108 research outputs found
Multi-scale Pedestrian Navigation and Movement in Urban Areas
Sustainable transport planning highlights the importance of walking to low-carbon
and healthy urban transport systems. Studies have identified multiple ways in which
vehicle traffic can negatively impact pedestrians and inhibit walking intentions.
However, pedestrian-vehicle interactions are underrepresented in models of pedestrian
mobility. This omission limits the ability of transport simulations to support
pedestrian-centric street design. Pedestrian navigation decisions take place simultaneously
at multiple spatial scales. Yet most models of pedestrian behaviour focus
either on local physical interactions or optimisation of routes across a road network.
This thesis presents a novel hierarchical pedestrian route choice framework that
integrates dynamic, perceptual decisions at the street level with abstract, network
based decisions at the neighbourhood level. The framework is based on Construal
Level Theory which states that decision makers construe decisions based on their
psychological distance from the object of the decision. The route choice framework
is implemented in a spatial agent-based simulation in which pedestrian and vehicle
agents complete trips in an urban environment. Global sensitivity analysis is used to
explore the behaviour produced by the multi-scale pedestrian route choice model.
Finally, simulation experiments are used to explore the impacts of restrictions to
pedestrian movement. The results demonstrate the potential insights that can be
gained by linking street scale movement and interactions with neighbourhood level
mobility patterns
An uncertainty prediction approach for active learning - application to earth observation
Mapping land cover and land usage dynamics are crucial in remote sensing since farmers
are encouraged to either intensify or extend crop use due to the ongoing rise in the world’s
population. A major issue in this area is interpreting and classifying a scene captured in
high-resolution satellite imagery. Several methods have been put forth, including neural
networks which generate data-dependent models (i.e. model is biased toward data) and
static rule-based approaches with thresholds which are limited in terms of diversity(i.e.
model lacks diversity in terms of rules). However, the problem of having a machine learning
model that, given a large amount of training data, can classify multiple classes over different
geographic Sentinel-2 imagery that out scales existing approaches remains open.
On the other hand, supervised machine learning has evolved into an essential part of many
areas due to the increasing number of labeled datasets. Examples include creating classifiers
for applications that recognize images and voices, anticipate traffic, propose products, act
as a virtual personal assistant and detect online fraud, among many more. Since these
classifiers are highly dependent from the training datasets, without human interaction or
accurate labels, the performance of these generated classifiers with unseen observations
is uncertain. Thus, researchers attempted to evaluate a number of independent models
using a statistical distance. However, the problem of, given a train-test split and classifiers
modeled over the train set, identifying a prediction error using the relation between train
and test sets remains open.
Moreover, while some training data is essential for supervised machine learning, what
happens if there is insufficient labeled data? After all, assigning labels to unlabeled datasets
is a time-consuming process that may need significant expert human involvement. When
there aren’t enough expert manual labels accessible for the vast amount of openly available
data, active learning becomes crucial. However, given a large amount of training and
unlabeled datasets, having an active learning model that can reduce the training cost of
the classifier and at the same time assist in labeling new data points remains an open
problem.
From the experimental approaches and findings, the main research contributions, which
concentrate on the issue of optical satellite image scene classification include: building
labeled Sentinel-2 datasets with surface reflectance values; proposal of machine learning
models for pixel-based image scene classification; proposal of a statistical distance based
Evidence Function Model (EFM) to detect ML models misclassification; and proposal of
a generalised sampling approach for active learning that, together with the EFM enables
a way of determining the most informative examples.
Firstly, using a manually annotated Sentinel-2 dataset, Machine Learning (ML) models
for scene classification were developed and their performance was compared to Sen2Cor the reference package from the European Space Agency – a micro-F1 value of 84%
was attained by the ML model, which is a significant improvement over the corresponding
Sen2Cor performance of 59%. Secondly, to quantify the misclassification of the ML models,
the Mahalanobis distance-based EFM was devised. This model achieved, for the labeled
Sentinel-2 dataset, a micro-F1 of 67.89% for misclassification detection. Lastly, EFM was
engineered as a sampling strategy for active learning leading to an approach that attains
the same level of accuracy with only 0.02% of the total training samples when compared
to a classifier trained with the full training set.
