Showing papers by "Open University of Catalonia published in 2020"

Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity

Abstract: The Covid-19 pandemic has raised significant challenges for the higher education community worldwide. A particular challenge has been the urgent and unexpected request for previously face-to-face university courses to be taught online. Online teaching and learning imply a certain pedagogical content knowledge (PCK), mainly related to designing and organising for better learning experiences and creating distinctive learning environments, with the help of digital technologies. With this article, we provide some expert insights into this online-learning-related PCK, with the goal of helping non-expert university teachers (i.e. those who have little experience with online learning) to navigate in these challenging times. Our findings point at the design of learning activities with certain characteristics, the combination of three types of presence (social, cognitive and facilitatory) and the need for adapting assessment to the new learning requirements. We end with a reflection on how responding to a crisis (as best we can) may precipitate enhanced teaching and learning practices in the postdigital era.

A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis

Abstract: Uncertain times require prompt reflexes to survive and this study is a collaborative reflex to better understand uncertainty and navigate through it. The Coronavirus (Covid-19) pandemic hit hard and interrupted many dimensions of our lives, particularly education. As a response to interruption of education due to the Covid-19 pandemic, this study is a collaborative reaction that narrates the overall view, reflections from the K-12 and higher educational landscape, lessons learned and suggestions from a total of 31 countries across the world with a representation of 62,7% of the whole world population. In addition to the value of each case by country, the synthesis of this research suggests that the current practices can be defined as emergency remote education and this practice is different from planned practices such as distance education, online learning or other derivations. Above all, this study points out how social injustice, inequity and the digital divide have been exacerbated during the pandemic and need unique and targeted measures if they are to be addressed. While there are support communities and mechanisms, parents are overburdened between regular daily/professional duties and emerging educational roles, and all parties are experiencing trauma, psychological pressure and anxiety to various degrees, which necessitates a pedagogy of care, affection and empathy. In terms of educational processes, the interruption of education signifies the importance of openness in education and highlights issues that should be taken into consideration such as using alternative assessment and evaluation methods as well as concerns about surveillance, ethics, and data privacy resulting from nearly exclusive dependency on online solutions.

Influence of COVID-19 confinement on students' performance in higher education.

Abstract: This study analyzes the effects of COVID-19 confinement on the autonomous learning performance of students in higher education. Using a field experiment with 458 students from three different subjects at Universidad Autonoma de Madrid (Spain), we study the differences in assessments by dividing students into two groups. The first group (control) corresponds to academic years 2017/2018 and 2018/2019. The second group (experimental) corresponds to students from 2019/2020, which is the group of students that had their face-to-face activities interrupted because of the confinement. The results show that there is a significant positive effect of the COVID-19 confinement on students' performance. This effect is also significant in activities that did not change their format when performed after the confinement. We find that this effect is significant both in subjects that increased the number of assessment activities and subjects that did not change the student workload. Additionally, an analysis of students' learning strategies before confinement shows that students did not study on a continuous basis. Based on these results, we conclude that COVID-19 confinement changed students' learning strategies to a more continuous habit, improving their efficiency. For these reasons, better scores in students' assessment are expected due to COVID-19 confinement that can be explained by an improvement in their learning performance.

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare.

Abstract: Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.

Academia in the Time of COVID-19: Towards an Ethics of Care

Abstract: The global COVID-19 pandemic is affecting people’s work-life balance across the world. For academics, confinement policies enacted by most countries have implied a sudden switch to home-work, a tra...

