Many educational institutions have been using online judges in programming classes, amongst others, to provide faster feedback for students and to reduce the teacher's workload. There is some evidence that online judges also help in reducing dropout. Nevertheless, there is still a high level of dropout noticeable in introductory programming classes. In this sense, the objective of this work is to develop and validate a method for predicting student dropout using data from the first two weeks of study, to allow for early intervention. Instead of the classical questionnaire-based method, we opted for a non-subjective, data-driven approach. However, such approaches are known to suffer from a potential overload of factors, which may not all be relevant to the prediction task. As a result, we reached a very promising 80% of accuracy, and performed explicit extraction of the main factors leading to student dropout.
While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. The jury is still out on which factors are the most appropriate predictors. However, the literature agrees that early prediction is vital to allow for a timely intervention. Whilst feature-rich predictors may have the best chance for high accuracy, they may be unwieldy. This study aims to predict learner dropout early-on, from the first week, by comparing several machinelearning approaches, including Random Forest, Adaptive Boost, XGBoost and GradientBoost Classifiers. The results show promising accuracies (82%-94%) using as little as 2 features. We show that the accuracies obtained outperform state of the art approaches, even when the latter deploy several features.
Plants’ bioactives are well-known safe drugs for vital diseases. Flavones and Flavonoid-rich dietary supplements are known to exhibit neuroprotective potential. In this study, we isolated a flavone 2-(3,4-dimethoxyphenyl)-3,7-dihydroxy-4H-chromen-4-one from Notholirion thomsonianum and it was evaluated against various targets of the oxidative stress-related neurological disorders. The compound showed excellent acetyl and butyrylcholinesterase inhibitions in its profile, giving IC50 values of 1.37 and 0.95 μM, respectively. Similarly, in in-vitro MAO-B assay, our flavone exhibited an IC50 value of 0.14 μM in comparison to the standard safinamide (IC50 0.025 μM). In in-vitro anti-inflammatory assay, our isolated compound exhibited IC50 values of 7.09, 0.38 and 0.84 μM against COX-1, COX-2 and 5-LOX, respectively. The COX-2 selectivity (SI) of the compound was 18.70. The compound was found safe in animals and was very effective in carrageenan-induced inflammation. Due to the polar groups in the structure, a very excellent antioxidant profile was observed in both in-vitro and in-vivo models. The compound was docked into the target proteins of the respective activities and the binding energies confirmed the potency of our compound. Furthermore, absorption, distribution, metabolism, excretion, and toxicity (ADMET) results showed that the isolated flavone has a good GIT absorption ability and comes with no hepatic and cardiotoxicity. In addition, the skin sensitization test, in-vitro human cell line activation test (h-CLAT) and KeratinoSens have revealed that isolated flavone is not skin sensitive with a confidence score of 59.6% and 91.6%. Herein, we have isolated a natural flavone with an effective profile against Alzheimer’s, inflammation and oxidative stress. The exploration of this natural flavone will provide a baseline for future research in the field of drug development.
Since their ‘official’ emergence in 2012 (Gardner and Brooks 2018), massive open online courses (MOOCs) have been growing rapidly. They offer low-cost education for both students and content providers; however, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). The most recent literature on MOOCs focuses on identifying factors that contribute to student success, completion level and engagement. One of the MOOC platforms’ ultimate targets is to become self-sustaining, enabling partners to create revenues and offset operating costs. Nevertheless, analysing learners’ purchasing behaviour on MOOCs remains limited. Thus, this study aims to predict students purchasing behaviour and therefore a MOOCs revenue, based on the rich array of activity clickstream and demographic data from learners. Specifically, we compare how several machine learning algorithms, namely RandomForest, GradientBoosting, AdaBoost and XGBoost can predict course purchasability using a large-scale data collection of 23 runs spread over 5 courses delivered by The University of Warwick between 2013 and 2017 via FutureLearn. We further identify the common representative predictive attributes that influence a learner’s certificate purchasing decisions. Our proposed model achieved promising accuracies, between 0.82 and 0.91, using only the time spent on each step. We further reached higher accuracy of 0.83 to 0.95, adding learner demographics (e.g. gender, age group, level of education, and country) which showed a considerable impact on the model’s performance. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenue.
BACKGROUND Bacillus species synthesize antifungal lipopeptides (LPs) making them a sustainable and eco‐friendly management option to combat Fusarium wilt of chickpea. RESULTS In this study, 18 endophytic Bacillus strains were assessed for their antifungal activity against Fusarium oxysporum f. sp. ciceris (FOC) associated with Fusarium wilt of chickpea. Among them, 13 strains produced significant inhibition zones in a direct antifungal assay while five strains failed to produce the inhibition of FOC. Bacillus thuringiensis CHGP12 exhibited the highest inhibition 3.45 cm of FOC. The LPs extracted from CHGP12 showed significant inhibition of the pathogen. Liquid chromatography–mass spectrometry (LC–MS) analysis confirmed that CHGP12 possessed the ability to produce fengycin, surfactin, iturin, bacillaene, bacillibactin, plantazolicin, and bacilysin. In an in vitro qualitative assay CHGP12 exhibited the ability to produce lipase, amylase, cellulase, protease, siderophores, and indole 3‐acetic acid (IAA). IAA and gibberellic acid (GA) were quantified using ultra‐performance liquid chromatography (UPLC) with 370 and 770 ng mL−1 concentrations of IAA and GA respectively. Furthermore, the disease severity showed a 40% decrease over control in CHGP12 treated plants compared to the control in a glasshouse experiment. Moreover, CHGP12 also exhibited a significant increase in total biomass of the plants namely, root and shoot growth parameters, stomatal conductance, and photosynthesis rate. CONCLUSION In conclusion, our findings suggest that B. thuringiensis CHGP12 is a promising strain with high antagonistic and growth‐promoting potential against Fusarium wilt of chickpea. © 2022 Society of Chemical Industry.
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