With the help of the above-mentioned research contributions, we were able to provide
an open-source Sentinel-2 image scene classification package which consists of ready-touse
Python scripts and a ML model that classifies Sentinel-2 L1C images generating a
20m-resolution RGB image with the six studied classes (Cloud, Cirrus, Shadow, Snow,
Water, and Other) giving academics a straightforward method for rapidly and effectively
classifying Sentinel-2 scene images. Additionally, an active learning approach that uses, as
sampling strategy, the observed prediction uncertainty given by EFM, will allow labeling
only the most informative points to be used as input to build classifiers; Sumário:
Uma Abordagem de Previsão de Incerteza para
Aprendizagem Ativa – Aplicação à Observação da Terra
O mapeamento da cobertura do solo e a dinâmica da utilização do solo são cruciais na
deteção remota uma vez que os agricultores são incentivados a intensificar ou estender as
culturas devido ao aumento contínuo da população mundial. Uma questão importante
nesta área é interpretar e classificar cenas capturadas em imagens de satélite de alta resolução.
Várias aproximações têm sido propostas incluindo a utilização de redes neuronais
que produzem modelos dependentes dos dados (ou seja, o modelo é tendencioso em relação
aos dados) e aproximações baseadas em regras que apresentam restrições de diversidade
(ou seja, o modelo carece de diversidade em termos de regras). No entanto, a criação de
um modelo de aprendizagem automática que, dada uma uma grande quantidade de dados
de treino, é capaz de classificar, com desempenho superior, as imagens do Sentinel-2 em
diferentes áreas geográficas permanece um problema em aberto.
Por outro lado, têm sido utilizadas técnicas de aprendizagem supervisionada na resolução
de problemas nas mais diversas áreas de devido à proliferação de conjuntos de dados etiquetados.
Exemplos disto incluem classificadores para aplicações que reconhecem imagem
e voz, antecipam tráfego, propõem produtos, atuam como assistentes pessoais virtuais e
detetam fraudes online, entre muitos outros. Uma vez que estes classificadores são fortemente
dependente do conjunto de dados de treino, sem interação humana ou etiquetas
precisas, o seu desempenho sobre novos dados é incerta. Neste sentido existem propostas
para avaliar modelos independentes usando uma distância estatística. No entanto, o problema
de, dada uma divisão de treino-teste e um classificador, identificar o erro de previsão
usando a relação entre aqueles conjuntos, permanece aberto.
Mais ainda, embora alguns dados de treino sejam essenciais para a aprendizagem supervisionada,
o que acontece quando a quantidade de dados etiquetados é insuficiente? Afinal,
atribuir etiquetas é um processo demorado e que exige perícia, o que se traduz num envolvimento
humano significativo. Quando a quantidade de dados etiquetados manualmente por
peritos é insuficiente a aprendizagem ativa torna-se crucial. No entanto, dada uma grande
quantidade dados de treino não etiquetados, ter um modelo de aprendizagem ativa que
reduz o custo de treino do classificador e, ao mesmo tempo, auxilia a etiquetagem de novas
observações permanece um problema em aberto.
A partir das abordagens e estudos experimentais, as principais contribuições deste trabalho,
que se concentra na classificação de cenas de imagens de satélite óptico incluem:
criação de conjuntos de dados Sentinel-2 etiquetados, com valores de refletância de superfície;
proposta de modelos de aprendizagem automática baseados em pixels para classificação de cenas de imagens de satétite; proposta de um Modelo de Função de Evidência (EFM)
baseado numa distância estatística para detetar erros de classificação de modelos de aprendizagem;
e proposta de uma abordagem de amostragem generalizada para aprendizagem
ativa que, em conjunto com o EFM, possibilita uma forma de determinar os exemplos mais
informativos.
Em primeiro lugar, usando um conjunto de dados Sentinel-2 etiquetado manualmente,
foram desenvolvidos modelos de Aprendizagem Automática (AA) para classificação de cenas
e seu desempenho foi comparado com o do Sen2Cor – o produto de referência da
Agência Espacial Europeia – tendo sido alcançado um valor de micro-F1 de 84% pelo classificador,
o que representa uma melhoria significativa em relação ao desempenho Sen2Cor
correspondente, de 59%. Em segundo lugar, para quantificar o erro de classificação dos
modelos de AA, foi concebido o Modelo de Função de Evidência baseado na distância de
Mahalanobis. Este modelo conseguiu, para o conjunto de dados etiquetado do Sentinel-2
um micro-F1 de 67,89% na deteção de classificação incorreta. Por fim, o EFM foi utilizado
como uma estratégia de amostragem para a aprendizagem ativa, uma abordagem
que permitiu atingir o mesmo nível de desempenho com apenas 0,02% do total de exemplos
de treino quando comparado com um classificador treinado com o conjunto de treino
completo.