Updating the Mediterranean Diet Pyramid towards Sustainability: Focus on Environmental Concerns

Abstract: Background: Nowadays the food production, supply and consumption chain represent a major cause of ecological pressure on the natural environment, and diet links worldwide human health with environmental sustainability. Food policy, dietary guidelines and food security strategies need to evolve from the limited historical approach, mainly focused on nutrients and health, to a new one considering the environmental, socio-economic and cultural impact—and thus the sustainability—of diets. Objective: To present an updated version of the Mediterranean Diet Pyramid (MDP) to reflect multiple environmental concerns. Methods: We performed a revision and restructuring of the MDP to incorporate more recent findings on the sustainability and environmental impact of the Mediterranean Diet pattern, as well as its associations with nutrition and health. For each level of the MDP we provided a third dimension featuring the corresponding environmental aspects related to it. Conclusions: The new environmental dimension of the MDP enhances food intake recommendations addressing both health and environmental issues. Compared to the previous 2011 version, it emphasizes more strongly a lower consumption of red meat and bovine dairy products, and a higher consumption of legumes and locally grown eco-friendly plant foods as much as possible.

Expanding the Boundaries of Justice in Urban Greening Scholarship: Toward an Emancipatory, Antisubordination, Intersectional, and Relational Approach

Abstract: Supported by a large body of scholarship, it is increasingly orthodox practice for cities to deploy urban greening interventions to address diverse socioenvironmental challenges, from protecting ur...

Editorial: How to Prevent, Detect and Control Common Method Variance in Electronic Commerce Research

Abstract: Many empirical studies submitted to the Journal of Theoretical and Applied Electronic Commerce Research make use of questionnaire instruments to collect data.[...]

Assessing concerns for the economic consequence of the COVID-19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United Kingdom.

Abstract: Many different countries have been under lockdown or extreme social distancing measures to control the spread of COVID-19. The potentially far-reaching side effects of these measures have not yet been fully understood. In this study we analyse the results of a multi-country survey conducted in Italy (N = 3,504), Spain (N = 3,524) and the United Kingdom (N = 3,523), with two separate analyses. In the first analysis, we examine the elicitation of citizens' concerns over the downplaying of the economic consequences of the lockdown during the COVID-19 pandemic. We control for Social Desirability Bias through a list experiment included in the survey. In the second analysis, we examine the data from the same survey to predict the level of stress, anxiety and depression associated with being economically vulnerable and having been affected by a negative economic shock. To accomplish this, we have used a prediction algorithm based on machine learning techniques. To quantify the size of this affected population, we compare its magnitude with the number of people affected by COVID-19 using measures of susceptibility, vulnerability and behavioural change collected in the same questionnaire. We find that the concern for the economy and for "the way out" of the lockdown is diffuse and there is evidence of minor underreporting. Additionally, we estimate that around 42.8% of the populations in the three countries are at high risk of stress, anxiety, and depression, based on their level of economic vulnerability and their exposure to a negative economic shock.

Turning the Crisis Into an Opportunity: Digital Health Strategies Deployed During the COVID-19 Outbreak.

Abstract: Digital health technologies offer significant opportunities to reshape current health care systems. From the adoption of electronic medical records to mobile health apps and other disruptive technologies, digital health solutions have promised a better quality of care at a more sustainable cost. However, the widescale adoption of these solutions is lagging behind. The most adverse scenarios often provide an opportunity to develop and test the capacity of digital health technologies to increase the efficiency of health care systems. Catalonia (Northeast Spain) is one of the most advanced regions in terms of digital health adoption across Europe. The region has a long tradition of health information exchange in the public health care sector and is currently implementing an ambitious digital health strategy. In this viewpoint, we discuss the crucial role digital health solutions play during the coronavirus disease (COVID-19) pandemic to support public health policies. We also report on the strategies currently deployed at scale during the outbreak in Catalonia.

Rediscovering the use of chatbots in education: A systematic literature review

Abstract: Chatbots have been around for years and have been used in many areas such as medicine or commerce. Our focus is on the development and current uses of chatbots in the field of education, where they can function as service assistants or as educational agents. In this research paper, we attempt to make a systematic review of the literature on educational chatbots that address various issues. From 485 sources, 80 studies on chatbots and their application in education were selected through a step‐by‐step procedure based on the guidelines of the PRISMA framework, using a set of predefined criteria. The results obtained demonstrate the existence of different types of educational chatbots currently in use that affect student learning or improve services in various areas. This paper also examines the type of technology used to unravel the learning outcome that can be obtained from each type of chatbots. Finally, our results identify instances where a chatbot can assist in learning under conditions similar to those of a human tutor, while exploring other possibilities and techniques for assessing the quality of chatbots. Our analysis details these findings and can provide a solid framework for research and development of chatbots for the educational field.

