Accurate Estimation of Time-on-Task While Programming

In a recent study, students were periodically prompted to self-report engagement while working on computer programming assignments in a CS1 course. A regression model predicting time-on-task was proposed. While it was a significant improvement over ad-hoc estimation techniques, the study nevertheless suffered from lack of error analysis, lack of comparison with existing methods, subtle complications in prompting students, and small sample size. In this paper we report results from a study with an increased number of student participants and modified prompting scheme intended to better capture natural student behavior. Furthermore, we perform a cross-validation analysis on our refined regression model and present the resulting error bounds. We compare with threshold approaches and find that, in at least one context, a simple 5-minute threshold of inactivity is a reasonable estimate for whether a student is on-task or not. We show that our approach to modeling student engagement while programming is robust and suitable for identification of students in need of intervention, understanding engagement behavior, and estimating time taken on programming assignments

Personal computers in the home a collaborative inquiry.

Source: Masters Abstracts International, Volume: 40-07, page: . Thesis (M.A.)--University of Windsor (Canada), 1985

A Practical Model of Student Engagement While Programming

We consider the question of how to predict whether a student is on or off task while working on a computer programming assignment using elapsed time since the last keystroke as the single independent variable. In this paper we report results of an empirical study in which we intermittently prompted CS1 students working on a programming assignment to self-report whether they were engaged in the assignment at that moment. Our regression model derived from the results of the study shows power-law decay in the engagement rate of students with increasing time of keyboard inactivity ranging from a nearly 80% engagement rate after 45 seconds to 30% after 32 minutes of inactivity. We find that students remain engaged in programming for a median of about 8 minutes before going off task, and when they do go off task, they most often return after 1 to 4 minutes of disengagement. Our model has application in estimating the amount of engaged time students take to complete programming assignments, identifying students in need of intervention, and understanding the effects of different engagement behaviors

Dispersion and connectivity estimates along the U.S. west coast from a realistic numerical model

Near-surface particle dispersion, larval dispersal and connectivity along the U.S west coast were explored using a realistic numerical model of the California Current System. Seasonal model velocities were qualitatively and quantitatively evaluated using Global Drifter Program data. The model displayed a clear seasonal cycle of eddy energy near the coast with energy maxima southwest of major headlands. Eddy speeds were correlated with drifter-based estimates during summer and fall when compared spatially. Over six million passive, Lagrangian particles were released in the upper 20 m of the water column within 10 km of the California and Oregon coasts and tracked for 7 years. The effect of subgridscale vertical turbulence was parameterized with a random walk model. Resulting trajectories yielded climatological maps of particle dispersion. Particle densities varied with release region, release season and time-since-release. Dispersal distances and coastal connectivity varied with season of release, release location, release depth and pelagic larval duration (PLD). Connectivity was clearly influenced by major geographic features such as the Gulf of the Farallones and Cape Mendocino. Given a moderate (30–60 day) PLD, mean dispersal distances varied from ∼10–230 km, with standard deviations of ∼130–220 km. For release locations from Palos Verdes to Point Sur, the primary direction of dispersal was northward for a moderate PLD, regardless of season. For long PLDs (120–180 day), mean dispersal distances were larger (∼40–440 km), with standard deviations of ∼330–540 km. In winter given a long PLD, dispersal was primarily southward for release locations north of Point Arena. Increasing release depths to 40–60 m altered mean dispersal distances by 50–250 km polewards, but had little effect on standard deviations. Point Conception did not act as a barrier to dispersal for source regions in the Southern California Bight

Systolic blood pressure reduction during the first 24 h in acute heart failure admission: friend or foe?

Aims: Changes in systolic blood pressure (SBP) during an admission for acute heart failure (AHF), especially those leading to hypotension, have been suggested to increase the risk for adverse outcomes. Methods and results: We analysed associations of SBP decrease during the first 24 h from randomization with serum creatinine changes at the last time-point available (72 h), using linear regression, and with 30- and 180-day outcomes, using Cox regression, in 1257 patients in the VERITAS study. After multivariable adjustment for baseline SBP, greater SBP decrease at 24 h from randomization was associated with greater creatinine increase at 72 h and greater risk for 30-day all-cause death, worsening heart failure (HF) or HF readmission. The hazard ratio (HR) for each 1 mmHg decrease in SBP at 24 h for 30-day death, worsening HF or HF rehospitalization was 1.01 [95% confidence interval (CI) 1.00–1.02; P = 0.021]. Similarly, the HR for each 1 mmHg decrease in SBP at 24 h for 180-day all-cause mortality was 1.01 (95% CI 1.00–1.03; P = 0.038). The associations between SBP decrease and outcomes did not differ by tezosentan treatment group, although tezosentan treatment was associated with a greater SBP decrease at 24 h. Conclusions: In the current post hoc analysis, SBP decrease during the first 24 h was associated with increased renal impairment and adverse outcomes at 30 and 180 days. Caution, with special attention to blood pressure monitoring, should be exercised when vasodilating agents are given to AHF patients

Predictors and associations with outcomes of length of hospital stay in patients with acute heart failure: results from VERITAS

Background: The length of hospital stay (LOS) is important in patients admitted for acute heart failure (AHF) because it prolongs an unpleasant experience for the patients and adds substantially to health care costs. Methods and Results: We examined the association between LOS and baseline characteristics, 10-day post-discharge HF readmission, and 90-day post-discharge mortality in 1347 patients with AHF enrolled in the VERITAS program. Longer LOS was associated with greater HF severity and disease burden at baseline; however, most of the variability of LOS could not be explained by these factors. LOS was associated with a higher HF risk of both HF readmission (odds ratio for 1-day increase: 1.08; 95% confidence interval [CI] 1.01–1.16; P = .019) and 90-day mortality (hazard ratio for 1-day increase: 1.05; 95% CI 1.02–1.07; P < .001), although these associations are partially explained by concurrent end-organ damage and worsening heart failure during the first days of admission. Conclusions: In patients who have been admitted for AHF, longer length of hospital stay is associated with a higher rate of short-term mortality. Clinical Trial Registration: VERITAS-1 and -2: Clinicaltrials.gov identifiers NCT00525707 and NCT00524433