Com a ajuda das contribuições acima mencionadas, foi possível desenvolver um pacote
de código aberto para classificação de cenas de imagens Sentinel-2 que, utilizando num
conjunto de scripts Python, um modelo de classificação, e uma imagem Sentinel-2 L1C,
gera a imagem RGB correspondente (com resolução de 20m) com as seis classes estudadas
(Cloud, Cirrus, Shadow, Snow, Water e Other), disponibilizando à academia um método
direto para a classificação de cenas de imagens do Sentinel-2 rápida e eficaz. Além disso, a
abordagem de aprendizagem ativa que usa, como estratégia de amostragem, a deteção de
classificacão incorreta dada pelo EFM, permite etiquetar apenas os pontos mais informativos
a serem usados como entrada na construção de classificadores
Exploring cyclists’ and pedestrians’ personal exposure, wellbeing and protective practices on-the-move
In dieser Doktorarbeit wurde untersucht, welche Faktoren Wohlbefinden, wahrgenommene Gesundheit und Mobilitätspraktiken von Radfahrenden und Fußgänger:innen während des Unterwegsseins beeinflussen. Ziel war es, die persönliche Exposition gegenüber Feinstaub und Lärm unterwegs zu messen und diese der individuell wahrgenommenen Belastung gegenüberzustellen. Zudem wurden weitere Faktoren, die das Wohlbefinden beeinflussen, untersucht. Die Arbeit beleuchtet überdies, wie über gesunde und angenehme Mobilität informiert werden könnte. Zuerst wurden mobile qualitative Interviews (Go-/Ride-Alongs) durchgeführt und mit tragbaren Sensoren zur Messung von Feinstaub und Lärm ergänzt. Der situative Kontext, die sensorische Wahrnehmung und soziale Aspekte beeinflussen, ob das Unterwegsseins in der Stadt als gesund und angenehm empfunden wird. Diese Faktoren können in vergleichsweise als hoch belastend gemessenen Situationen ausgleichend wirken. Weiterhin wurden Informationsmöglichkeiten für eine gesunde Mobilität in der Stadt exploriert. Ein Literaturreview hat aufgezeigt, dass Gesundheitsthemen wenig Berücksichtigung in Forschung zu Mobilitäts-Apps finden. Daran anschließend wurden Fokusgruppen durchgeführt. Es wurde ermittelt, wie gesunde und angenehme Routen kommuniziert werden können. Hier könnendas Vorhandensein von Routenalternativen und Bewältigungsstrategien ein Gefühl von Selbstwirksamkeit geben. Es wurde eine „pleasant routing app“ vorgeschlagen, die angenehme und gesunde Routenaspekte integriert. Um die Attraktivität des Fahrradfahrens und zu Fuß Gehens zu steigern, sollten Erfahrungen, Wahrnehmungen und Praktiken von Radfahrenden und Fußgänger:innen berücksichtigt werden. Letztendlich kann somit aktive Mobilität ihr Potenzial entfalten und zu einer lebenswerten, gesunden und umweltfreundlichen Stadt beitragen.This thesis investigates factors influencing cyclists’ and pedestrians’ health and wellbeing on-the-move. Moreover, the possibilities of smartphone apps for supporting a healthy and pleasant trip are investigated. The scope of this thesis is to combine the topic healthy and pleasant mobility with possibilities of mobility apps. First, the thesis explores how cyclists and pedestrians perceive their personal exposure towards air pollution and noise as well as other factors influencing commuting experience and wellbeing on-the-move. This is contrasted to actual measured particulate matter and noise. Qualitative interviews on-the-move (‘go-/ride-alongs’) are complemented by wearable sensors measuring particulate matter and noise. The results show discrepancies as well as coherences between perceived and measured exposure. The situational context, sensory awareness (e.g. water views) and social cues (e.g. seeing other people) are important for a perceived pleasant commute, even in polluted areas. Second, this thesis identifies how far health impacting factors are considered in research using mobility apps to identify their possibilities for supporting a healthy commute. A literature review reveals that research applying mobility apps is lacking the consideration of health topics and it is proposed to integrate health topics in mobility app development. Following these findings, the thesis investigates communication options to inform about a healthy and pleasant commute. Focus groups were applied showing that information should include feasible coping strategies and increase self-efficacy. Pleasant trip characteristics could be included in a healthy mobility app. If active mode users’ experiences, perceptions and practices are considered, cycling and walking can become more attractive and more people are encouraged to cycle or walk. Hence, active modes can unfold their potential for supporting the transformation towards liveable, healthy and environmentally friendly cities
Wissen ordnen und entgrenzen – vom analogen zum digitalen Europa?