Context Based Emotion Recognition Using EMOTIC Dataset

Abstract: In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacity of recognizing emotions. From a computer vision perspective, most of the previous efforts have been focusing in analyzing the facial expressions and, in some cases, also the body pose. Some of these methods work remarkably well in specific settings. However, their performance is limited in natural, unconstrained environments. Psychological studies show that the scene context, in addition to facial expression and body pose, provides important information to our perception of people's emotions. However, the processing of the context for automatic emotion recognition has not been explored in depth, partly due to the lack of proper data. In this paper we present EMOTIC, a dataset of images of people in a diverse set of natural situations, annotated with their apparent emotion. The EMOTIC dataset combines two different types of emotion representation: (1) a set of 26 discrete categories, and (2) the continuous dimensions Valence , Arousal , and Dominance . We also present a detailed statistical and algorithmic analysis of the dataset along with annotators’ agreement analysis. Using the EMOTIC dataset we train different CNN models for emotion recognition, combining the information of the bounding box containing the person with the contextual information extracted from the scene. Our results show how scene context provides important information to automatically recognize emotional states and motivate further research in this direction.

Girls in STEM: Is It a Female Role-Model Thing?

Abstract: Women are underrepresented in STEM (science, technology, engineering, and mathematics) careers, and this poses new challenges at the dawn of the era of digital transformation. The goal of the present study is to demonstrate how female role models influence girls' preferences for STEM studies. This paper evaluates a role-model intervention in which female volunteers working in STEM go into schools to talk to girls about their careers. The study was conducted with 304 girls, from 12 years old (sixth primary grade) to 16 years old (fourth secondary grade), both before and after the role-model sessions. An adaptation of the expectancy-value theory of achievement motivation is used to test the extent to which this role-model intervention improves girls' beliefs that they can be successful in STEM fields and increases their likelihood of choosing a STEM career. The results of multigroup structural equation modeling analysis show that on average, the role-model intervention has a positive and significant effect on mathematics enjoyment, importance attached to math, expectations of success in math, and girls' aspirations in STEM, and a negative effect on gender stereotypes. Additionally, the female role-model sessions significantly increase the positive impact of expectations of success on STEM choices. Finally, the moderation role of the counterstereotypical content of the role-model sessions is tested. The results show that the higher the counterstereotypical character of the sessions, the higher the relationship between expectations of success in math and the choice of STEM. These results are discussed regarding their implications for long-term STEM engagement.

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics.

Abstract: One of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. Therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called CHHO-CS, which combines Harris hawks optimizer (HHO) with two operators: cuckoo search (CS) and chaotic maps. The role of CS is to control the main position vectors of the HHO algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. Feature selection (FS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then FS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. The support vector machines (SVMs) are then used by the proposed CHHO-CS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. The extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. The experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods.

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Abstract: Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial intelligence—are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.

A variable neighborhood search simheuristic for project portfolio selection under uncertainty

Abstract: With limited financial resources, decision-makers in firms and governments face the task of selecting the best portfolio of projects to invest in. As the pool of project proposals increases and more realistic constraints are considered, the problem becomes NP-hard. Thus, metaheuristics have been employed for solving large instances of the project portfolio selection problem (PPSP). However, most of the existing works do not account for uncertainty. This paper contributes to close this gap by analyzing a stochastic version of the PPSP: the goal is to maximize the expected net present value of the inversion, while considering random cash flows and discount rates in future periods, as well as a rich set of constraints including the maximum risk allowed. To solve this stochastic PPSP, a simulation-optimization algorithm is introduced. Our approach integrates a variable neighborhood search metaheuristic with Monte Carlo simulation. A series of computational experiments contribute to validate our approach and illustrate how the solutions vary as the level of uncertainty increases.

