Search

Early Warning Systems (EWSs) aggregate multiple sources of data to provide timely information to stakeholders about students in need of academic support. There is an increasing need to incorporate relevant data about student behaviors into the algorithms underlying EWSs to improve predictors of students' success or failure. Many EWSs currently incorporate counts of course resource use, although these measures provide no information about which resources students are using. We use seven years of data from seven core STEM courses at a large university to investigate the associations between students' use of categorized course resources (e.g., lecture or exam preparation resources) and their final course grade. Using logistic regression, we find that students who use exam preparation resources to a greater degree than their peers are more likely to receive a final grade of B or higher. In contrast, students who use more lecture-related resources than their peers are less likely to receive a final grade of B or higher. We discuss the implications of our results for developers deciding how to incorporate categories of course resource usage data into EWSs, for academic advisors using this information with students, and for instructors deciding which resources to include on their LMS site.

In an intelligent tutoring system (ITS), it can be useful to know when a student has disengaged from a task and might benefit from a particular intervention. However, predicting disengagement on a trial-by-trial basis is a challenging problem, particularly in complex cognitive domains. In the present work, data-driven methods were used to address two aspects of this problem: identification of predictive features at the single-trial level, and selection of accurate and robust models. Experiment data were collected in a middle-school classroom using a vocabulary training ITS. On each trial, the ITS presented a low-frequency (Tier 2 or frontier) word and prompted students to type in the word's meaning. Single-trial measures--including the orthographic and semantic accuracy of each response, and context-sensitive measures such as interaction patterns across trials--were computed throughout the task. There were two key findings. First, as expected, different features predicted when a student was likely to be more engaged (e.g., high semantic accuracy) or less engaged (e.g., repetition of same or similar words across consecutive trials). Second, there was added value in representing context-sensitive information, which captures patterns of performance over time, as well as trial-specific information. These findings provide useful insights into effective methods for representing and modeling temporal patterns of student engagement in an ITS, especially those related to language learning. Such models may be useful in the design and implementation of adaptive tutors in complex cognitive domains like language learning.

Recent rapid air temperature increases across the northern-latitude tundra have prolonged permafrost thawing and snow melting periods, resulting in increased soil temperature (Ts) and volumetric soil water content (SWC). Under prolonged soil warming at 8°C, Alaskan tundra soils were incubated in a microcosm system and examined for the SWC differential influence on the microbial decomposition activity of large molecular weight (MW) humic substances (HS). When one microcosm soil (AKC1-1) was incubated at a constant SWC of 41% for 90 days (T = 90) and then SWC was gradually decreased from 41% to 29% for another T = 90, the initial HS was partly depolymerized. In contrast, in AKC1-2 incubated at a gradually decreasing SWC from the initial 32% to 10% for T = 90 and then increasing to 27% for another T = 90, HS depolymerization was undetected. Overall, the microbial communities in AKC1-1 could maintain metabolic activity at sufficient and constant SWC during the initial T = 90 incubation. In contrast, AKC1-2 microbes may have been damaged by drought stress during the drying SWC regimen, possibly resulting in the loss of HS decomposition activity, which did not recover even after re-wetting to an optimal SWC range (20–40%). After T = 90, the CO2 production in both treatments was attributed to the increased decomposition of small-MW organic compounds (including aerobic HS-degradative products) within an optimal SWC range. We expect this study to provide new insights into the early effects of warming- and topography-induced SWC variations on the microbial contribution to CO2 emissions via HS decomposition in northern-latitude tundra soil. [ABSTRACT FROM AUTHOR]

To search useful fatty acid biomarkers for cyanobacteria feeding abilities of control organisms, we compared fatty acid composition of six cultured algal species in four taxonomic groups (Cryptophyceae, Bacillariophyceae, Chlorophyceae, and Cyanophyceae) and evaluated several fatty acid biomarkers using data from three lakes that varied in trophic status. In addition, a laboratory feeding experiment was conducted to verify fatty acid markers for cyanobacteria usingSelenastrum capricornutum(Chlorophyceae) andMicrocystis aeruginosa(Cyanophyceae). Two strains ofM. aeruginosahad the highest content of 18:3ω6 whileAnabaenasp. had a relatively high content of 18:1ω7 among the six species. Both 18:1ω7 and 18:3ω6 (γ-linolenic acid, GLA) concentrations showed significant linear relationships with cyanobacteria biomass in natural systems. In the laboratory feeding experiment, a principal component analysis score plot indicate that fatty acid composition ofBranchinella kugenumaensisfed withM. aeruginosabecame similar to that of the cyanobacterial population. In S-plot of partial least square discriminant analysis, 18:3ω6 and 18:1ω7 were selected for fairy shrimps feeding onM. aeruginosa, whereas 18:3ω3 (ALA, α-linolenic acid) and 18:1ω9 were selected for those feeding onS. capricornutum. 18:3ω6 content ofB. kugenumaensisfed withM. aeruginosasignificantly increased after the third day and 18:1ω7 content was significantly different from the other feeding group after six days. In particular, 18:3ω6 appeared to be a useful fatty acid biomarker forM. aeruginosain tracing trophic relations of herbivorous organisms in freshwater ecosystems. [ABSTRACT FROM AUTHOR]

We conducted a 7-d co-existence experiment to verify the inhibitory activity of an aquatic vascular plant, Myriophyllum spicatum, on the cyanobacterium Microcystis aeruginosa, and to screen for allelopathic compounds. Plants were placed on a modified solid M4 medium while M. aeruginosa was cultured in an L16 liquid medium. Our treatments included rearing Myriophyllum spicatum alone (Plant-Only; PO) or with Microcystis aeruginosa (Plant-Cyanobacteria; PCB), as well as an untreated control, or plates with the cyanobacterium alone. In the PCB treatment, M. aeruginosa became transparent in three replicates on the 6th day and in four replicates on Day 7. To make the distinction in Myriophyllum spicatum metabolites between the PO and PCB treatments, we obtained 13 m/z profiles using a 15 T Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Principle component analyses were performed on those profiles, from which we obtained accurate mass values for selected peaks that had most negative loadings on the first principal component and most positive loadings on the second principal component: 282.06983 and 283.06836. We identified those peaks as C7H17N5O3S2 and C10H12N4O6, respectively. The Dictionary of Natural Products database suggests that the latter is either oxanosine or xanthosine. [ABSTRACT FROM AUTHOR]