Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses

With the rapid advancements in technology, Massive Open Online Courses (MOOCs) have become the most popular form of online educational delivery, largely due to the removal of geographical and financial barriers for participants. A large number of learners globally enrol in such courses. Despite the flexible accessibility, results indicate that the completion rate is quite low. Educational Data Mining and Learning Analytics are emerging fields of research that aim to enhance the delivery of education through the application of various statistical and machine learning approaches. An extensive literature survey indicates that no significant research is available within the area of MOOC data analysis, in particular considering the behavioural patterns of users. In this paper, therefore, two sets of features, based on learner behavioural patterns, were compared in terms of their suitability for predicting the course outcome of learners participating in MOOCs. Our Exploratory Data Analysis demonstrates that there is strong correlation between click steam actions and successful learner outcomes. Various Machine Learning algorithms have been applied to enhance the accuracy of classifier models. Simulation results from our investigation have shown that Random Forest achieved viable performance for our prediction problem, obtaining the highest performance of the models tested. Conversely, Linear Discriminant Analysis achieved the lowest relative performance, though represented only a marginal reduction in performance relative to the Random Forest

Accurate, precise modeling of cell proliferation kinetics from time-lapse imaging and automated image analysis of agar yeast culture arrays

BACKGROUND: Genome-wide mutant strain collections have increased demand for high throughput cellular phenotyping (HTCP). For example, investigators use HTCP to investigate interactions between gene deletion mutations and additional chemical or genetic perturbations by assessing differences in cell proliferation among the collection of 5000 S. cerevisiae gene deletion strains. Such studies have thus far been predominantly qualitative, using agar cell arrays to subjectively score growth differences. Quantitative systems level analysis of gene interactions would be enabled by more precise HTCP methods, such as kinetic analysis of cell proliferation in liquid culture by optical density. However, requirements for processing liquid cultures make them relatively cumbersome and low throughput compared to agar. To improve HTCP performance and advance capabilities for quantifying interactions, YeastXtract software was developed for automated analysis of cell array images. RESULTS: YeastXtract software was developed for kinetic growth curve analysis of spotted agar cultures. The accuracy and precision for image analysis of agar culture arrays was comparable to OD measurements of liquid cultures. Using YeastXtract, image intensity vs. biomass of spot cultures was linearly correlated over two orders of magnitude. Thus cell proliferation could be measured over about seven generations, including four to five generations of relatively constant exponential phase growth. Spot area normalization reduced the variation in measurements of total growth efficiency. A growth model, based on the logistic function, increased precision and accuracy of maximum specific rate measurements, compared to empirical methods. The logistic function model was also more robust against data sparseness, meaning that less data was required to obtain accurate, precise, quantitative growth phenotypes. CONCLUSION: Microbial cultures spotted onto agar media are widely used for genotype-phenotype analysis, however quantitative HTCP methods capable of measuring kinetic growth rates have not been available previously. YeastXtract provides objective, automated, quantitative, image analysis of agar cell culture arrays. Fitting the resulting data to a logistic equation-based growth model yields robust, accurate growth rate information. These methods allow the incorporation of imaging and automated image analysis of cell arrays, grown on solid agar media, into HTCP-driven experimental approaches, such as global, quantitative analysis of gene interaction networks

Microbial Community Evolution Is Significantly Impacted by the Use of Calcium Isosaccharinic Acid as an Analogue for the Products of Alkaline Cellulose Degradation

Diasteriomeric isosaccharinic acid (ISA) is an important consideration within safety assessments for the disposal of the United Kingdoms’ nuclear waste legacy, where it may potentially influence radionuclide migration. Since the intrusion of micro-organisms may occur within a disposal concept, the impact of ISA may be impacted by microbial metabolism. Within the present study we have established two polymicrobial consortia derived from a hyperalkaline soil. Here, α-ISA and a diatereomeric mix of ISAs’ were used as a sole carbon source, reflecting two common substrates appearing within the literature. The metabolism of ISA within these two consortia was similar, where ISA degradation resulted in the acetogenesis and hydrogenotrophic methanogenesis. The chemical data obtained confirm that the diastereomeric nature of ISA is likely to have no impact on its metabolism within alkaline environments. High throughput sequencing of the original soil showed a diverse community which, in the presence of ISA allowed for the dominance the Clostridiales associated taxa with Clostridium clariflavum prevalent. Further taxonomic investigation at the genus level showed that there was in fact a significant difference (p = 0.004) between the two community profiles. Our study demonstrates that the selection of carbon substrate is likely to have a significant impact on microbial community composition estimations, which may have implications with respect to a safety assessment of an ILW-GDF

Kaneohe Bay Sewage Diversion Experiment: Perspectives on Ecosystem Responses to Nutritional Perturbation

Kaneohe Bay, Hawaii, received increasing amounts of sewage from the 1950s through 1977. Most sewage was diverted from the bay in 1977 and early 1978. This investigation, begun in January 1976 and continued through August 1979, described the bay over that period, with particular reference to the responses of the ecosystem to sewage diversion. The sewage was a nutritional subsidy. All of the inorganic nitrogen and most of the inorganic phosphorus introduced into the ecosystem were taken up biologically before being advected from the bay. The major uptake was by phytoplankton, and the internal water-column cycle between dissolved nutrients, phytoplankton, zooplankton, microheterotrophs, and detritus supported a rate of productivity far exceeding the rate of nutrient loading. These water-column particles were partly washed out of the ecosystem and partly sedimented and became available to the benthos. The primary benthic response to nutrient loading was a large buildup of detritivorous heterotrophic biomass. Cycling of nutrients among heterotrophs, autotrophs, detritus, and inorganic nutrients was important. With sewage diversion, the biomass of both plankton and benthos decreased rapidly. Benthic biological composition has not yet returned to presewage conditions, partly because some key organisms are long-lived and partly because the bay substratum has been perturbed by both the sewage and other human influences