How to improve students’ experience in blending learning? Evidence from the perceptions of students in a Postgraduate Master’s Degree

[EN] This paper examines the perceptions of a group of students of a Postgraduate Master’s Degree in Cosmetics Industry at the Universitat de València, delivered with a blended learning modality, in relation to their experience in face-to-face learning and differentiating between those with or without a previous background in a remote online learning environment, with the added purpose of identifying strategies to enhance that experience, while offering further evidence for scholars, educators and institutions in this field. To this end, a survey with open questions devised ad hoc leaning on our literature review was submitted to a group of 114 students of the Master’s Degree in the period 2017-2020. Students were enquired about the pros and cons of their blended learning experience in relation to the traditional face-to-face learning, and which modality they would choose next time if both were offered, only considering the achievement, experience and satisfaction, regardless of the price. 77 students of our initial sample participated in the questionnaire, 38 of them without previous experience in blended or distance learning. The results show a certain predilection for face-to-face learning, especially in the group of newbies in blended or distance learning. They highlight how they miss a closer interaction with their peers and professors and the difficulties to assimilate certain content, while appraising the flexibility, autonomy, and the self-pace of the blended learning modality. Correspondingly, students with experience in remote online education settings generally show a better predisposal and find fewer disadvantages in blended learning. This suggests that the factor of experience and adaptation to new tools and methods improves student perception and confidence and shapes their preferences, with a foreseeable growing acceptance of blended learning in the future. Finally, the outcome allows us to define a series of strategies to improve the achievement, experience, and satisfaction of students in this learning context.Garcia-Ortega, B.; Galan-Cubillo, J. (2021). How to improve students’ experience in blending learning? Evidence from the perceptions of students in a Postgraduate Master’s Degree. WPOM-Working Papers on Operations Management. 12(2):1-15. https://doi.org/10.4995/wpom.15677OJS115122Al-Khanjari, Z. A. S. (2018). Applying online learning in software engineering education. In Computer Systems and Software Engineering: Concepts, Methodologies, Tools, and Applications (pp. 217-231). IGI Global. https://doi.org/10.4018/978-1-5225-3923-0.ch010Angeli, C., Valanides, N., & Bonk, C. J. (2003). 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Computers & education, 56(3), 818-826. https://doi.org/10.1016/j.compedu.2010.10.023Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1-47. https://doi.org/10.1177/016146811311500307McGEE, E., & Poojary, P. (2020). Exploring Blended Learning Relationships in Higher Education Using a Systems-based Framework. Turkish Online Journal of Distance Education, 21(4), 1-13. https://doi.org/10.17718/tojde.803343Kemp, N. (2020). University students' perceived effort and learning in face-to-face and online classes. Journal of Applied Learning and Teaching, 3(1), 69-77. https://doi.org/10.37074/jalt.2020.3.s1.14Krause, K. (2007) "Griffith University blended learning strategy," Document number2008/0016252, 2007.Norberg, A., Dziuban, C. D., & Moskal, P. D. (2011). A time‐based blended learning model. On the Horizon. https://doi.org/10.1108/10748121111163913Poon, J. (2013). Blended learning: An institutional approach for enhancing students' learning experiences. Journal of online learning and teaching, 9(2), 271-288.Rafiola, R., Setyosari, P., Radjah, C., & Ramli, M. (2020). The Effect of Learning Motivation, Self-Efficacy, and Blended Learning on Students' Achievement in The Industrial Revolution 4.0. International Journal of Emerging Technologies in Learning (iJET), 15(8), 71-82. https://doi.org/10.3991/ijet.v15i08.12525Rovai, A. P., & Downey, J. R. (2010). Why some distance education programs fail while others succeed in a global environment. The Internet and Higher Education, 13(3), 141-147. https://doi.org/10.1016/j.iheduc.2009.07.001Rovai, A. P., & Jordan, H. M. (2004). Blended learning and sense of community: A comparative analysis with traditional and fully online graduate courses. International Review of Research in Open and Distributed Learning, 5(2), 1-13. https://doi.org/10.19173/irrodl.v5i2.192Sayed, M. (2013). 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Fault Management in Manned Spacecraft: From Design to Operations

