Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Detecting, quantifying and adjusting for publication bias in meta-analyses: protocol of a systematic review on methods.

BACKGROUND: Health professionals and policymakers aspire to make healthcare decisions based on the entire relevant research evidence. This, however, can rarely be achieved because a considerable amount of research findings are not published, especially in case of 'negative' results - a phenomenon widely recognized as publication bias. Different methods of detecting, quantifying and adjusting for publication bias in meta-analyses have been described in the literature, such as graphical approaches and formal statistical tests to detect publication bias, and statistical approaches to modify effect sizes to adjust a pooled estimate when the presence of publication bias is suspected. An up-to-date systematic review of the existing methods is lacking. METHODS/DESIGN: The objectives of this systematic review are as follows:âeuro¢ To systematically review methodological articles which focus on non-publication of studies and to describe methods of detecting and/or quantifying and/or adjusting for publication bias in meta-analyses.âeuro¢ To appraise strengths and weaknesses of methods, the resources they require, and the conditions under which the method could be used, based on findings of included studies.We will systematically search Web of Science, Medline, and the Cochrane Library for methodological articles that describe at least one method of detecting and/or quantifying and/or adjusting for publication bias in meta-analyses. A dedicated data extraction form is developed and pilot-tested. Working in teams of two, we will independently extract relevant information from each eligible article. As this will be a qualitative systematic review, data reporting will involve a descriptive summary. DISCUSSION: Results are expected to be publicly available in mid 2013. This systematic review together with the results of other systematic reviews of the OPEN project (To Overcome Failure to Publish Negative Findings) will serve as a basis for the development of future policies and guidelines regarding the assessment and handling of publication bias in meta-analyses.

Drosophila rely on learning while foraging under semi-natural conditions

Learning is predicted to affect manifold ecological and evolutionary processes, but the extent to which animals rely on learning in nature remains poorly known, especially for short-lived non-social invertebrates. This is in particular the case for Drosophila, a favourite laboratory system to study molecular mechanisms of learning. Here we tested whether Drosophila melanogaster use learned information to choose food while free-flying in a large greenhouse emulating the natural environment. In a series of experiments flies were first given an opportunity to learn which of two food odours was associated with good versus unpalatable taste; subsequently, their preference for the two odours was assessed with olfactory traps set up in the greenhouse. Flies that had experienced palatable apple-flavoured food and unpalatable orange-flavoured food were more likely to be attracted to the odour of apple than flies with the opposite experience. This was true both when the flies first learned in the laboratory and were then released and recaptured in the greenhouse, and when the learning occurred under free-flying conditions in the greenhouse. Furthermore, flies retained the memory of their experience while exploring the greenhouse overnight in the absence of focal odours, pointing to the involvement of consolidated memory. These results support the notion that even small, short lived insects which are not central-place foragers make use of learned cues in their natural environments.

Altered processing of contextual information during fear extinction in PTSD: an fMRI study.

Medial prefrontal cortical areas have been hypothesized to underlie altered contextual processing in posttraumatic stress disorder (PTSD). We investigated brain signaling of contextual information in this disorder. Eighteen PTSD subjects and 16 healthy trauma-exposed subjects underwent a two-day fear conditioning and extinction paradigm. On day 1, within visual context A, a conditioned stimulus (CS) was followed 60% of the time by an electric shock (conditioning). The conditioned response was then extinguished (extinction learning) in context B. On day 2, recall of the extinction memory was tested in context B. Skin conductance response (SCR) and functional magnetic resonance imaging (fMRI) data were collected during context presentations. There were no SCR group differences in any context presentation. Concerning fMRI data, during late conditioning, when context A signaled danger, PTSD subjects showed dorsal anterior cingulate cortical (dACC) hyperactivation. During early extinction, when context B had not yet fully acquired signal value for safety, PTSD subjects still showed dACC hyperactivation. During late extinction, when context B had come to signal safety, they showed ventromedial prefrontal cortex (vmPFC) hypoactivation. During early extinction recall, when context B signaled safety, they showed both vmPFC hypoactivation and dACC hyperactivation. These findings suggest that PTSD subjects show alterations in the processing of contextual information related to danger and safety. This impairment is manifest even prior to a physiologically-measured, cue-elicited fear response, and characterized by hypoactivation in vmPFC and hyperactivation in dACC.