Cervical screening: ESGO-EFC position paper of the European Society of Gynaecologic Oncology (ESGO) and the European Federation of Colposcopy (EFC)

Abstract: This paper summarises the position of ESGO and EFC on cervical screening based on existing guidelines and opinions of a team of lead experts. HPV test is replacing cytology as this offers greater protection against cervical cancer and allows longer screening intervals. Only a dozen of HPV tests are considered as clinically validated for screening. The lower specificity of HPV test dictates the use of triage tests that can select women for colposcopy. Reflex cytology is currently the only well validated triage test; HPV genotyping and p16 immunostaining may be used in the future, although methylation assays and viral load also look promising. A summary of quality assurance benchmarks is provided, and the importance to audit the screening histories of women who developed cancer is noted as a key objective. HPV-based screening is more cost-effective than cytology or cotesting. HPV-based screening should continue in the post-vaccination era. Only a fraction of the female population is vaccinated, and this varies across countries. A major challenge will be to personalise screening frequency according to vaccination status. Still the most important factor for successful prevention by screening is high population coverage and organised screening. Screening with self-sampling to reach under-screened women is promising.

Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis.

Abstract: Objectives Myositis is a heterogeneous family of diseases that includes dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotising myopathy (IMNM), inclusion body myositis (IBM), polymyositis and overlap myositis. Additional subtypes of myositis can be defined by the presence of myositis-specific autoantibodies (MSAs). The purpose of this study was to define unique gene expression profiles in muscle biopsies from patients with MSA-positive DM, AS and IMNM as well as IBM. Methods RNA-seq was performed on muscle biopsies from 119 myositis patients with IBM or defined MSAs and 20 controls. Machine learning algorithms were trained on transcriptomic data and recursive feature elimination was used to determine which genes were most useful for classifying muscle biopsies into each type and MSA-defined subtype of myositis. Results The support vector machine learning algorithm classified the muscle biopsies with >90% accuracy. Recursive feature elimination identified genes that are most useful to the machine learning algorithm and that are only overexpressed in one type of myositis. For example, CAMK1G (calcium/calmodulin-dependent protein kinase IG), EGR4 (early growth response protein 4) and CXCL8 (interleukin 8) are highly expressed in AS but not in DM or other types of myositis. Using the same computational approach, we also identified genes that are uniquely overexpressed in different MSA-defined subtypes. These included apolipoprotein A4 (APOA4), which is only expressed in anti-3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) myopathy, and MADCAM1 (mucosal vascular addressin cell adhesion molecule 1), which is only expressed in anti-Mi2-positive DM. Conclusions Unique gene expression profiles in muscle biopsies from patients with MSA-defined subtypes of myositis and IBM suggest that different pathological mechanisms underly muscle damage in each of these diseases.

Quantitative Storytelling in the Making of a Composite Indicator

Abstract: The reasons for and against composite indicators are briefly reviewed, as well as the available theories for their construction. After noting the strong normative dimension of these measures—which ultimately aim to ‘tell a story’, e.g. to promote the social discovery of a particular phenomenon, we inquire whether a less partisan use of a composite indicator can be proposed by allowing more latitude in the framing of its construction. We thus explore whether a composite indicator can be built to tell ‘more than one story’ and test this in practical contexts. These include measures used in convergence analysis in the field of cohesion policies and a recent case involving the World Bank’s Doing Business Index. Our experiments are built to imagine different constituencies and stakeholders who agree on the use of evidence and of statistical information while differing on the interpretation of what is relevant and vital.