The contents include: 1) Fault Management dimensions; 2) Fault Management analysis; 3) Real-time Fault Management; 4) Learning from Real Failures; 5) Evolution of on-board Fault Management Approach

The match between molecular subtypes, histology and microenvironment of pancreatic cancer and its relevance for chemoresistance

In the last decade, several studies based on whole transcriptomic and genomic analyses of pancreatic tumors and their stroma have come to light to supplement histopathological stratification of pancreatic cancers with a molecular point-of-view. Three main molecular studies: Collisson et al. 2011, Moffitt et al. 2015 and Bailey et al. 2016 have found specific gene signatures, which identify different molecular subtypes of pancreatic cancer and provide a comprehensive stratification for both a personalized treatment or to identify potential druggable targets. However, the routine clinical management of pancreatic cancer does not consider a broad molecular analysis of each patient, due probably to the lack of target therapies for this tumor. Therefore, the current treatment decision is taken based on patients’ clinicopathological features and performance status. Histopathological evaluation of tumor samples could reveal many other attributes not only from tumor cells but also from their microenvironment specially about the presence of pancreatic stellate cells, regulatory T cells, tumor-associated macrophages, myeloid derived suppressor cells and extracellular matrix structure. In the present article, we revise the four molecular subtypes proposed by Bailey et al. and associate each subtype with other reported molecular subtypes. Moreover, we provide for each subtype a potential description of the tumor microenvironment that may influence treatment response according to the gene expression profile, the mutational landscape and their associated histolog

Advanced Technologies for Future Spacecraft Cockpits and Space-based Control Centers

The National Aeronautics and Space Administration (NASA) is embarking on a new era of Space Exploration, aimed at sending crewed spacecraft beyond Low Earth Orbit (LEO), in medium and long duration missions to the Lunar surface, Mars and beyond. The challenges of such missions are significant and will require new technologies and paradigms in vehicle design and mission operations. Current roles and responsibilities of spacecraft systems, crew and the flight control team, for example, may not be sustainable when real-time support is not assured due to distance-induced communication lags, radio blackouts, equipment failures, or other unexpected factors. Therefore, technologies and applications that enable greater Systems and Mission Management capabilities on-board the space-based system will be necessary to reduce the dependency on real-time critical Earth-based support. The focus of this paper is in such technologies that will be required to bring advance Systems and Mission Management capabilities to space-based environments where the crew will be required to manage both the systems performance and mission execution without dependence on the ground. We refer to this concept as autonomy. Environments that require high levels of autonomy include the cockpits of future spacecraft such as the Mars Exploration Vehicle, and space-based control centers such as a Lunar Base Command and Control Center. Furthermore, this paper will evaluate the requirements, available technology, and roadmap to enable full operational implementation of onboard System Health Management, Mission Planning/re-planning, Autonomous Task/Command Execution, and Human Computer Interface applications. The technology topics covered by the paper include enabling technology to perform Intelligent Caution and Warning, where the systems provides directly actionable data for human understanding and response to failures, task automation applications that automate nominal and Off-nominal task execution based on human input or integrated health state-derived conditions. Shifting from Systems to Mission Management functions, we discuss the role of automated planning applications (tactical planning) on-board, which receive data from the other cockpit automation systems and evaluate the mission plan against the dynamic systems and mission states and events, to provide the crew with capabilities that enable them to understand, change, and manage the timeline of their mission. Lastly, we discuss the role of advanced human interface technologies that organize and provide the system md mission information to the crew in ways that maximize their situational awareness and ability to provide oversight and control of aLl the automated data and functions

Functional Glyconanomaterials

Nanotechnology provides a new array of techniques and platforms to study biological processes including glycosystems [...

