Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums

In MOOCs, identifying urgent comments on discussion forums is an ongoing challenge. Whilst urgent comments require immediate reactions from instructors, to improve interaction with their learners, and potentially reducing drop-out rates—the task is difficult, as truly urgent comments are rare. From a data analytics perspective, this represents a highly unbalanced (sparse) dataset. Here, we aim to automate the urgent comments identification process, based on fine-grained learner modelling—to be used for automatic recommendations to instructors. To showcase and compare these models, we apply them to the first gold standard dataset for Urgent iNstructor InTErvention (UNITE), which we created by labelling FutureLearn MOOC data. We implement both benchmark shallow classifiers and deep learning. Importantly, we not only compare, for the first time for the unbalanced problem, several data balancing techniques, comprising text augmentation, text augmentation with undersampling, and undersampling, but also propose several new pipelines for combining different augmenters for text augmentation. Results show that models with undersampling can predict most urgent cases; and 3X augmentation + undersampling usually attains the best performance. We additionally validate the best models via a generic benchmark dataset (Stanford). As a case study, we showcase how the naïve Bayes with count vector can adaptively support instructors in answering learner questions/comments, potentially saving time or increasing efficiency in supporting learners. Finally, we show that the errors from the classifier mirrors the disagreements between annotators. Thus, our proposed algorithms perform at least as well as a ‘super-diligent’ human instructor (with the time to consider all comments)

Automatic subject-based contextualisation of programming assignment lists.

As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as ’Online Judges’. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for programming as CS students do. In this sense, contextualised assignment lists, with programming problems related to the students’ major, could enhance engagement in the learning process. Thus, students would solve programming problems related to their academic context, improving their comprehension of the applicability and importance of programming. Nonetheless, preparing these contextually personalised programming assignments for classes for different courses is really laborious and would increase considerably the instructors’/monitors’ workload. Thus, this work aims, for the first time, to the best of our knowledge, to automatically classify the programming assignments in Online Judges based on students’ academic contexts by proposing a new context taxonomy, as well as a comprehensive pipeline evaluation methodology of cutting edge competitive Natural Language Processing (NLP). Our comprehensive methodology pipeline allows for comparing state of the art data augmentation, classifiers, beside NLP approaches. The context taxonomy created contains 23 subject matters related to the non-CS majors, representing thus a challenging multi-classification problem. We show how even on this problem, our comprehensive pipeline evaluation methodology allows us to achieve an accuracy of 95.2%, which makes it possible to automatically create contextually personalised program assignments for non-CS with a minimal error rate (4.8%)

Staphylococcus aureus in Some Brazilian Dairy Industries: Changes of Contamination and Diversity

Staphylococcus aureus, a major food-poisoning pathogen, is a common contaminant in dairy industries worldwide, including in Brazil. We determined the occurrence of S. aureus in five dairies in Brazil over 8 months. Of 421 samples, 31 (7.4%) were positive for S. aureus and prevalence varied from 0 to 63.3% between dairies. Sixty-six isolates from the 31 samples were typed by Multi-Locus Sequence Typing to determine if these isolates were persistent or continuously reintroduced. Seven known sequence types (STs), ST1, ST5, ST30, ST97, ST126, ST188 and ST398, and four new ST were identified, ST3531, ST3540, ST3562 and ST3534. Clonal complex (CC) 1 (including the four new ST), known as an epidemic clone, was the dominant CC. However, there were no indications of persistence of particular ST. The resistance toward 11 antibiotic compounds was assessed. Twelve profiles were generated with 75.8% of strains being sensitive to all antibiotic classes and no Methicillin-resistant S. aureus (MRSA) strains were found. The enterotoxin-encoding genes involved in food-poisoning, e.g., sea, sed, see, and seg were targeted by PCR. The two toxin-encoding genes, sed and see, were not detected. Only three strains (4.5%) harbored seg and two of these also harbored sea. Despite the isolates being Methicillin-sensitive S. aureus (MSSA), the presence of CC1 clones in the processing environment, including some harboring enterotoxin encoding genes, is of concern and hygiene must have high priority to reduce contamination