Elements of Open Education: An Invitation to Future Research

Abstract: This paper explores elements of open education within the context of higher education. After an introduction to the origins of open education and its theoretical foundations, the topics of open and distance learning, international education issues in open education, open educational practices and scholarship, open educational resources, MOOCs, prior learning accreditation and recognition, and learner characteristics are considered, following the framework of macro, meso, and micro levels of research in open and distance learning. Implications for future research at the macro, meso, and micro levels are then provided.

Neutrophil dysregulation is pathogenic in idiopathic inflammatory myopathies

Abstract: Idiopathic inflammatory myopathies (IIM) are characterized by muscle inflammation and weakness, myositis-specific autoantibodies (MSAs), and extramuscular organ damage. The role of neutrophil dysregulation and neutrophil extracellular traps (NETs) in IIM is unclear. We assessed whether pathogenic neutrophil subsets (low-density granulocytes [LDGs]) and NETs were elevated in IIM, associated with clinical presentation and MSAs, and their effect on skeletal myoblasts and myotubes. Circulating NETs and LDGs were quantified and correlated with clinical measures. Specific MSAs were tested for their ability to induce NETs. NETs and neutrophil gene expression were measured in IIM biopsies. Whether NETs damage skeletal myoblasts and myotubes was tested. Circulating LDGs and NETs were increased in IIM. IIM LDGs had an enhanced ability to form NETs. LDGs and NETs correlated with IIM disease activity and muscle damage. The serum MSA anti-MDA5 correlated with circulating and tissue NETs and directly enhanced NET formation. An enhanced neutrophil gene signature was present in IIM muscle and associated with muscle injury and tissue IFN gene signatures. IIM NETs decreased the viability of myotubes in a citrullinated histone-dependent manner. Dysregulated neutrophil pathways may play pathogenic roles in IIM through their ability to directly injure muscle cells and other affected tissues.

Scientific and local ecological knowledge, shaping perceptions towards protected areas and related ecosystem services

Abstract: Most protected areas are managed based on objectives related to scientific ecological knowledge of species and ecosystems. However, a core principle of sustainability science is that understanding and including local ecological knowledge, perceptions of ecosystem service provision and landscape vulnerability will improve sustainability and resilience of social-ecological systems. Here, we take up these assumptions in the context of protected areas to provide insight on the effectiveness of nature protection goals, particularly in highly human-influenced landscapes. We examined how residents’ ecological knowledge systems, comprised of both local and scientific, mediated the relationship between their characteristics and a set of variables that represented perceptions of ecosystem services, landscape change, human-nature relationships, and impacts. We administered a face-to-face survey to local residents in the Sierra de Guadarrama protected areas, Spain. We used bi- and multi-variate analysis, including partial least squares path modeling to test our hypotheses. Ecological knowledge systems were highly correlated and were instrumental in predicting perceptions of water-related ecosystem services, landscape change, increasing outdoors activities, and human-nature relationships. Engagement with nature, socio-demographics, trip characteristics, and a rural–urban gradient explained a high degree of variation in ecological knowledge. Bundles of perceived ecosystem services and impacts, in relation to ecological knowledge, emerged as social representation on how residents relate to, understand, and perceive landscapes. Our findings provide insight into the interactions between ecological knowledge systems and their role in shaping perceptions of local communities about protected areas. These results are expected to inform protected area management and landscape sustainability.

Ageism in the era of digital platforms

Abstract: Ageism is the most invisible form of discrimination While there is some awareness of gender, racial, and socioeconomic discrimination on digital platforms, ageism has received less attention This article analyzes some tools that are frequently embedded on digital platforms from an old-age perspective, in order to increase awareness of the different ways in which ageism works We will firstly look at how innovation teams, following homophilic patterns, disregard older people Secondly, we will show how ageism tends to be amplified by the methods often used on digital platforms And thirdly, we will show how corporate values contradict the usability issues that mainly affect people with a low level of (digital) skills, which is more common among older people Counterbalancing the abusive power control of the corporations behind digital platforms and compensating for the underrepresentation of groups in less favorable situations could help to tackle such discrimination