Awareness and Learning in Participatory Noise Sensing

The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

Technical and Didactical Scenarios of Student-centered Teaching and Learning

In this paper we envision didactical concepts for university education based on self-responsible and project-based learning and outline principles of adequate technical support. We use the scenario technique describing how a fictive student named Anna organizes her studies of informatics at a fictive university from the first days of her studies to make a career for herself.(DIPF/Orig.)

Development and validation of the team learning questionnaire

Nowadays the organizational scenario is changing in several aspects that affect organization commitment. Team learning construct has emerged as a tool to deal with these changes and the dynamic nature of this situation. Although team learning has acquired importance in recent years, instruments to measure team learning should be developed. The aim of this paper is to develop and validate a team learning scale, the Team Learning Questionnaire, attending to four dimensions of team learning: Continued Improvement Seeking, Dialogue Promotion and Open Communication, Collaborative Learning, and Strategic and Proactive Leadership that Promote Learning. Results provide evidence of the reliability and validity of the scale.

Mind the gap! An exploration of the role of lifelong learning in promoting co-production and citizenship within social care for older people

Contemporary themes in public policy have emphasised co-productive approaches within both the access and provision of support services to older people. This paper provides a cross disciplinary exploration from its respective authors perspectives on social work and educational gerontology to examine the potential for lifelong learning and learning interventions from which co-production with those using social care services in later life might be better facilitated. Using an example from the UK, we specifically elicit how co-produced care can enhance the horizon of learning and learning research. The synthesis of ideas across these two disciplines could enrich understanding and provide essential levers for moving towards empowerment and emancipation by engaging with a more co-productive approach in social care for older people. (DIPF/Orig.)

Learning English is Fun! Incidental vocabulary acquisition of music and traditional class ninth graders via listening to pop songs

Finnish youth are constantly exposed to music and lyrics in English in their free time. It is likely that this has a positive effect on vocabulary learning. Learning vocabulary while simultaneously accompanied with melodies is likely to result in better learning outcomes. The present thesis covers a study on the vocabulary learning of traditional and music class ninth graders in a south-western upper comprehensive school in Finland, mainly concentrating on vocabulary learning as a by-product of listening to pop music and learning vocabulary through semantic priming. The theoretical background presents viable linguistic arguments and theories, which provide clarity for why it would be possible to learn English vocabulary via listening to pop songs. There is conflicting evidence on the benefits of music on vocabulary learning, and this thesis sets out to shed light on the situation. Additionally, incorporating pop music in English classes could assist in decreasing the gap between real world English and school English. The thesis is a mixed method research study consisting of both quantitative and qualitative research materials. The methodology comprises vocabulary tests both before and after pop music samples and a background questionnaire filled by students. According to the results, all students reported liking listening to music and they clearly listened to English pop music the most. A statistically significant difference was found when analysing the results of the differences in pre- and post-vocabulary tests. However, the traditional class appeared to listen to mainstream pop music more than the students in the music class, and thus it seems likely that the traditional class benefited more from vocabulary learning occurring via listening to pop songs. In conclusion, it can be established that it is possible to learn English vocabulary via listening to pop songs and that students wish their English lectures would involve more music-related vocabulary exercises in the future. Thus, when it comes to school learning, pop songs should be utilised in vocabulary learning, which could also in turn result in more diverse learning and the students could, more easily than before, relate to the themes and topics of the lectures. Furthermore, with the help of pop songs it would be possible to decrease the gap between school English and real-world English.

The Effectiveness of Outdoor Education on Environmental Learning, Appreciation, and Activism

The main objective of this research was to determine the effectiveness of outdoor education on student knowledge retention, appreciation for nature, and environmental activism in a college level course on south Florida ecology. Six class sections were given quizzes on four course topics either post-lecture or post-field trip. Students were also given pre-course and post-course opinion surveys. Although mean quiz scores for the post-field trip were higher than for the post-lecture, statistical analysis determined that there was no significant difference in quiz scores for location taken (post-lecture or post-field trip). Survey results show a correlation between knowledge of environmental issues and environmental activism. Even though student survey responses point to outdoor education and field trips being the most effective method of learning and influential on appreciation for nature, the quiz scores do not reflect such.

From the workplace to the classroom:examining the impact of self-leadership learning strategies on higher educational attainment and success

Self-leadership is a concept from the organisational and management literature broadly combining processes of self-goal setting, self-regulation and self-motivation. Research has typically focused on the impact of self-leadership on work performance outcomes, with little attention to potential benefits for learning and development. In this paper, we employ a longitudinal design to examine the association of a number of processes of self-leadership with higher educational attainment in a sample of business students (N = 150). Self-reported use of strategies related to behavioural, cognitive and motivational aspects of self-leadership were measured in the first semester of the academic year, and correlated with end-of year grade point average. We found that in particular, self-goal setting, pro-active goal-related behaviour, behaviour regulation and direction, motivational awareness, and optimism were all significant predictors of educational attainment. We discuss implications for educational research and for teachers and tutors in practice.