The edited volume "Ordering and delimiting knowledge – from analogue to digital Europe?" asks how knowledge orders confirm, reinforce, question or create new social differentiations, and to what extent the digital transformation changes such differentiation processes gradually or in principle. Knowledge orders are understood here as intentionally construed and medially mediated orders that delimit, systematise, classify and categorise bodies of knowledge. The contributions to this edit-ed volume examine the emergence, establishment and contestation of such knowledge orders on three levels: their practical social relevance, their European dimension, and their transformation through digital representation
Novel deep learning architectures for marine and aquaculture applications
Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices
LIPIcs, Volume 277, GIScience 2023, Complete Volume
LIPIcs, Volume 277, GIScience 2023, Complete Volum
12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK
No abstract available
LSTM-MLP BASED UNCERTAINTY MODELLING APPROACH FOR COMPLEX HUMAN INDOOR TRAJECTORY
Modelling the movement uncertainty of human indoor trajectory consist of an essential part in promoting the performance of smart city related applications. At this stage, the existing uncertainty modelling algorithms usually take the constant sampling error or measurement error into consideration and cannot adapt well to the changeable human motion modes and complex handheld modes of smartphones. To fill this gap, this paper applied the Long Short-Term Memory (LSTM) network for continuous prediction of uncertainty error of human indoor trajectory with complex motion modes and detected indoor landmark points. The human motion information including handheld modes, walking speed, and heading information in extracted and fused with detected landmark points for reconstruction of human indoor trajectory under large-scale areas using Gradient Descent (GD) algorithm. In addition, the hybrid LSTM and Multilayer Perceptron (MLP) network is adopted for uncertainty error prediction, by considering both sampling error and measurement error in a specific time period, and the reconstructed trajectory with human motion features are modelled as the input vector for model training with the ground-truth uncertainty error as reference. Comprehensive experiments on real-world collected dataset indicate that the proposed LSTM-assisted uncertainty modelling algorithm has robust outperformance in uncertainty error prediction and uncertainty region definition compared with traditional uncertainty modelling approaches
Wissen ordnen und entgrenzen - vom analogen zum digitalen Europa? Ein Europa der Differenzen, Band 4
Der Band fragt danach, wie Wissensordnungen gesellschaftliche Differenzierungen bestätigen, verstärken, infrage stellen oder neu schaffen und inwiefern die Digitalisierung solche Differenzierungsprozesse graduell oder prinzipiell verändert. Als Wissensordnungen werden hier intentional konstruierte und medial vermittelte Ordnungen verstanden, die Wissensbestände eingrenzen, systematisieren, klassifizieren und kategorisieren. Die Entstehung, Etablierung und Infragestellung solcher Wissensordnungen untersuchen die Beiträge auf drei Ebenen: ihrer handlungspraktischen gesellschaftlichen Relevanz, ihrer Veränderung durch digitale Repräsentation und ihrer europäischen Dimension
Evaluación de aplicaciones educativas de AR con estudiantes adultos
There is a growing interest in the educational applications of Augmented Reality (AR). While most applications of these technologies have been examined in the context of children education, our knowledge about their usefulness in adult education is deficient and particularly more in the category of Location-Based Augmented Reality (LBMAR) games, so the aim of this thesis is to examine the opinions of adult learners (young, middle-aged and elderly) about one particular LBMAR game, the “Ingress”. The main problems addressed by this research comprise questions relating to the usefulness of some key technological components of AR in education (secondarily) and to aspects of training adults by using AR (primarily). As concerns the technological aspects, i.e. "which one of the three types of AR (marker-based, markerless, location-based) is more often associated with naturalistic approaches and what are their relative advantages?”, this research showed that naturalistic approaches applied to marker-based AR enhance active participation in virtual environments, motivate learners, promote personal involvement in conquering new information, offer different perspectives of the content and arouse interest for knowledge. In the case of markerless AR, naturalistic designs foster participants’ collaboration in games, enhance interactivity, offer panoramic views, visualization, and the possibility to examine the role of visual controls. Moreover, naturalistic approaches applied to location-based AR are suitable for designing multidisciplinary applications, can be used for training and have the possibility to be designed