M-Learning + Facebook: experiencias y percepciones de estudiantes de diseño

Durante los últimos años, diversas instituciones y universidades han comenzado a experimentar con el m-learning y Facebook a través de diferentes proyectos como parte de sus metodologías de aprendizaje y como una oportunidad para trabajar con los jóvenes. Sin embargo, poco se sabe de las percepciones y experiencias que pueden obtener estudiantes de diseño sobre este tema. En este estudio 24 estudian - tes han completado sus actividades de aprendizaje durante dos meses, utilizando un smarthphone y la popular red social Facebook. Al final del plazo, los estudiantes participaron además en un grupo de discusión para expresar sus experiencias. Los resultados indicaron que los estudiantes utilizaron Facebook como parte de su rutina diaria y que fueron creadores de contenido proporcionando estos a otros. Además los resultados indican que durante el primer mes perdieron mucho tiempo observando contenidos propuestos en Facebook, que después comentaron. El grupo en Facebook fue utilizado para la interacción social principalmente con otros estudiantes y el profesor, como un complemento a las sesiones presenciales. Los resultados obtenidos y el empleo de estrategias, puede ayudar a la concep - tualización del m-learning y mostrar como Facebook puede funcionar como un entorno de aprendizaje para apoyar la enseñanza y aprendizaje en el área del diseño.

Is the Front Line Prepared for the Changing Faces of Patients? Predictors of Cross-Cultural Preparedness Among Clinical Nurses and Resident Physicians in Lausanne, Switzerland.

UNLABELLED: Phenomenon: Assuring quality medical care for all persons requires that healthcare providers understand how sociocultural factors affect a patient's health beliefs/behaviors. Switzerland's changing demographics highlight the importance of provider cross-cultural preparedness for all patients-especially those at risk for social/health precarity. We evaluated healthcare provider cross-cultural preparedness for commonly encountered vulnerable patient profiles. APPROACH: A survey on cross-cultural care was mailed to Lausanne University hospital's "front-line healthcare providers": clinical nurses and resident physicians at our institution. Preparedness items asked "How prepared do you feel to care for ... ?" (referring to example patient profiles) on an ascending 5-point Likert scale. We examined proportions of "4 - well/5 - very well prepared" and the mean composite score for preparedness. We used linear regression to examine the adjusted effect of demographics, work context, cultural-competence training, and cross-cultural care problem awareness, on preparedness. FINDINGS: Of 885 questionnaires, 368 (41.2%) were returned: 124 (33.6%) physicians and 244 (66.4%) nurses. Mean preparedness composite was 3.30 (SD = 0.70), with the lowest proportion of healthcare providers feeling prepared for patients "whose religious beliefs affect treatment" (22%). After adjustment, working in a sensitized department (β = 0.21, p = .01), training on the history/culture of a specific group (β = 0.25, p = .03), and awareness regarding (a) a lack of practical experience caring for diverse populations (β = 0.25, p = .004) and (b) inadequate cross-cultural training (β = 0.18, p = .04) were associated with higher preparedness. Speaking French as a dominant language and physician role (vs. nurse) were negatively associated with preparedness (β = -0.26, p = .01; β = -0.22, p = .01). Insights: The state of cross-cultural care preparedness among Lausanne's front-line healthcare providers leaves room for improvement. Our study points toward institutional strategies to improve preparedness: notably, making sure departments are sensitized to cross-cultural care resources and increasing provider diversity to reflect the changing Swiss demographic.

Desarrollo de la materia Proyectos. Grado de Comunicación Audiovisual. Universitat de Barcelona

El objetivo del presente trabajo es presentar la propuesta de desarrollo de la materia Proyectos del Grado de Comunicación Audiovisual de la Universidad de Barcelona, que incluye las asignaturas Proyectos I y Proyectos II. Ambas asignaturas son especialmente idóneas para trabajar las competencias transversales del grado, dado que el objetivo de la materia a la que pertenecen es integrar las competencias adquiridas en el conjunto de asignaturas cursadas por los alumnos hasta este momento, poniendo en relación los diferente lenguajes (escrito, oral, audiovisual y multimedia). Todo esto permite que el estudiante adquiera una visión integral y transversal. El presente trabajo reflexiona sobre los mecanismos que permitan al profesorado diseñar de forma colaborativa pautas y estrategias de enseñanza-aprendizaje; los modos de evaluación de las competencias de estas asignaturas, y todos aquellos aspectos claves que deben recoger los planes docentes. Materias como la de Proyectos suponen un reto en la actividad docente, al requerir del trabajo interdisciplinar e integrador de las áreas de conocimiento implicadas y de los docentes vinculados, al tiempo que facilitan la generación de puentes entre el ámbito académico y profesional.

