Embracing imperfection in learning analytics

© 2018 Copyright held by the owner/author(s). Learning Analytics (LA) sits at the confluence of many contributing disciplines, which brings the risk of hidden assumptions inherited from those fields. Here, we consider a hidden assumption derived from computer science, namely, that improving computational accuracy in classification is always a worthy goal. We demonstrate that this assumption is unlikely to hold in some important educational contexts, and argue that embracing computational “imperfection” can improve outcomes for those scenarios. Specifically, we show that learner-facing approaches aimed at “learning how to learn” require more holistic validation strategies. We consider what information must be provided in order to reasonably evaluate algorithmic tools in LA, to facilitate transparency and realistic performance comparisons

Reflective writing analytics for actionable feedback

© 2017 ACM. Reflective writing can provide a powerful way for students to integrate professional experience and academic learning. However, writing reflectively requires high quality actionable feedback, which is time-consuming to provide at scale. This paper reports progress on the design, implementation, and validation of a Reflective Writing Analytics platform to provide actionable feedback within a tertiary authentic assessment context. The contributions are: (1) a new conceptual framework for reflective writing; (2) a computational approach to modelling reflective writing, deriving analytics, and providing feedback; (3) the pedagogical and user experience rationale for platform design decisions; and (4) a pilot in a student learning context, with preliminary data on educator and student acceptance, and the extent to which we can evidence that the software provided actionable feedback for reflective writing

The rise of fully turbulent flow

Over a century of research into the origin of turbulence in wallbounded shear flows has resulted in a puzzling picture in which turbulence appears in a variety of different states competing with laminar background flow. At slightly higher speeds the situation changes distinctly and the entire flow is turbulent. Neither the origin of the different states encountered during transition, nor their front dynamics, let alone the transformation to full turbulence could be explained to date. Combining experiments, theory and computer simulations here we uncover the bifurcation scenario organising the route to fully turbulent pipe flow and explain the front dynamics of the different states encountered in the process. Key to resolving this problem is the interpretation of the flow as a bistable system with nonlinear propagation (advection) of turbulent fronts. These findings bridge the gap between our understanding of the onset of turbulence and fully turbulent flows.Comment: 31 pages, 9 figure

3D deep convolutional neural network-based ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI

Hyperpolarized gas MRI enables visualization of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. In this work, we evaluate a 3D V-Net to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 743 helium-3 (3He) or xenon-129 (129Xe) volumetric scans and corresponding expert segmentations from 326 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined 3He and 129Xe MRI scans outperformed other DL methods, achieving a mean ± SD Dice of 0.958 ± 0.022, average boundary Hausdorff distance of 2.22 ± 2.16 mm, Hausdorff 95th percentile of 8.53 ± 12.98 mm and relative error of 0.087 ± 0.049. Moreover, no difference in performance was observed between 129Xe and 3He scans in the testing set. Combined training on 129Xe and 3He yielded statistically significant improvements over the conventional methods (p < 0.0001). The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and noise artifacts and is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing

In vitro modulation of inflammatory cytokine and IgG levels by extracts of Perna canaliculus

BACKGROUND: Inflammation is a predominant characteristic of autoimmune diseases which is characterized by the increased expression of pro-inflammatory cytokines. Soon to be published work from our laboratory has shown that ingestion of Perna canaliculus prevents the development of autoimmune diseases such as Systemic Lupus Erythematosus and rheumatoid arthritis in laboratory animals. The current paper attempts to illustrate how Perna can alleviate inflammation by modulating inflammatory cytokines, cyclooxygenase enzymes and Immunoglobulin-G (IgG) levels. METHODS: In the present study, hydrochloric acid [HCl] and Tween-20 were used to develop extracts of Perna. These extracts were assayed for protein content. Increasing concentrations of these extracts were then tested in cell culture for modulation of inflammatory cytokine, cyclooxygenase enzymes and IgG levels. Parallel tests were run using an available glycogen extract of Perna as a comparison to our in-house laboratory preparations. RESULTS: Tween-20 Perna extracts were found to be more stable and less toxic in cell culture than HCl digest of Perna. They also assayed higher in protein content that HCl extracts. Although both extracts inhibited IgG production in V2E9 hybridomas, Tween-20 extracts were more consistent in IgG suppression than HCl extracts. Overall Tween-20 extracts effectively decreased levels of TNF-α, IL-1, IL-2 and IL-6 as observed using cytokine bioassays. Twenty micrograms of Tween-20 Perna extracts induced such significant decreases in inflammatory cytokine production that when tested on sensitive cell lines, they very nearly abolished the decrease in viability induced by these cytokines. Tween-20 extracts effectively inhibited both COX-1 and COX-2 cyclooxygenase activity. As a comparison, the glycogen extract also demonstrated a similar though weaker effect on COX-1 and COX-2 enzymes. The active components of both extracts (Tween-20 and glycogen) were observed to possess molecular weights above 100 kDa. Although the anti-cytokine activity of the Tween-20 extract was destroyed by Proteinase-K treatment, the anti-COX-1 and anti-COX-2 activity of both the extracts were not sensitive to protease treatment. CONCLUSION: We have successfully demonstrated modulation in the levels of inflammatory cytokines, cyclooxygenase enzymes and immunoglobulins by our in-house laboratory preparations of Perna canaliculus, whereby suggesting an immunomodulatory role of Perna canaliculus in regulating inflammation

Carboxypeptidase G2 rescue in patients with methotrexate intoxication and renal failure

The methotrexate (MTX) rescue agent carboxypeptidase G2 (CPDG2) rapidly hydrolyses MTX to the inactive metabolite DAMPA (4-[[2,4-diamino-6-(pteridinyl)methyl]-methylamino]-benzoic acid) and glutamate in patients with MTX-induced renal failure and delayed MTX excretion. DAMPA is thought to be an inactive metabolite of MTX because it is not an effective inhibitor of the MTX target enzyme dihydrofolate reductase. DAMPA is eliminated more rapidly than MTX in these patients, which suggests a nonrenal route of elimination. In a phase II study (May 1997–March 2002), CPDG2 was administered intravenously to 82 patients at a median dose of 50 U kg−1 (range 33–60 U kg−1). Eligible patients for this study had serum MTX concentrations of >10 μM at 36 h or >5 μM at 42 h after start of MTX infusion and documented renal failure (serum creatinine ⩾1.5 times the upper limit of normal). Immediately before CPDG2 administration, a median MTX serum level of 11.93 μM (range 0.52–901 μM) was documented. Carboxypeptidase G2 was given at a median of 52 h (range 25–178 h) following the start of an MTX infusion of 1–12 g m−2 4–36 h−1 and resulted in a rapid 97% (range 73–99%) reduction of the MTX serum level. Toxicity related to CPDG2 was not observed. Toxicity related to MTX was documented in about half the patients; four patients died despite CPDG2 administration due to severe myelosuppression and septic complications. In conclusion, administration of CPDG2 is a well-tolerated, safe and a very effective way of MTX elimination in delayed excretion due to renal failure

Exploratory Analysis in Learning Analytics

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex datasets were analyzed and iteratively modeled with a variety of computationally intensive methods to provide the most effective outcomes for learning assessment, performance management and learner tracking. The article presents the research contexts, the tools and methods used in the exploratory phases of analysis, the major findings and the implications for learning analytics research methods

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally