Open University of Catalonia

About: Open University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Collaborative learning & Educational technology. The organization has 1943 authors who have published 4646 publications receiving 64200 citations. The organization is also known as: Universitat Oberta de Catalunya & UOC.

The Concept of Flow in Online Consumer Behavior

Abstract: The concept of flow has become increasingly relevant in the field of online navigation and specifically in explaining consumer behaviour in electronic markets. Not only can it be used to characterize the user’s interactive relationship with virtual environments, but it can also have a positive and desirable impact on the individuals’ consumption experiences and also on the performance of the companies’ websites which induce flow state in their customers. The purpose of this conceptual article is to analyse in-depth the concept of flow and elucidate its relevance to the context of online consumer behaviour. It contains a comprehensive and critical analysis of the literature and highlights the potential for businesses to generate flow experiences in their online environments. It also identifies the ambiguities and inconsistencies regarding the conceptualisation and operationalisation of flow in online commercial websites. Finally, we stress the importance of conducting further research in this area, with particular focus on the role of flow within the prevailing social web context.

Brecha digital de género: Una revisión y una propuesta

Abstract: In this article we offer a review of national and international studies of the gender digital divide, centering specifically in the research on women underrepresentation in the educational and professional field of new information and communication technologies (ICT). We also briefly characterize the interventions and actions developed in this field whose aim has been to counter this situation. Afterwards, we evaluate critically the theoretical assumptions of the reviewed investigations and undertaken actions, too centered in considering this question "a women's problem". Finally, we offer some recommendations to articulate a gendered perspective, which would allow us to progress in the knowledge of the concrete causes and the possible solutions of the gender digital divide without essentializing neither gender nor technology.

FRAGGLE: A FRamework for AGile Gamification of Learning Experiences

Abstract: Student's motivation difficulties are present in learning scenarios, even university grades. Between the techniques being applied by instructors to counteract this fact, we can find gamification techniques. Unfortunately, sometimes the result is the appearance of unwanted effects, which is a very common symptom of poor designs caused by the lack of proven methodologies for its purpose. This paper presents our first approach of a conceptual framework for designing the gamification of learning experiences. It is focused in higher education and based on the use of the Agile methodologies in order to obtain a fast Minimum Viable Product MVP ready for testing. Its aims are to apply different techniques all the way down to the lowest levels of abstraction through its step-by-step process.

A roadmap to using randomization in clinical trials.

Abstract: Background Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial. Methods We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. Results Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size. Conclusions The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics

Abstract: In the context of simulation-based optimisation, this paper reviews recent work related to the role of metaheuristics, matheuristics (combinations of exact optimisation methods with metaheuristics), simheuristics (hybridisation of simulation with metaheuristics), biased-randomised heuristics for ‘agile’ optimisation via parallel computing, and learnheuristics (combination of statistical/machine learning with metaheuristics) to deal with NP-hard and large-scale optimisation problems in areas such as transport and logistics, manufacturing and production, smart cities, telecommunication networks, finance and insurance, sustainable energy consumption, health care, military and defence, e-marketing, or bioinformatics. The manuscript provides the main related concepts and updated references that illustrate the applications of these hybrid optimisation–simulation–learning approaches in solving rich and real-life challenges under dynamic and uncertainty scenarios. A numerical analysis is also included to illustrate the benefits that these approaches can offer across different application fields. Finally, this work concludes by highlighting open research lines on the combination of these methodologies to extend the concept of simulation-based optimisation.