Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Producción CientíficaPeer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples.European Union’s Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 793317)Ministerio de Ciencia, Innovación y Universidades (projects TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-R)Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Understanding student behavior and perceptions toward earning badges in a gamified MOOC

Producción CientíficaDespite the advantages of MOOCs, such as the open and free access to education, these courses are criticized for students’ lack of motivation and their high dropout rates. Gamification is a technique used to increase student motivation and engagement in small-scale educational contexts. However, the effects of gamification on student engagement have been scarcely explored in MOOC environments, and the findings so far are inconsistent. To address this gap, this research work examines the students’ behavior toward earning badges and how it relates to their engagement in a gamified MOOC. According to the results, the behaviors toward badges of the active students were generally positive and significantly correlated with other variables measuring their engagement (e.g., pageviews, submitted tasks, forum posts), although this positive behavior seems to decrease throughout the course. Additionally, students that reported high motivation by badges at the end of the course showed a higher engagement level than those that were not appealed by badges.European Regional Development Fund, under project grants TIN2014-53199-C3-2-R and TIN2017-85179-C3-2-RJunta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA082U16 and VA257P18)European Commission, under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Producción CientíficaThis paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past massive open online course, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, such as the target variable to predict and the practical constraints to consider. Two predictive models were built: LD-specific model (with features based on the LD and pedagogical intentions), and a generic model (with cumulative features, not informed by the LD). Although the LD-specific predictive model did not outperform the generic one, some LD-driven features were powerful. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor’s opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the problems in the LD. The implications for improving the LD are discussed.Ministerio de Ciencia e Innovación (Proyect grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA257P18)European Commission under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KAEuropean Union’s Horizon 2020 under the Marie Sklodowska-Curie grant agreement 79331

Macroscopic transport by synthetic molecular machines

Nature uses molecular motors and machines in virtually every significant biological process, but demonstrating that simpler artificial structures operating through the same gross mechanisms can be interfaced with—and perform physical tasks in—the macroscopic world represents a significant hurdle for molecular nanotechnology. Here we describe a wholly synthetic molecular system that converts an external energy source (light) into biased brownian motion to transport a macroscopic cargo and do measurable work. The millimetre-scale directional transport of a liquid on a surface is achieved by using the biased brownian motion of stimuli-responsive rotaxanes (‘molecular shuttles’) to expose or conceal fluoroalkane residues and thereby modify surface tension. The collective operation of a monolayer of the molecular shuttles is sufficient to power the movement of a microlitre droplet of diiodomethane up a twelve-degree incline.

Two rapid assays for screening of patulin biodegradation

Artículo sobre distintos ensayos para comprobar la biodegradación de la patulinaThe mycotoxin patulin is produced by the blue mould pathogen Penicillium expansum in rotting apples during postharvest storage. Patulin is toxic to a wide range of organisms, including humans, animals, fungi and bacteria. Wash water from apple packing and processing houses often harbours patulin and fungal spores, which can contaminate the environment. Ubiquitous epiphytic yeasts, such as Rhodosporidium kratochvilovae strain LS11 which is a biocontrol agent of P. expansum in apples, have the capacity to resist the toxicity of patulin and to biodegrade it. Two non-toxic products are formed. One is desoxypatulinic acid. The aim of the work was to develop rapid, high-throughput bioassays for monitoring patulin degradation in multiple samples. Escherichia coli was highly sensitive to patulin, but insensitive to desoxypatulinic acid. This was utilized to develop a detection test for patulin, replacing time-consuming thin layer chromatography or high-performance liquid chromatography. Two assays for patulin degradation were developed, one in liquid medium and the other in semi-solid medium. Both assays allow the contemporary screening of a large number of samples. The liquid medium assay utilizes 96-well microtiter plates and was optimized for using a minimum of patulin. The semisolid medium assay has the added advantage of slowing down the biodegradation, which allows the study and isolation of transient degradation products. The two assays are complementary and have several areas of utilization, from screening a bank of microorganisms for biodegradation ability to the study of biodegradation pathways