so as to account for locality and context. The fact that, as appears from the peer-reviewed literature that was examined, the location-based AR technologies are more appropriate for education and for smartphones, hints that a deeper examination of their potential usefulness for education (and for adult education in particular) might be interesting. As concerns the educational aspects, which also constitute the main focus of the thesis, these focus on the exploration of the usefulness of the LBMAR game “Ingress” in adult education, by answering two intertwined questions: a) how to assess opinions of adult learners about the LBMAR game “Ingress”? and b) what do adult learners think about this game and how do they perceive its features? Providing answers to these questions is tantamount to receiving adequate results from quantitative and qualitative empirical research which would be designed so as to explore their opinions, views and attitudes with respect to this LBMAR game. Hence, 45 adult persons from Greece, aged 20 to 62,cooperated as subjects of this research. They followed a short introductory informal training (on AR, VR, MAR and the games that are relevant to these technologies) by the researcher of this thesis and were subsequently given the instructions of how to use “Ingress” on a smartphone. The quantitative research was carried out before and after training and all trainees participated by filling 31 Likert-type closed questions before and after training. The qualitative research was based on the analysis of their responses to two different sets of open-ended questions. The first set consisted in 5 such questions to which answered 24 participants and the second set had 2 broader questions to which responded 36 participants. This research showed the advantages for adult education of integrating the ARCS model into the learning phase of an LBMAR game. Specifically, it was shown that using Keller’s widely known “ARCS model” (Attention – Relevance – Confidence - Satisfaction) enabled the classification of users’ responses with respect to their interaction with the game and is therefore useful in evaluating adult education with LBMAR games. The players’ responses did not change linearly with their age and the training has had different impact on each age group of learners. Also, statistical analyses proved that training increased the scores of the factors of ARCS model. Other results of this research showed that those over 36 years old focused more on the facts that the play of “Ingress” is primarily a geographical game and its scenario reflects interesting discussions about the evolution of humanity. Participants in the age groups 20–35 and >52 agreed that the game does not have idle phases, that it combines excitement with insecurity, and that it is pleasant to play locally a game of planetary proportions.Another age-related observation concerned the answers to the question (“How do you feel when you endow the geographical space with personal preferences?”) between age groups with age groups agreeing in pairs: the first two age groups (20–30) and (30–40) agreed more than with the last two (40–50) and (50–60). Yet, in question “Do you think that the game offers opportunities for learning and teaching geography, building on your previous geographical knowledge?” , there was an overlap in the responses of participants among age groups. As for the first question, the most critical concept was: “the users feel a kind of nostalgia”, followed by the concept “the users consider portals as personal creations”. In the case of the second question, the most critical concept was that the participants believe that the game offers entirely new opportunities for education in geography, compared with their previous experiences. This fosters an evidence of constructivist approaches to adult education and, also, relevance of some other prominent theories of adult education such as humanism. Methodologically, this research it was shown that content analysis is a valuable method for exploring opinions and attitudes of adult users towards MAR games and Jaccard indices can be used to quantitatively explore themes emerging from content analysis. Content analysis was performed on the users’ responses qualitatively in order to identify characteristic sentences expressing attitudes and opinions. For the quantitative assessment of similarities between responses for each question and subconcept, the Jaccard similarity index was calculated pair-wise for every pair of participants. In addition to the Jaccard indices and furthering the scope of new methods for content analysis, this thesis shows how to use Social Network Analysis (SNA) to model concept maps, thus opening up excellent opportunities to create visualizations of concepts and their inter-relationships. Quantitative aspects of SNA analysis (i.e. by using radial centrality and information centrality) provide mechanisms suitable to measure internal relationships in concept maps (in addition to visual inspection) that would not otherwise be visible. Using SNA enabled the