GARFIELD: A Recommender System to Personalize Gamified Learning

Students often lack intrinsic motivation to engage with educational activities. While gamification has the potential to mitigate that issue, it does not always work, possibly due to poor gamification design. Researchers have developed strategies to improve gamification designs through personalization. However, most of those are based on theoretical understanding of game elements and their impact on students, instead of considering real interaction data. Thus, we developed an approach to personalize gamification designs upon data from real students’ experiences with a learning environment. We followed the CRISP-DM methodology to develop personalization strategies by analyzing self-reports from 221 Brazilian students who used one out of our five gamification designs. Then, we regressed from such data to obtain recommendations of which design is the most suitable to achieve a desired motivation level, leading to our interactive recommender system: GARFIELD. Its recommendations showed a moderate performance compared to the ground truth, demonstrating our approach’s potential. To the best of our knowledge, GARFIELD is the first model to guide practitioners and instructors on how to personalize gamification based on empirical data.acceptedVersionPeer reviewe

Heme-Oxygenases during Erythropoiesis in K562 and Human Bone Marrow Cells

In mammalian cells, heme can be degraded by heme-oxygenases (HO). Heme-oxygenase 1 (HO-1) is known to be the heme inducible isoform, whereas heme-oxygenase 2 (HO-2) is the constitutive enzyme. Here we investigated the presence of HO during erythroid differentiation in human bone marrow erythroid precursors and K562 cells. HO-1 mRNA and protein expression levels were below limits of detection in K562 cells. Moreover, heme was unable to induce HO-1, at the protein and mRNA profiles. Surprisingly, HO-2 expression was inhibited upon incubation with heme. To evaluate the physiological relevance of these findings, we analyzed HO expression during normal erythropoiesis in human bone marrow. Erythroid precursors were characterized by lack of significant expression of HO-1 and by progressive reduction of HO-2 during differentiation. FLVCR expression, a recently described heme exporter found in erythroid precursors, was also analyzed. Interestingly, the disruption in the HO detoxification system was accompanied by a transient induction of FLVCR. It will be interesting to verify if the inhibition of HO expression, that we found, is preventing a futile cycle of concomitant heme synthesis and catabolism. We believe that a significant feature of erythropoiesis could be the replacement of heme breakdown by heme exportation, as a mechanism to prevent heme toxicity

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Skin color and severe maternal outcomes: evidence from the brazilian network for surveillance of severe maternal morbidity

Taking into account the probable role that race/skin color may have for determining outcomes in maternal health, the objective of this study was to assess whether maternal race/skin color is a predictor of severe maternal morbidity. This is a secondary analysis of the Brazilian Network for Surveillance of Severe Maternal Morbidity, a national multicenter cross-sectional study of 27 Brazilian referral maternity hospitals. A prospective surveillance was performed to identify cases of maternal death (MD), maternal near miss (MNM) events, and potentially life-threatening conditions (PLTC), according to standard WHO definition and criteria. Among 9,555 women with severe maternal morbidity, data on race/skin color was available for 7,139 women, who were further divided into two groups: 4,108 nonwhite women (2,253 black and 1,855 from other races/skin color) and 3,031 white women. Indicators of severe maternal morbidity according to WHO definition are shown by skin color group. Adjusted Prevalence Ratios (PRadj - 95%CI) for Severe Maternal Outcome (SMO=MNM+MD) were estimated according to sociodemographic/obstetric characteristics, pregnancy outcomes, and perinatal results considering race. Results. Among 7,139 women with severe maternal morbidity evaluated, 90.5% were classified as PLTC, 8.5% as MNM, and 1.6% as MD. There was a significantly higher prevalence of MNM and MD among white women. MNMR (maternal near miss ratio) was 9.37 per thousand live births (LB). SMOR (severe maternal outcome ratio) was 11.08 per 1000 LB, and MMR (maternal mortality ratio) was 170.4 per 100,000 LB. Maternal mortality to maternal near miss ratio was 1 to 5.2, irrespective of maternal skin color. Hypertension, the main cause of maternal complications, affected mostly nonwhite women. Hemorrhage, the second more common cause of maternal complication, predominated among white women. Nonwhite skin color was associated with a reduced risk of SMO in multivariate analysis. Nonwhite skin color was associated with a lower risk for severe maternal outcomes. This result could be due to confounding factors linked to a high rate of Brazilian miscegenation.2019CNPQ - Conselho Nacional de Desenvolvimento Científico e Tecnológico402702/2008-