Social Addictive Gameful Engineering (SAGE): A Game-based Learning and Assessment System for Computational Thinking

At an unrivaled and enduring pace, computing has transformed the world, resulting in demand for a universal fourth foundation beyond reading, writing, and arithmetic: computational thinking (CT). Despite increasingly widespread acceptance of CT as a crucial competency for all, transforming education systems accordingly has proven complex. The principal hypothesis of this thesis is that we can improve the efficiency and efficacy of teaching and learning CT by building gameful learning and assessment systems on top of block-based programming environments. Additionally, we believe this can be accomplished at scale and cost conducive to accelerating CT dissemination for all. After introducing the requirements, approach, and architecture, we present a solution named Gameful Direct Instruction. This involves embedding Parsons Programming Puzzles (PPPs) in Scratch, which is a block-based programming environment currently used prevalently in grades 6-8. PPPs encourage students to practice CT by assembling into correct order sets of mixed-up blocks that comprise samples of well-written code which focus on individual concepts. The structure provided by PPPs enable instructors to design games that steer learner attention toward targeted learning goals through puzzle-solving play. Learners receive continuous automated feedback as they attempt to arrange programming constructs in correct order, leading to more efficient comprehension of core CT concepts than they might otherwise attain through less structured Scratch assignments. We measure this efficiency first via a pilot study conducted after the initial integration of PPPs with Scratch, and second after the addition of scaffolding enhancements in a study involving a larger adult general population. We complement Gameful Direct Instruction with a solution named Gameful Constructionism. This involves integrating with Scratch implicit assessment functionality that facilitates constructionist video game (CVG) design and play. CVGs enable learner to explore CT using construction tools sufficiently expressive for personally meaningful gameplay. Instructors are enabled to guide learning by defining game objectives useful for implicit assessment, while affording learners the opportunity to take ownership of the experience and progress through the sequence of interest and motivation toward sustained engagement. When strategically arranged within a learning progression after PPP gameplay produces evidence of efficient comprehension, CVGs amplify the impact of direct instruction by providing the sculpted context in which learners can apply CT concepts more freely, thereby broadening and deepening understanding, and improving learning efficacy. We measure this efficacy in a study of the general adult population. Since these approaches leverage low fidelity yet motivating gameful techniques, they facilitate the development of learning content at scale and cost supportive of widespread CT uptake. We conclude this thesis with a glance at future work that anticipates further progress in scalability via a solution named Gameful Intelligent Tutoring. This involves augmenting Scratch with Intelligent Tutoring System (ITS) functionality that offers across-activity next-game recommendations, and within-activity just-in-time and on-demand hints. Since these data-driven methods operate without requiring knowledge engineering for each game designed, the instructor can evolve her role from one focused on knowledge transfer to one centered on supporting learning through the design of educational experiences, and we can accelerate the dissemination of CT at scale and reasonable cost while also advancing toward continuously differentiated instruction for each learner

LISA Data Analysis using MCMC methods

The Laser Interferometer Space Antenna (LISA) is expected to simultaneously detect many thousands of low frequency gravitational wave signals. This presents a data analysis challenge that is very different to the one encountered in ground based gravitational wave astronomy. LISA data analysis requires the identification of individual signals from a data stream containing an unknown number of overlapping signals. Because of the signal overlaps, a global fit to all the signals has to be performed in order to avoid biasing the solution. However, performing such a global fit requires the exploration of an enormous parameter space with a dimension upwards of 50,000. Markov Chain Monte Carlo (MCMC) methods offer a very promising solution to the LISA data analysis problem. MCMC algorithms are able to efficiently explore large parameter spaces, simultaneously providing parameter estimates, error analyses and even model selection. Here we present the first application of MCMC methods to simulated LISA data and demonstrate the great potential of the MCMC approach. Our implementation uses a generalized F-statistic to evaluate the likelihoods, and simulated annealing to speed convergence of the Markov chains. As a final step we super-cool the chains to extract maximum likelihood estimates, and estimates of the Bayes factors for competing models. We find that the MCMC approach is able to correctly identify the number of signals present, extract the source parameters, and return error estimates consistent with Fisher information matrix predictions.Comment: 14 pages, 7 figure

Sensitivity and parameter-estimation precision for alternate LISA configurations

We describe a simple framework to assess the LISA scientific performance (more specifically, its sensitivity and expected parameter-estimation precision for prescribed gravitational-wave signals) under the assumption of failure of one or two inter-spacecraft laser measurements (links) and of one to four intra-spacecraft laser measurements. We apply the framework to the simple case of measuring the LISA sensitivity to monochromatic circular binaries, and the LISA parameter-estimation precision for the gravitational-wave polarization angle of these systems. Compared to the six-link baseline configuration, the five-link case is characterized by a small loss in signal-to-noise ratio (SNR) in the high-frequency section of the LISA band; the four-link case shows a reduction by a factor of sqrt(2) at low frequencies, and by up to ~2 at high frequencies. The uncertainty in the estimate of polarization, as computed in the Fisher-matrix formalism, also worsens when moving from six to five, and then to four links: this can be explained by the reduced SNR available in those configurations (except for observations shorter than three months, where five and six links do better than four even with the same SNR). In addition, we prove (for generic signals) that the SNR and Fisher matrix are invariant with respect to the choice of a basis of TDI observables; rather, they depend only on which inter-spacecraft and intra-spacecraft measurements are available.Comment: 17 pages, 4 EPS figures, IOP style, corrected CQG versio