Facilitating coordination improvement efforts in cross-functional process development programs

Business process improvement is a common approach in increasing the effectiveness of an organization. It can be seen as an effort to increase coordination between units. Process improvement has proved to be challenging, and most management consultation firms facilitate organizations in this kind of initiatives. Cross-functional improvement is one of the main areas for internal consultants as well. However, the needs, challenges and means of cross-functional help have been rarely discussed in the literature. The objective of this thesis is on one hand to present a conceptual and descriptive framework to help understand the challenges of facilitating coordination improvement efforts in cross-functional improvement programs, and on the other hand to develop and test feasible solutions for some facilitation situations. The research questions are: 1. Why and in what kind of situations do organizations need help in developing coordination in cross-functional processes? 2. How can a facilitator help organizations in improving coordination to develop cross-functional processes? The study consists of two parts. The first part is an overview of the dissertation, and the second part comprises six research publications. The theoretical background for the study are the differentiation causing challenges in cross-functional settings, the coordination needed to improve processes, change management principles, methods and tools, and consultation practises. Three of the publications introduce tools for helping in developing prerequisites, planning responsibilities and supporting learning during the cross-functional program. The three other papers present frameworks to help understand and analyse the improvement situation. The main methodological approaches used in this study are design science research, action research and case research. The research data has been collected from ten cases representing different kinds of organizations, processes and developing situations. The data has been collected mainly by observation, semi-structured interviews and questionnaires. The research contributes to the rare literature combining coordination theories and process improvement practises. It also provides additional understanding of a holistic point of view in process improvement situations. The most important contribution is the addition to the theories of facilitating change in process improvement situations. From the managerial point of view, this study gives advice to managers and consultants in planning and executing cross-functional programs. The main factors increasing the need for facilitation are the challenges for differentiation, challenges of organizational change in general, and the novelty of initiatives and improvement practices concerning process development. Organizations need help in creating the prerequisites to change, in planning initiatives, easing conflict management and collaboration between groups, as well as supporting the learning of cross-functional improvement. The main challenges of facilitation are combining the different roles as a consultant, maintaining the ownership for the improvement project with the client, and supporting learning in the client organization.

L'informatique: un outil remarquable pour l'enseignement de la médecine interne générale [The informatics: a remarkable tool for teaching general internal medicine].

INTERMED training implies a three week course, integrated in the "primary care module" for medical students in the first master year at the school of medicine in Lausanne. INTERMED uses an innovative teaching method based on repetitive sequences of e-learning-based individual learning followed by collaborative learning activities in teams, named Team-based learning (TBL). The e-learning takes place in a web-based virtual learning environment using a series of interactive multimedia virtual patients. By using INTERMED students go through a complete medical encounter applying clinical reasoning and choosing the diagnostic and therapeutic approach. INTERMED offers an authentic experience in an engaging and safe environment where errors are allowed and without consequences.

Stratégies de communication au service de la formation: quelques outils pratiques [Effective communication strategies to frame the trainer-trainee dialogue in the clinical setting].

Communication between trainer and trainee plays a central role in teaching and learning in the clinical environment. There are various strategies to frame the dialogue between trainee and trainer. These strategies allow trainers to be more effective in their supervision, which is important in our busy clinical environment. Communication strategies are well adapted to both in- and out-patient settings, to both under- and postgraduate contexts. This article presents three strategies that we think are particularly useful. They are meant to give feedback, to ask questions and to present a case.

The FaceUOC Project : An Open Social Learning experience by the Open University of Catalonia