Adaptive and Maladaptive Implications of Reinforcement Learning Processes: Fronto-Striatal Loops and Behavioural Correlates

That humans and animals learn from interaction with the environment is a foundational idea underlying nearly all theories of learning and intelligence. Learning that certain outcomes are associated with specific actions or stimuli (both internal and external), is at the very core of the capacity to adapt behaviour to environmental changes. In the present work, appetitive and aversive reinforcement learning paradigms have been used to investigate the fronto-striatal loops and behavioural correlates of adaptive and maladaptive reinforcement learning processes, aiming to a deeper understanding of how cortical and subcortical substrates interacts between them and with other brain systems to support learning. By combining a large variety of neuroscientific approaches, including behavioral and psychophysiological methods, EEG and neuroimaging techniques, these studies aim at clarifying and advancing the knowledge of the neural bases and computational mechanisms of reinforcement learning, both in normal and neurologically impaired population.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Design and implementation of a Reinforcement Learning framework for iOS devices

Reinforcement Learning is an increasingly popular area of Artificial Intelligence. The applications of this learning paradigm are many, but its application in mobile computing is in its infancy. This study aims to provide an overview of current Reinforcement Learning applications on mobile devices, as well as to introduce a new framework for iOS devices: Swift-RL Lib. This new Swift package allows developers to easily support and integrate two of the most common RL algorithms, Q-Learning and Deep Q-Network, in a fully customizable environment. All processes are performed on the device, without any need for remote computation. The framework was tested in different settings and evaluated through several use cases. Through an in-depth performance analysis, we show that the platform provides effective and efficient support for Reinforcement Learning for mobile applications.

The influence of emotions, attitudes and perceptions on learning with technology

Welcome to this very international issue of Research in Learning Technology in which we present research that has been undertaken in UK, Chile, Finland, Germany, Portugal and USA. The articles on the use of technology span a range of effective teaching practices, showcase strategies for successful learning and propose ideas for future mechanisms to better engage students in their educational experiences. For me, one question running through this issue is: how is technology helping us to deliver more student-centred education?

Concepts and methods for efficient decentralized learning in federated settings

Deep Neural Networks (DNNs) have revolutionized a wide range of applications beyond traditional machine learning and artificial intelligence fields, e.g., computer vision, healthcare, natural language processing and others. At the same time, edge devices have become central in our society, generating an unprecedented amount of data which could be used to train data-hungry models such as DNNs. However, the potentially sensitive or confidential nature of gathered data poses privacy concerns when storing and processing them in centralized locations. To this purpose, decentralized learning decouples model training from the need of directly accessing raw data, by alternating on-device training and periodic communications. The ability of distilling knowledge from decentralized data, however, comes at the cost of facing more challenging learning settings, such as coping with heterogeneous hardware and network connectivity, statistical diversity of data, and ensuring verifiable privacy guarantees. This Thesis proposes an extensive overview of decentralized learning literature, including a novel taxonomy and a detailed description of the most relevant system-level contributions in the related literature for privacy, communication efficiency, data and system heterogeneity, and poisoning defense. Next, this Thesis presents the design of an original solution to tackle communication efficiency and system heterogeneity, and empirically evaluates it on federated settings. For communication efficiency, an original method, specifically designed for Convolutional Neural Networks, is also described and evaluated against the state-of-the-art. Furthermore, this Thesis provides an in-depth review of recently proposed methods to tackle the performance degradation introduced by data heterogeneity, followed by empirical evaluations on challenging data distributions, highlighting strengths and possible weaknesses of the considered solutions. Finally, this Thesis presents a novel perspective on the usage of Knowledge Distillation as a mean for optimizing decentralized learning systems in settings characterized by data heterogeneity or system heterogeneity. Our vision on relevant future research directions close the manuscript.

The transformative potential of community engagement in higher education: an analysis of service-learning effects on students, communities, and institutions.

Service-learning in higher education is gaining attention as a reliable tool to support students’ learning and fulfil the mission of higher education institutions (HEIs). This dissertation addresses existing gaps in the literature by examining the effects and perspectives of service-learning in HEIs through three studies. The first study compares the effects of a voluntary semester-long service-learning course with traditional courses. A survey completed by 110 students before and after the lectures found no significant group differences in the psychosocial variables under inspection. Nevertheless, service-learning students showed higher scores concerning the quality of participation. Factors such as students’ perception of competence, duration of service-learning, and self-reported measures may have influenced the results. The second study explores the under-researched perspective of community partners in higher education and European settings. Twelve semi-structured interviews were conducted with community partners from various community organisations across Europe. The results highlight positive effects on community members and organisations, intrinsic motivations, organisational empowerment, different forms of reciprocity, the co-educational role of community partners, and the significant role of a sense of community and belonging. The third study focuses on faculty perspectives on service-learning in the European context. Twenty-two semi-structured interviews were conducted in 14 European countries. The findings confirm the transformative impact of service-learning on the community, students, teachers, and HEIs, emphasising the importance of motivation and institutionalisation processes in sustaining engaged scholarship. The study also identifies the relevance of the community experience, sense of community, and community responsibility with the service-learning experience; relatedness is proposed as the fifth pillar of service-learning. Overall, this dissertation provides new insights into the effects and perspectives of service-learning in higher education. It integrates the 4Rs model with the addition of relatedness, guiding the theoretical and practical implications of the findings. The dissertation also suggests limitations and areas for further research.

Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.