way. Finally, researchers study them and—as is usually the case when apples and oranges are compared—find no conclusive evidence as to whether or not they work, where they do or do not work, when they do or do not work and, most importantly, why, they do or do not work. This contribution presents an affordance framework for such collaborative learning environments; an interaction design procedure for designing, developing, and implementing them; and an educational affordance approach to the use of tasks in those environments. It also presents the results of three projects dealing with these three issues

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Learning 21st century science in context with mobile technologies

The paper describes a project to support personal inquiry learning with handheld and desktop technology between formal and informal settings. It presents a trial of the technology and learning across a school classroom, sports hall, and library. The main aim of the study was to incorporate inquiry learning activities within an extended school science environment in order to investigate opportunities for technological mediations and to extract initial recommendations for the design of mobile technology to link inquiry learning across different contexts. A critical incident analysis was carried out to identify learning breakdowns and breakthroughs that led to design implications. The main findings are the opportunities that a combination of mobile and fixed technology bring to: manage the formation of groups, display live visualisations of student and teacher data on a shared screen to facilitate motivation and personal relevance, incorporate broader technical support, provide context-specific guidance on the sequence, reasons and aims of learning activities, offer opportunities to micro-sites for reflection and learning in the field, to explicitly support appropriation of data within inquiry and show the relation between specific activities and the general inquiry process

Group Leadership in Online Collaborative Learning

The innovations in computer and communications technologies combined with on-going needs to deliver educational programs to students regardless of their physical locations, have lead to the innovation of distance education programs and technologies. To keep up with recent developments in both areas of technologies and techniques related to distance education programs, educators, administrators, and researchers are in constant needs of learning about issues, solutions, and challenges of these technologies. The Encyclopedia of Distance Learning offers the most comprehensive coverage of the issues, concepts, trends, and technologies of distance learning. With more than 2,000 terms and definitions and over 6,000 additional references, this authoritative 4-volume encyclopedia is considered the foremost reference source for the latest understanding, discoveries, and research in the field of distance learning

A Survey on Studying the Social Networks of Students

Do studies show that physical and online students' social networks support education? Analyzing interactions between students in schools and universities can provide a wealth of information. Studies on students' social networks can help us understand their behavioral dynamics, the correlation between their friendships and academic performance, community and group formation, information diffusion, and so on. Educational goals and holistic development of students with various academic abilities and backgrounds can be achieved by incorporating the findings attained by the studies in terms of knowledge propagation in classroom and spread of delinquent behaviors. Moreover, we use Social Network Analysis (SNA) to identify isolated students, ascertain the group study culture, analyze the spreading of various habits like smoking, drinking, and so on. In this paper, we present a review of the research showing how analysis of students' social networks can help us identify how improved educational methods can be used to make learning more inclusive at both school and university levels and achieve holistic development of students through expansion of their social networks, as well as control the spread of delinquent behaviors.Comment: Huso 201

Interaction in computer supported collaborative learning: an analysis of the implementation phase

There is extensive research on interaction frameworks in distance education and studies in Computer Supported Collaborative Learning (CSCL) have also focused on establishing interaction models. There is still research to be done, though, in order to identify the elements that configure interaction to build up a framework for their integration, aligned with the learning goals. The purpose of this study is to understand the key elements that configure effective interaction in the implementation phase of CSCL and to analyze the different types of interactions that occur during collaborative learning processes. The study was designed under a nonexperimental quantitative methodology and 106 learners answered a questionnaire after participating in 5 different higher education subjects implementing CSCL. A factorial analysis of results prove that students identify three types of interaction to be necessary during the implementation phase of collaboration in order to reach knowledge convergence: cognitive, social and organizational interaction. Therefore, instructors and institutions who wish to promote effective CSCL should bear in mind the learning goals together with the social and organizational aspects interwoven in the design, implementation and assessment phases of collaborative learning.This paper has been written in the context of the research project: “Ecologías de aprendizaje en la era digital: nuevas oportunidades para la formación del profesorado de educación secundaria” (ECO4LEARN-SE) “Learning ecologies in the digital era: new opportunities for teacher training in secondary education”. It has been partially financed by the Ministry of Science, Innovation and Universities in Spain, (Ref. RTI2018–095690-B-I00)S

The mechanics of CSCL macro scripts

Macro scripts structure collaborative learning and foster the emergence of knowledge-productive interactions such as argumentation, explanations and mutual regulation. We propose a pedagogical model for the designing of scripts and illustrate this model using three scripts. In brief, a script disturbs the natural convergence of a team and in doing so increases the intensity of interaction required between team members for the completion of their collaborative task. The nature of the perturbation determines the types of interactions that are necessary for overcoming it: for instance, if a script provides students with conflicting evidence, more argumentation is required before students can reach an agreement. Tools for authoring scripts manipulate abstract representations of the script components and the mechanisms that relate components to one another. These mechanisms are encompassed in the transformation of data structures (social structure, resources structure and products structure) between script phases. We describe how this pedagogical design model is translated into computational structures in three illustrated script