The possibilities and expansion of the use of Web 2.0 has opened up a world of possibilities in online learning. In spite of the integration of these tools in education major changes are required in the educational design of instructional processes.This paper presents an educational experience conducted by the Open University of Catalonia using the social network Facebook for the purpose of testing a learning model that uses a participation and collaboration methodology among users based on the use of open educational resources.- The aim of the experience is to test an Open Social Learning (OSL) model, understood to be a virtual learning environment open to the Internet community, based on the use of open resources and on a methodology focused on the participation and collaboration of users in the construction of knowledge.- The topic chosen for this experience in Facebook was 2.0 Journeys: online tools and resources. The objective of this 5 weeks course was to provide students with resources for managing the various textual, photographic, audiovisual and multimedia materials resulting from a journey.- The most important changes in the design and development of a course based on OSL are the role of the teacher, the role of the student, the type of content and the methodology:- The teacher mixes with the participants, guiding them and offering the benefit of his/her experience and knowledge.- Students learn through their participation and collaboration with a mixed group of users.- The content is open and editable under different types of license that specify the level of accessibility.- The methodology of the course was based on the creation of a learning community able to self-manage its learning process. For this a facilitator was needed and also a central activity was established for people to participate and contribute in the community.- We used an ethnographic methodology and also questionnaires to students in order to acquire results regarding the quality of this type of learning experience.- Some of the data obtained raised questions to consider for future designs of educational situations based on OSL:- Difficulties in breaking the facilitator-centred structure- Change in the time required to adapt to the system and to achieve the objectives- Lack of commitment with free courses- The trend to return to traditional ways of learning- Accreditation- This experience has taught all of us that education can happen any time and in any place but not in any way.

The Transformation of Practical Teaching Focusing on Dialogic Learning

This study investigates the transformation of practical teaching in a Catalan school, connected to the design, implementation and development of project-based learning, and focusing on dialogic learning to investigate its limits and possibilities. Qualitative and design-based research (DBR) methods are applied. These methods are based on empirical educational research with the theory-driven of learning environments. DBR is proposed and applied using practical guidance for the teachers of the school. It can be associated with the current proposals for Embedding Social Sciences and Humanities in the Horizon 2020 Societal Challenges. This position statement defends the social sciences and the humanities as the most fundamental and important ideas to face all societal challenges. The results of this study show that before the training process, teachers apply dialogic learning in specific moments (for example, when they speak about the weekend); however, during the process and after the process, they work systematically with dialogic learning through the PEPT: they start and finish every activity with a individual and group reflection about their own processes, favouring motivation, reasoning and the implication of all the participants. These results prove that progressive transformations of teaching practice benefit cooperative work in class

Blocking and unblocking in a navigation task

Rodrigo, Chamizo, McLaren, & Mackintosh (1997) demonstrated the blocking effect in a navigational task using a swimming pool: rats initially trained to use three landmarks (ABC) to find an invisible platform learned less about a fourth landmark (X) added later than did rats trained from the outset with these four landmarks (ABCX). The aim of the experiment reported here was to demonstrate unblocking using a similar procedure as in the previous work. Three groups of rats were initially trained to find an invisible platfom in the presence of three landmarks: ABC for the Blocking and Unblocking groups and LMN for the Control group. Then, all animals were trained to find the platform in the presence of four landmarks, ABCX. In this second training, unlike animals in the Blocking group to which only a new landmark (X) was added in comparison to the first training, the animals in the Unblocking group also had a change in the platform position. In the Control group, both the four landmarks and the platform position were totally new at the beginning of this second training. As in Rodrigo et al. (1997) a blocking effect was found: rats in the Blocking group learned less with respect to the added landmark (X) than did animals in the Control group. However, rats in the Unblocking group learned about the added landmark (X) as well as did animals in the Control group. The results are interpreted as an unblocking effect due to a change in the platform position between the two phases of training, similarly to what is normal in classical conditioning experiments, in which a change in the conditions of reinforcement between the two training phases of a blocking design produce an attenuation or elimination of this effect. These results are explained within an error-correcting connectionist account of spatial navigation (McLaren, 2002).

Impacto de la interactividad de un entorno virtual, en el fomento del aprendizaje colaborativo en la Universidad Santiago de Cali

En este estudio se analiza el impacto de la interactividad de un entorno virtual, el fomento del aprendizaje colaborativo de un grupo conformado por diez y seis (N=16) estudiantes del segundo semestre del programa de Ingeniería Industrial de la universidad Santiago de Cali. Se planteó como objetivo, establecer un foro virtual con una actividad de aprendizaje colaborativo, basada en un problema a resolver como tarea conjunta.La experiencia se desarrolló mediante un diseño de tipo exploratorio y descriptivo, enfocado en recolectar y analizar información que permitió comprender y valorar el impacto de la interactividad en el aprendizaje colaborativo de los estudiantes. Con el estudio, se apertura un espacio de discusión en torno al impacto real de las interacciones en el fomento del aprendizaje, durante la realización de tareas colaborativas de los estudiantes, utilizando el foro virtual como herramienta de apoyo.