CD Maps-Dynamic Profiling of CD1-CD100 Surface Expression on Human Leukocyte and Lymphocyte Subsets

CD molecules are surface molecules expressed on cells of the immune system that play key roles in immune cell-cell communication and sensing the microenvironment. These molecules are essential markers for the identification and isolation of leukocytes and lymphocyte subsets. Here, we present the results of the first phase of the CD Maps study, mapping the expression of CD1-CD100 (n = 110) on 47 immune cell subsets from blood, thymus, and tonsil using an eight-color standardized EuroFlow approach and quantification of expression. The resulting dataset included median antibody binding capacities (ABCs) and percentage of positivity for all markers on all subsets and was developed into an interactive CD Maps web resource. Using the resource, we examined differentially expressed proteins between granulocyte, monocyte, and dendritic cell subsets, and profiled dynamic expression of markers during thymocyte differentiation, T-cell maturation, and between functionally distinct B-cell subset clusters. The CD Maps resource will serve as a benchmark of antibody reactivities ensuring improved reproducibility of flow cytometry-based research. Moreover, it will provide a full picture of the surfaceome of human immune cells and serves as a useful platform to increase our understanding of leukocyte biology, as well as to facilitate the identification of new biomarkers and therapeutic targets of immunological and hematological diseases

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Why and how might genetic and phylogenetic diversity be reflected in the identification of key biodiversity areas?

‘Key biodiversity areas' are defined as sites contributing significantly to the global persistence of biodiversity. The identification of these sites builds from existing approaches based on measures of species and ecosystem diversity and process. Here, we therefore build from the work of Sgró et al. (2011 Evol. Appl. 4, 326–337. (doi:10.1111/j.1752-4571.2010.00157.x)) to extend a framework for how components of genetic diversity might be considered in the identification of key biodiversity areas. We make three recommendations to inform the ongoing process of consolidating a key biodiversity areas standard: (i) thresholds for the threatened species criterion currently consider a site's share of a threatened species' population; expand these to include the proportion of the species' genetic diversity unique to a site; (ii) expand criterion for ‘threatened species' to consider ‘threatened taxa’ and (iii) expand the centre of endemism criterion to identify as key biodiversity areas those sites holding a threshold proportion of the compositional or phylogenetic diversity of species (within a taxonomic group) whose restricted ranges collectively define a centre of endemism. We also recommend consideration of occurrence of EDGE species (i.e. threatened phylogenetic diversity) in key biodiversity areas to prioritize species-specific conservation actions among sites

New Implications on Genomic Adaptation Derived from the Helicobacter pylori Genome Comparison

BACKGROUND: Helicobacter pylori has a reduced genome and lives in a tough environment for long-term persistence. It evolved with its particular characteristics for biological adaptation. Because several H. pylori genome sequences are available, comparative analysis could help to better understand genomic adaptation of this particular bacterium. PRINCIPAL FINDINGS: We analyzed nine H. pylori genomes with emphasis on microevolution from a different perspective. Inversion was an important factor to shape the genome structure. Illegitimate recombination not only led to genomic inversion but also inverted fragment duplication, both of which contributed to the creation of new genes and gene family, and further, homological recombination contributed to events of inversion. Based on the information of genomic rearrangement, the first genome scaffold structure of H. pylori last common ancestor was produced. The core genome consists of 1186 genes, of which 22 genes could particularly adapt to human stomach niche. H. pylori contains high proportion of pseudogenes whose genesis was principally caused by homopolynucleotide (HPN) mutations. Such mutations are reversible and facilitate the control of gene expression through the change of DNA structure. The reversible mutations and a quasi-panmictic feature could allow such genes or gene fragments frequently transferred within or between populations. Hence, pseudogenes could be a reservoir of adaptation materials and the HPN mutations could be favorable to H. pylori adaptation, leading to HPN accumulation on the genomes, which corresponds to a special feature of Helicobacter species: extremely high HPN composition of genome. CONCLUSION: Our research demonstrated that both genome content and structure of H. pylori have been highly adapted to its particular life style