classification of users’ responses with respect to their interaction with the game and therefore was a fruitful approach for education that involves MAR games. Furthermore, with this novelty, it is shown how texts derived from interviews or from responses to open questions by different individuals can be analyzed both qualitatively and quantitatively with SNA. Concluding, this research has produced novelties at both the educational and the methodological levels. As concerns adult education, it was shown that i) LBMAR games are suitable for it, ii) adult education about them can be enhanced by following Keller’s ARCS model, iii) perception and satisfaction of adult learners depends on age, and iv) certain theories of adult education (i.e. constructivism and humanism) can be relevant when adult learners use LBMAR games such as “Ingress”. As concerns methods of educational research, this research suggested entirely new methods, for first time ever, for analyzing data that are derived from trainees' responses to open questions. These new methods are content analysis of the participants’ responses with the use of Jaccard indices and methods of SNA and can have a wider applicability to educational research.Existe un creciente interés por las aplicaciones educativas de la Realidad Aumentada (RA). Mientras que la mayoría de las aplicaciones de estas tecnologías se han examinado en el contexto de la educación infantil, nuestro conocimiento sobre su utilidad en la educación de adultos es deficiente y, en particular, más en la categoría de juegos de Realidad Aumentada Basada en la Localización (LBMAR), por lo que el objetivo de esta tesis es examinar las opiniones de los estudiantes adultos (jóvenes, de mediana edad y mayores) sobre un juego LBMAR, el "Ingress". Los principales problemas que aborda esta investigación comprenden cuestiones relacionadas con la utilidad de algunos componentes tecnológicos de la RA en la educación (secundariamente) y con aspectos de la formación de adultos mediante el uso de la RA (principalmente). En cuanto a los aspectos tecnológicos, es decir "¿cuál de los tres tipos de RA (basada en marcadores, sin marcadores, basada en la localización) se asocia más a los enfoques naturalistas y cuáles son sus ventajas relativas?", esta investigación demostró que los enfoques naturalistas aplicados a la RA basada en marcadores potencian la participación activa en entornos virtuales, motivan a los alumnos, promueven la implicación personal en la conquista de nueva información, ofrecen diferentes perspectivas del contenido y despiertan el interés por el conocimiento. En el caso de la RA sin marcadores, los diseños naturalistas fomentan la colaboración de los participantes en los juegos, mejoran la interactividad, ofrecen vistas panorámicas, visualización y la posibilidad de examinar los controles visuales. Además, los enfoques naturalistas aplicados a la RA basada en la localización son adecuados para el diseño de aplicaciones multidisciplinares, pueden utilizarse para la formación y tienen la posibilidad de diseñarse teniendo en cuenta la localidad y el contexto. El hecho de que, como se desprende de la literatura revisada por pares que se examinó, las tecnologías de RA basadas en la localización son más apropiadas para la educación y para los teléfonos smartphones, sugiere que podría ser interesante un examen más profundo de su utilidad potencial para la educación (y para la educación de adultos en particular). En cuanto a los aspectos educativos, que también constituyen el foco principal de la tesis, éstos se centran en la exploración de la utilidad del juego LBMAR "Ingress" en la educación de adultos, respondiendo a dos preguntas entrelazadas: a) ¿cómo evaluar las opiniones de los alumnos adultos sobre el juego LBMAR "Ingress"? y b) ¿qué piensan los alumnos adultos sobre este juego y cómo perciben sus características? Dar respuesta a estas preguntas equivale a recibir resultados adecuados de una investigación empírica, cuantitativa y cualitativa, que se diseñaría para explorar sus opiniones, puntos de vista y actitudes con respecto a este juego LBMAR. Por lo tanto, 45 personas adultas de Grecia, con edades entre 20 y 62 años, colaboraron como sujetos de esta investigación. Siguieron una breve formación informal introductoria por parte del investigador de esta tesis (sobre RA, realidad virtual, RA móvil, y sobre los juegos relacionados con estas tecnologías) y posteriormente se les dieron las instrucciones de cómo utilizar "Ingress" en un smartphone. La investigación cuantitativa se llevó a cabo antes y después de la formación y todos los alumnos participaron respondiendo a 31 preguntas cerradas del tipo Likert antes y después de la formación. La investigación cualitativa se basó en el análisis de sus respuestas a dos conjuntos diferentes de preguntas abiertas. El primer conjunto constaba de 5 preguntas de este tipo (a las que respondieron 24 participantes) y el segundo conjunto tenía 2 preguntas más amplias, a las que respondieron 36 participantes. La investigación cuantitativa mostró las ventajas para