Towards a Functional Explanation of the Connectivity LGN - V1

The principles behind the connectivity between LGN and V1 are not well understood. Models have to explain two basic experimental trends: (i) the combination of thalamic responses is local and it gives rise to a variety of oriented Gabor-like receptive felds in V1 [1], and (ii) these filters are spatially organized in orientation maps [2]. Competing explanations of orientation maps use purely geometrical arguments such as optimal wiring or packing from LGN [3-5], but they make no explicit reference to visual function. On the other hand, explanations based on func- tional arguments such as maximum information transference (infomax) [6,7] usually neglect a potential contribution from LGN local circuitry. In this work we explore the abil- ity of the conventional functional arguments (infomax and variants), to derive both trends simultaneously assuming a plausible sampling model linking the retina to the LGN [8], as opposed to previous attempts operating from the retina. Consistently with other aspects of human vi- sion [14-16], additional constraints should be added to plain infomax to understand the second trend of the LGN-V1 con- nectivity. Possibilities include energy budget [11], wiring constraints [8], or error minimization in noisy systems, ei- ther linear [16] or nonlinear [14, 15]. In particular, consideration of high noise (neglected here) would favor the redundancy in the prediction (which would be required to match the relations between spatially neighbor neurons in the same orientation domain)

Calcium Sulfoaluminate Cement Clinkering

Non peer reviewedPublisher PD

Pacto fiscal y autonomía concejil

UIDB/00749/2020 UIDP/00749/2020El objetivo de este trabajo es describir la relación entre la estabilización del sistema fiscal castellano y la lucha por las rentas señoriales a través de los pleitos y las concordias que se dieron en una etapa de clara expansión de la economía en la corona de Castilla (c.a 1450-1550). El recurso a la justicia regia y la concordia fiscal es, en nuestra opinión, un excelente índice para mostrar la funcionalidad del sistema en esa época y la integración de la nobleza en el estado, frente a las resistencias de los vasallos señoriales. PDFpublishersversionpublishe

Derivatives and Inverse of a Linear-Nonlinear Multi-Layer Spatial Vision Model

Analyzing the mathematical properties of perceptually meaningful linear-nonlinear transforms is interesting because this computation is at the core of many vision models. Here we make such analysis in detail using a specific model [Malo & Simoncelli, SPIE Human Vision Electr. Imag. 2015] which is illustrative because it consists of a cascade of standard linear-nonlinear modules. The interest of the analytic results and the numerical methods involved transcend the particular model because of the ubiquity of the linear-nonlinear structure. Here we extend [Malo&Simoncelli 15] by considering 4 layers: (1) linear spectral integration and nonlinear brightness response, (2) definition of local contrast by using linear filters and divisive normalization, (3) linear CSF filter and nonlinear local con- trast masking, and (4) linear wavelet-like decomposition and nonlinear divisive normalization to account for orientation and scale-dependent masking. The extra layers were measured using Maximum Differentiation [Malo et al. VSS 2016]. First, we describe the general architecture using a unified notation in which every module is composed by isomorphic linear and nonlinear transforms. The chain-rule is interesting to simplify the analysis of systems with this modular architecture, and invertibility is related to the non-singularity of the Jacobian matrices. Second, we consider the details of the four layers in our particular model, and how they improve the original version of the model. Third, we explicitly list the derivatives of every module, which are relevant for the definition of perceptual distances, perceptual gradient descent, and characterization of the deformation of space. Fourth, we address the inverse, and we find different analytical and numerical problems in each specific module. Solutions are proposed for all of them. Finally, we describe through examples how to use the toolbox to apply and check the above theory. In summary, the formulation and toolbox are ready to explore the geometric and perceptual issues addressed in the introductory section (giving all the technical information that was missing in [Malo&Simoncelli 15])