Occurrence of extended-spectrum beta-lactamase producing E. coli in broiler farm workers and the farm environment in Chiang Mai-Lamphun, Thailand

Antimicrobial resistance has become a major global public health threat. Extended-spectrum beta-lactamase (ESBL) producing E. coli appears as an emergence cause of treatment failure and increase mortality due to limited available effective antimicrobial agents. This study was conducted to determine the occurrence and antimicrobial resistance of ESBL producing E. coli in broilers, farm workers and environment in broiler farms in Chiang Mai-Lamphun, Thailand. The prevalence of ESBL producing E. coli in the broiler farms was 60.4% (29/48). The prevalence of ESBL producing E. coli from boot swabs, farm worker’s rectal swabs, feed and water samples were 43.8%, 55.7%, 12.5% and 2.1%, respectively. All isolates showed susceptible to imipenem and, in contrast, resistant to ampicillin. The results demonstrated high antimicrobial resistant rate to streptomycin (94.3%), gentamicin (86.8%), tetracycline (77.4%), chloramphenicol (66.0%), nalidixic acid (58.5%), and sulfamethoxazole/trimethoprim (56.6%). High percentage (96.2%) of isolates was classified as multidrug resistance (MDR). Thirty-five antimicrobial resistance profiles were identified with AMP-GEN-SXT-NAL-TET-CHL-STR, AMP-GEN-SXT-TET-CHL-STR (14.3%) as the 2 most prevalent profiles. The common resistance profiles between farm workers and broilers was demonstrated. These findings are suggestive for possible transmission between poultry and humans in broiler farms, most likely via close contact. Antimicrobial usage should be strictly controlled together with increase awareness on hygiene practices in broiler farms

Evidence for He I 10830 \AA~ absorption during the transit of a warm Neptune around the M-dwarf GJ 3470 with the Habitable-zone Planet Finder

Understanding the dynamics and kinematics of out-flowing atmospheres of hot and warm exoplanets is crucial to understanding the origins and evolutionary history of the exoplanets near the evaporation desert. Recently, ground based measurements of the meta-stable Helium atom's resonant absorption at 10830 \AA~has become a powerful probe of the base environment which is driving the outflow of exoplanet atmospheres. We report evidence for the He I 10830 \AA~in absorption (equivalent width $\sim$ $0.012 \pm 0.002$ \AA) in the exosphere of a warm Neptune orbiting the M-dwarf GJ 3470, during three transits using the Habitable Zone Planet Finder (HPF) near infrared spectrograph. This marks the first reported evidence for He I 10830 \AA\, atmospheric absorption for a planet orbiting an M-dwarf. Our detected absorption is broad and its blueshifted wing extends to -36 km/sec, the largest reported in the literature to date. We modelled the state of Helium atoms in the exosphere of GJ3470b based on assumptions on the UV and X-ray flux of GJ 3470, and found our measurement of flux-weighted column density of meta-stable state Helium $(N_{He^2_3S} = 2.4 \times 10^{10} \mathrm{cm^{-2}})$, derived from our transit observations, to be consistent with model, within its uncertainties. The methodology developed here will be useful to study and constrain the atmospheric outflow models of other exoplanets like GJ 3470b which are near the edge of the evaporation desert.Comment: Accepted in Ap

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Extracting galactic binary signals from the first round of Mock LISA Data Challenges

We report on the performance of an end-to-end Bayesian analysis pipeline for detecting and characterizing galactic binary signals in simulated LISA data. Our principal analysis tool is the Blocked-Annealed Metropolis Hasting (BAM) algorithm, which has been optimized to search for tens of thousands of overlapping signals across the LISA band. The BAM algorithm employs Bayesian model selection to determine the number of resolvable sources, and provides posterior distribution functions for all the model parameters. The BAM algorithm performed almost flawlessly on all the Round 1 Mock LISA Data Challenge data sets, including those with many highly overlapping sources. The only misses were later traced to a coding error that affected high frequency sources. In addition to the BAM algorithm we also successfully tested a Genetic Algorithm (GA), but only on data sets with isolated signals as the GA has yet to be optimized to handle large numbers of overlapping signals.Comment: 13 pages, 4 figures, submitted to Proceedings of GWDAW-11 (Berlin, Dec. '06