la educación de adultos de integrar el modelo ARCS en la fase de aprendizaje de un juego LBMAR. En concreto, se demostró que la utilización del conocido "modelo ARCS" de Keller (Atención - Relevancia - Confianza - Satisfacción) permitió clasificar las respuestas de los usuarios con respecto a sus interacciones con el juego y, también, es útil para evaluar la educación de adultos con juegos LBMAR. Las respuestas de los jugadores no cambiaron linealmente con su edad y el entrenamiento ha tenido un impacto diferente en cada grupo de edad de los alumnos. Además, los análisis estadísticos demostraron que el entrenamiento aumentó las puntuaciones de los factores del modelo ARCS. Otros resultados de esta investigación mostraron que los mayores de 36 años se centraron más en el hecho de que el juego "Ingress" es principalmente un juego geográfico y su escenario refleja interesantes debates sobre la evolución de la humanidad. Las opiniones de los participantes de los grupos de edad de 20-35 y >52 coincidieron en que el juego no tiene fases ociosas, que combina la emoción con la inseguridad y que es agradable jugar a nivel local a un juego de proporciones planetarias. Otra observación relacionada con la edad se refería a las respuestas a la pregunta ("¿Cómo te sientes cuando dotas al espacio geográfico de preferencias personales?") entre grupos de edad que coincidían: los dos primeros grupos de edad (20-30) y (30-40) estaban más de acuerdo que los dos últimos (40-50) y (50-60). Sin embargo, en la pregunta "¿Crees que el juego ofrece oportunidades para aprender y enseñar geografía, aprovechando tus conocimientos geográficos previos?" hubo un solapamiento en las respuestas de los participantes entre los grupos de edad. En cuanto a la primera pregunta, el concepto más crítico fue: "los usuarios sienten una especie de nostalgia", seguido del concepto "los usuarios consideran los portales como creaciones personales". En el caso de la segunda pregunta, el concepto más crítico fue que los participantes creen que el juego ofrece oportunidades totalmente nuevas para la educación en geografía en comparación con sus experiencias anteriores. Esto fomenta una evidencia de los enfoques constructivistas de la educación de adultos y, también, la relevancia de algunas otras teorías prominentes de la educación de adultos, como el humanismo. Metodológicamente, esta investigación demostró también que el análisis de contenido es un método valioso para explorar las opiniones y actitudes de los usuarios adultos hacia los juegos MAR y los índices de Jaccard pueden utilizarse para explorar cuantitativamente los temas que surgen del análisis de contenido. El análisis de contenido se realizó sobre las respuestas de los usuarios de forma cualitativa para identificar las frases características que expresan actitudes y opiniones. Para la evaluación cuantitativa de las similitudes entre las respuestas de cada pregunta y subconcepto, se calculó el índice de similitud de Jaccard por parejas para cada par de participantes. Además de los índices de Jaccard y de ampliar el alcance de los nuevos métodos de análisis de contenido, esta tesis muestra cómo utilizar el Análisis de Redes Sociales (Social Networks Analysis - SNA) para modelar los mapas conceptuales, abriendo así excelentes oportunidades para crear visualizaciones de los conceptos y sus interrelaciones. Los aspectos cuantitativos del análisis SNA (es decir mediante el uso de la centralidad radial y la centralidad de la información) proporcionan mecanismos adecuados para medir las relaciones internas en los mapas conceptuales (además de la inspección visual) que de otro modo no serían visibles. El uso del SNA permitió la clasificación de las respuestas de los usuarios con respecto a su interacción con el juego y, por lo tanto, fue un enfoque fructífero para la educación que involucra los juegos MAR. Además, con esta novedad, se muestra cómo los textos derivados de las entrevistas o de las respuestas a las preguntas abiertas de diferentes individuos pueden ser analizados tanto cualitativamente como cuantitativamente usando SNA. En conclusión, esta investigación ha aportado novedades, tanto a nivel educativo como metodológico. En lo que respecta a la educación de adultos, se ha demostrado que i) los juegos LBMAR son adecuados para ella, ii) la educación de adultos sobre ellos puede mejorarse siguiendo el modelo ARCS de Keller, iii) la percepción y la satisfacción de los alumnos adultos depende de la edad, y iv) ciertas teorías de la educación de adultos (es decir, el constructivismo y el humanismo) pueden ser relevantes cuando los alumnos adultos utilizan juegos LBMAR como "Ingress". En cuanto a los métodos de investigación educativa, esta investigación sugirió métodos totalmente nuevos para analizar los datos que se derivan de las respuestas de los alumnos a las preguntas abiertas. Estos nuevos métodos son el análisis de contenido de las respuestas de los participantes con el uso de los índices de Jaccard y los métodos de SNA y pueden tener una aplicabilidad más amplia a la investigación educativa
- …