The influence of learner characteristics on degree and type of participation in a CSCL environment

Computer-Supported Collaborative Learning (CSCL) is often presented as a promising learning method. However, it is also facing some new challenges. Apart from answering the question of whether or not working with CSCL generates satisfying learning outcomes, it is important to determine whether or not all participants profit from collaboration, with the computer as a means of communication. This paper describes the implementation and effects of an experimental program in 5 classes with a total of 120 students in elementary education who, in groups of four, engaged in Knowledge Forum discussion tasks on the subject of healthy eating. The study explores whether or not differences occur in the participation of students who differ in gender, sociocultural background and ability, and whether or not computer skills, computer attitudes, comprehensive reading scores and popularity with classmates are related to student participation. Students’ participation in this CSCL environment appears to be dependent on a number of learner characteristics. Girls contribute more words to the discussions than boys do and are more dependent on their computer skills in this production. Students who are good at comprehensive reading also contribute more words. Popularity among classmates appears to influence the degree of participation further. We also found indications that students with immigrant parents write fewer contributions than those whose parents are not immigrants

Does Reviewing Lead to Better Learning and Decision Making? Answers from a Randomized Stock Market Experiment

status: publishe

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping

Activation and modulation of antiviral and apoptotic genes in pigs infected with classical swine fever viruses of high, moderate or low virulence

The immune response to CSFV and the strategies of this virus to evade and suppress the pigs’ immune system are still poorly understood. Therefore, we investigated the transcriptional response in the tonsils, median retropharyngeal lymph node (MRLN), and spleen of pigs infected with CSFV strains of similar origin with high, moderate, and low virulence. Using a porcine spleen/intestinal cDNA microarray, expression levels in RNA pools prepared from infected tissue at 3 dpi (three pigs per virus strain) were compared to levels in pools prepared from uninfected homologue tissues (nine pigs). A total of 44 genes were found to be differentially expressed. The genes were functionally clustered in six groups: innate and adaptive immune response, interferon-regulated genes, apoptosis, ubiquitin-mediated proteolysis, oxidative phosphorylation and cytoskeleton. Significant up-regulation of three IFN-γ-induced genes in the MRLNs of pigs infected with the low virulence strain was the only clear qualitative difference in gene expression observed between the strains with high, moderate and low virulence. Real-time PCR analysis of four response genes in all individual samples largely confirmed the microarray data at 3 dpi. Additional PCR analysis of infected tonsil, MRLN, and spleen samples collected at 7 and 10 dpi indicated that the strong induction of expression of the antiviral response genes chemokine CXCL10 and 2′–5′ oligoadenylate synthetase 2, and of the TNF-related apoptosis-inducing ligand (TRAIL) gene at 3 dpi, decreased to lower levels at 7 and 10 dpi. For the highly and moderately virulent strains, this decrease in antiviral and apoptotic gene expression coincided with higher levels of virus in these immune tissues

Clustering of classical swine fever virus isolates by codon pair bias

<p>Abstract</p> <p>Background</p> <p>The genetic code consists of non-random usage of synonymous codons for the same amino acids, termed codon bias or codon usage. Codon juxtaposition is also non-random, referred to as codon context bias or codon pair bias. The codon and codon pair bias vary among different organisms, as well as with viruses. Reasons for these differences are not completely understood. For classical swine fever virus (CSFV), it was suggested that the synonymous codon usage does not significantly influence virulence, but the relationship between variations in codon pair usage and CSFV virulence is unknown. Virulence can be related to the fitness of a virus: Differences in codon pair usage influence genome translation efficiency, which may in turn relate to the fitness of a virus. Accordingly, the potential of the codon pair bias for clustering CSFV isolates into classes of different virulence was investigated.</p> <p>Results</p> <p>The complete genomic sequences encoding the viral polyprotein of 52 different CSFV isolates were analyzed. This included 49 sequences from the GenBank database (NCBI) and three newly sequenced genomes. The codon usage did not differ among isolates of different virulence or genotype. In contrast, a clustering of isolates based on their codon pair bias was observed, clearly discriminating highly virulent isolates and vaccine strains on one side from moderately virulent strains on the other side. However, phylogenetic trees based on the codon pair bias and on the primary nucleotide sequence resulted in a very similar genotype distribution.</p> <p>Conclusion</p> <p>Clustering of CSFV genomes based on their codon pair bias correlate with the genotype rather than with the virulence of the isolates.</p

Phenotypes of Non-Attached Pseudomonas aeruginosa Aggregates Resemble Surface Attached Biofilm

For a chronic infection to be established, bacteria must be able to cope with hostile conditions such as low iron levels, oxidative stress, and clearance by the host defense, as well as antibiotic treatment. It is generally accepted that biofilm formation facilitates tolerance to these adverse conditions. However, microscopic investigations of samples isolated from sites of chronic infections seem to suggest that some bacteria do not need to be attached to surfaces in order to establish chronic infections. In this study we employed scanning electron microscopy, confocal laser scanning microscopy, RT-PCR as well as traditional culturing techniques to study the properties of Pseudomonas aeruginosa aggregates. We found that non-attached aggregates from stationary-phase cultures have comparable growth rates to surface attached biofilms. The growth rate estimations indicated that, independently of age, both aggregates and flow-cell biofilm had the same slow growth rate as a stationary phase shaking cultures. Internal structures of the aggregates matrix components and their capacity to survive otherwise lethal treatments with antibiotics (referred to as tolerance) and resistance to phagocytes were also found to be strikingly similar to flow-cell biofilms. Our data indicate that the tolerance of both biofilms and non-attached aggregates towards antibiotics is reversible by physical disruption. We provide evidence that the antibiotic tolerance is likely to be dependent on both the physiological states of the aggregates and particular matrix components. Bacterial surface-attachment and subsequent biofilm formation are considered hallmarks of the capacity of microbes to cause persistent infections. We have observed non-attached aggregates in the lungs of cystic fibrosis patients; otitis media; soft tissue fillers and non-healing wounds, and we propose that aggregated cells exhibit enhanced survival in the hostile host environment, compared with non-aggregated bacterial populations