Por qué mi curso no es un MOOC o El fracaso televisivo de un actor teatral

Este trabajo es una reflexión sobre el proceso de transformación del curso tradicional presencial a un MOOC (Massive Open Online Course - Curso en línea, masivo y abierto). Se analizan las nuevas demandas del alumnado, las diferencias en función de los medios necesarios y el esfuerzo de adaptación que ello requiere por parte del profesor. Se utiliza como símil la comparación del profesor con un actor teatral, y la exigencia de nuevas técnicas interpretativas para actuar en otros medios, como el cine o la TV. Del mismo modo, se requiere una adaptación del papel docente para difundir la educación en nuevos entornos, como los que proporcionan los MOOCs. Por tanto, el cambio de medio abre nuevas expectativas al espectador, pero lleva también consigo el conoci-miento de esos medios, la adaptación del contenido a transmitir y la adopción de nuevas técnicas interpreta-tivas de los actores. Asumir estos tres niveles de cambio no es sencillo, por lo que se analizan los aspectos que debemos trabajar para evitar el fracaso del docente tradicional en entornos de tipo MOOC.This work is a thinking about the process of shifting a traditional classroom course into a MOOC (Massive Open Online Course). We analyze the new demands of students, the differences in terms of the necessary resources and the adaptation effort required to the teacher. We will use a metaphor comparing the teacher with the theater actor, as opposed to the demand for the filmmaker or TV director with very different skills. Similarly, an adaptation of the teaching role is required to spread education in new environments as provided by MOOCs. Therefore, the change means new possibilities for the viewer, but also entails a knowledge of the new medium, adapting the content to be transmitted and the adoption of new techniques of interpretation. Taking these three levels of change is not easy, so that the issues that we must work to prevent the failure of traditional teaching environments applied into new MOOCs are analyzed

Massive open online courses and completion rates: does academic readiness and its factors influence completion rates in MOOCs?

With the increase in the cost of an education and the flat employment rate, many institutions and students are looking to online learning to solve this academic dilemma. Online education is thought to be a low-cost academic alternative to brick and mortar courses. Massive Open Online Courses (MOOCs) goals include issues of equity in higher education, the rising costs of a college education, and funding concerns. MOOCs can be taken from anywhere as long as the participant has a computer and access to the Internet is available. Also, traditional MOOCs do not require any financial commitment and do not have academic prerequisites or an admissions process. Completion rates among learners taking MOOCs are low, begging the question of whether they actually address matters of escalating college costs and higher education equity. The purpose of this study is to explore whether academic readiness in the context of the likelihood the learner completing the course. This study focuses on one component of the many factors in MOOCs - the likelihood of course completion and academic readiness. Academic readiness in MOOCs is not a requirement, but a component that may determine whether a learner has the tools needed to complete a MOOC. Academic readiness suggests a level of knowledge and cognitive abilities necessary to understand the course content and to navigate the course technologically. Theories addressing structural elements within MOOCs include Clow's funnel of participation, behaviorism, and constructivism. Of these theories, constructivism provides the theoretical framework for understanding learners' abilities and willingness to learn in the study. This quantitative study attempts to evaluate the likelihood of course completion and the factors that may influence these outcomes using secondary data from Duke's MOOC pre- and post-course surveys. Logistic regression analysis with the dependent variable (a learner completes a Duke's MOOCs) and the independent variables (academic readiness and its factors -- college degree; age; race; gender; previous experience with course subject, course level -- beginner, intermediate; or advanced; and STEM or non-STEM) will be used to estimate the likelihood that these variables will encourage learners to complete MOOCs or understand why learners do not

Social factors influencing the success of adult learners: examining the use of online learning programmes at a higher education institution in South Africa

This study presents a quantitative investigation of the influence of various social factors – including finances, secondary school attended, resources available, culture, and family support – on the perceptions of success (in terms of academic performance and skills gained) of adult learners who are using online learning as the primary educational medium. The research was conducted with 100 students of The International Hotel School in South Africa. A quantitative research methodology was followed and a survey questionnaire was used as the data collection method. The data from closed-ended questions was analysed using Statistical Analysis System (SAS), with the data from open-ended questions being used to aid in the interpretation of the information organised quantitatively. Findings suggest that certain social factor constructs namely: finances, secondary school preparation for tertiary education, and internet accessibility, significantly impact the perceptions the students have on being successful in online learning. Some recommendations that spring from the study are to provide more funding to students, better access to more suitable resources and providing students with unlimited access to the internet for longer periods of time. It is also recommended that a follow-up study with a larger and more varied sample (possibly including public sector tertiary education students), and more questionnaire items per social factor is necessary to cast further light on the impact of social factors on adult students’ online learning experiences.Educational StudiesM. Ed. (With specialisation in Adult Education

Mathematical Modeling of Viral Evolution and Epidemiology

Phylogenetic trees can be used to study the evolution of any sequence that evolves, including viruses. In a viral epidemic, the history of transmission events defines constraints on the evolutionary history of the viral population. The spread of many viruses is driven by social and sexual networks, and because of the relationship between their evolutionary and transmission histories, phylogenetic inference from viral sequences can be used to improve the inference of patterns of the epidemic, which in turn may be able to enhance epidemiological intervention. The simultaneous simulation of viral transmission networks, phylogenetic trees, and sequences can provide a method to observe the effects of virus model parameters on the epidemic as well as to study the accuracies and errors of transmission inference tools, but the success of such simulations relies on the existence of appropriate models. Further, the development of massively-scalable tools to analyze ultra-large datasets of viral sequences can aid epidemiologists in the real-time surveillance of the spread of disease. To enable viral epidemic simulation analyses, I developed FAVITES: a novel framework to simulate viral transmission networks, phylogenetic trees, and sequences, and I used FAVITES to study the effects of model parameters on epidemic outcomes. In an effort to better capture the unbalanced topologies commonly observed in retroviral phylogenies, I developed a novel evolutionary model (dual-birth), derived probabilistic distributions and theoretical expectations of trees sampled under the model, developed an approach to estimate model parameters given real data, and used the model to analyze Alu retrotransposons in the human genome. In order to potentially aid public health officials, I developed a scalable and non-parametric phylogenetic method of viral transmission risk prioritization, which I evaluated against current best-practice methods via simulation and real data. Lastly, I contributed to Bioinformatics education by developing multiple publicly-accessible adaptive online interactive texts