Case studies of personalized learning

Deliverable 4.1, Literature review of personalised learning and the Cloud, started with an evaluation and synthesis of the definitions of personalized learning, followed by an analysis of how this is implemented in a method (e-learning vs. i-learning, m-learning and u-learning), learning approach and the appropriate didactic process, based on adapted didactic theories. From this research a list of criteria was created needed to implement personalised learning onto the learner of the future. This list of criteria is the basis for the analysis of all case studies investigated. – as well to the learning process as the learning place. In total 60 case studies (all 59 case studies mentioned in D6.4 Education on the Cloud 2015 + one extra) were analysed. The case studies were compared with the list of criteria, and a score was calculated. As a result, the best examples could be retained. On average most case studies were good on: taking different learning methods into account, interactivity and accessibility and usability of learning materials for everyone. All had a real formal education content, thus aiming at the core-curriculum, valuing previous knowledge, competences, life and work skills, also informal. Also the availability of an instructor / tutor or other network of peers, experts and teachers to guide and support the learning is common. On the other hand, most case studies lack diagnostics tests as well at the start (diagnostic entry test), during the personalized learning trajectory and at the end (assessment at the end). Also most do not include non-formal and informal learning aspects. And the ownership of personalized learning is not in the hands of the learner. Five of the 60 case studies can as a result be considered as very good examples of real personalized learning

A literature review of personalized learning and the Cloud

In order to provide effective application of the Cloud in education it is essential to know how the learning should and could – if needed – be adapted. In this respect the concept of ‘personalising learning’ is frequently used. But what exactly is personalising learning. And how can it be implemented in using the cloud? The aim of WG3 i-Learner of the School on the Cloud network is to investigate this from the point of view of the learner, whereas WG2 i-Teacher looks on the role of the educators, and WG4 i-Future on the technology. The document has two parts: - The first part starts with an evaluation and synthesis of the definitions of personalized learning (Ch. 3), followed by an analysis of how this is implemented in learning style (e-learning vs. i-learning, m-learning and u-learning, Ch. 4) and learning approach (Ch. 5). To implement this an appropriate pedagogy (Ch. 6) is needed. - The second part is an attempt on how to implement this onto the learner of the future (Ch. 7), as well to the learning process and to the learning place. Recommendations are made in Ch. 8

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this work we jointly model and integrate extensive multi-omics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, re-analysis of the ILCCO data highlights the impact of highly-associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Longitudinal Physique Changes Among Healthy White Veterans at Boston

This study presents the results of a second round of measurements taken on a group of 1813 medically screened, healthy, white veterans living in the Boston area five years after their initial measurements. The men are grouped in five year cohorts from below 30 to above 70 years of age. As we can now observe the characteristic changes with age for each five year cohort over this age range, it is possible to partition the observed cross-sectional differences in cohort averages between those differences most likely due to changes with age and those likely due to secular differences established before entrance into the study. Of the 29 measurements found to be reliable, all (save possibly chest breadth and depth) have been affected by the secular trend toward larger size, including measurements taken on the head. Changes with age are smaller in magnitude. Individual shrinkage in the vertebral column is detectable as early as the fifth decade of life. A number of measurements increase throughout life. Weight increases both longitudinally with age, and from cohort to cohort (secular trend) until the beginning of the sixth decade. There are no significant decreases in weight with old age. Men are currently gaining weight at a rate which suggests that between their middle 20’s and early 50’s, they may expect on an average to gain 8 kilograms so that the youngest current participants in the study can expect to have an average weight of about 89 kilograms by the time they are 50 years old