An educational researcher's guide to ChatGPT: How it works and how to use it

In an era of rapid technological advancements, ChatGPT has emerged as a revolutionary tool in educational research, offering opportunities to enhance research efficiency and foster innovative thinking. This article provides a comprehensive guide to understanding and utilizing ChatGPT effectively in educational research. The operational mechanisms of ChatGPT are explained, along with a discussion of practical applications such as content creation, literature summarization, statistical analysis, and idea generation. A critical discussion of prompt engineering highlights strategies for crafting inputs that optimize AI responses. Examples include paying explicit attention to both the content of the prompt as well as how the prompt is phrased. Advanced features, such as web integration, customized GPTs, and Python-based data analysis, are explored to showcase the expanding possibilities of generative AI in educational contexts. The potential for ChatGPT to streamline workflows is contrasted with ethical considerations, including risks of misinformation, inherent biases, and privacy concerns. Researchers are urged to validate outputs and disclose AI usage transparently to maintain research integrity. While recognizing the challenges, the article underscores ChatGPT’s potential to improve educational research by enabling more accessible, efficient, and collaborative practices. Researchers are encouraged to adapt to these evolving tools, embracing their potential while remaining vigilant to ethical considerations and limitations

Effect of future CO2 and temperature regimes on phytoplankton community composition, biomass and photosynthetic rates in the western English Channel

CO2 storage in the oceans is strongly affected by biological processes. Production of organic matter through phytoplankton photosynthesis drives CO2 sequestration, which feeds back to atmospheric CO2 and global climate. The ongoing increase in atmospheric CO2 and temperature is strongly associated with changes in ocean chemistry and increasing seawater temperatures. To investigate these impacts on coastal phytoplankton under conditions predicted for the year 2100 (pCO2 elevated to 800 µatm and +4 °C temperature), three factorial experiments were conducted with natural communities sampled from the western English Channel (WEC). Elevated pCO2 increased phytoplankton biomass by up to 20-fold while elevated temperature resulted in an increase of up to 14-fold. Light-saturated photosynthetic carbon fixation rates increased > 6-fold under elevated pCO2 while an increase of up to 3-fold resulted from elevated temperature. The combined effects of elevated pCO2 and temperature reduced biomass in late summer and had no effects on biomass in the autumn with no significant effects on photosynthetic carbon fixation rates in either season. Individual treatments of elevated pCO2 and temperature resulted in near mono-specific communities: diatoms in late summer and nanophytoplankton in autumn. Combined effects of both factors resulted in the most diverse phytoplankton communities and promoted increased dinoflagellate and Synechococcus biomass at the expense of diatoms and nanophytoplankton. Elevated pCO2 alone promoted dominance of the harmful algal bloom (HAB) species, Phaeocystis in spring and autumn, while the combination of elevated pCO2 and temperature promoted biomass of the HAB species, Prorocentrum minimum in autumn. The results indicate that experimental simulations of year 2100 pCO2 and temperature may significantly modify phytoplankton community structure with a positive feedback on atmospheric CO2 in late summer and no change on feedback in autumn. In either scenario, no increase in phytoplankton productivity during a period of changes in bulk carbonate chemistry resulting from ongoing anthropogenic carbon uptake, may be expected to negatively influence carbon biogeochemistry in the WEC

Unconfirmed Near-Earth Objects

We studied the Near-Earth Asteroid (NEA) candidates posted on the Minor Planet Center's Near-Earth Object Confirmation Page (NEOCP) between years 2013 and 2016. Out of more than 17,000 NEA candidates, while the majority became either new discoveries or were associated with previously known objects, about 11% were unable to be followed-up or confirmed. We further demonstrate that of the unconfirmed candidates, 926+/-50 are likely to be NEAs, representing 18% of discovered NEAs in that period. Only 11% (~93) of the unconfirmed NEA candidates were large (having absolute magnitude H<22). To identify the reasons why these NEAs were not recovered, we analyzed those from the most prolific asteroid surveys: Pan-STARRS, the Catalina Sky Survey, the Dark Energy Survey, and the Space Surveillance Telescope. We examined the influence of plane-of-sky positions and rates of motion, brightnesses, submission delays, and computed absolute magnitudes, as well as correlations with the phase of the moon and seasonal effects. We find that delayed submission of newly discovered NEA candidate to the NEOCP drove a large fraction of the unconfirmed NEA candidates. A high rate of motion was another significant contributing factor. We suggest that prompt submission of suspected NEA discoveries and rapid response to fast moving targets and targets with fast growing ephemeris uncertainty would allow better coordination among dedicated follow-up observers, decrease the number of unconfirmed NEA candidates, and increase the discovery rate of NEAs.Comment: 35 pages, 19 figures, 8 table

Linking a dermal permeation and an inhalation model to a simple pharmacokinetic model to study airborne exposure to di(n-butyl) phthalate

Six males clad only in shorts were exposed to high levels of airborne di(n-butyl) phthalate (DnBP) and diethyl phthalate (DEP) in chamber experiments conducted in 2014. In two 6 h sessions, the subjects were exposed only dermally while breathing clean air from a hood, and both dermally and via inhalation when exposed without a hood. Full urine samples were taken before, during, and for 48 h after leaving the chamber and measured for key DnBP and DEP metabolites. The data clearly demonstrated high levels of DnBP and DEP metabolite excretions while in the chamber and during the first 24 h once leaving the chamber under both conditions. The data for DnBP were used in a modeling exercise linking dose models for inhalation and transdermal permeation with a simple pharmacokinetic model that predicted timing and mass of metabolite excretions. These models were developed and calibrated independent of these experiments. Tests included modeling of the “hood-on” (transdermal penetration only), “hood-off” (both inhalation and transdermal) scenarios, and a derived “inhalation-only” scenario. Results showed that the linked model tended to duplicate the pattern of excretion with regard to timing of peaks, decline of concentrations over time, and the ratio of DnBP metabolites. However, the transdermal model tended to overpredict penetration of DnBP such that predictions of metabolite excretions were between 1.1 and 4.5 times higher than the cumulative excretion of DnBP metabolites over the 54 h of the simulation. A similar overprediction was not seen for the “inhalation-only” simulations. Possible explanations and model refinements for these overpredictions are discussed. In a demonstration of the linked model designed to characterize general population exposures to typical airborne indoor concentrations of DnBP in the United States, it was estimated that up to one-quarter of total exposures could be due to inhalation and dermal uptake

Effects of elevated CO2 on phytoplankton community biomass and species composition during a spring Phaeocystis spp. bloom in the western English Channel

A 21-year time series of phytoplankton community structure was analysed in relation to Phaeocystis spp. to elucidate its contribution to the annual carbon budget at station L4 in the western English Channel (WEC). Between 1993–2014 Phaeocystis spp. contributed ∼4.6% of the annual phytoplankton carbon and during the March − May spring bloom, the mean Phaeocystis spp. biomass constituted 17% with a maximal contribution of 47% in 2001. Upper maximal weekly values above the time series mean ranged from 63 to 82% of the total phytoplankton carbon (∼42–137 mg carbon (C) m −3 ) with significant inter-annual variability in Phaeocystis spp. Maximal biomass usually occurred by the end of April, although in some cases as early as mid-April (2007) and as late as late May (2013). The effects of elevated pCO 2 on the Phaeocystis spp. spring bloom were investigated during a fifteen-day semi-continuous microcosm experiment. The phytoplankton community biomass was estimated at ∼160 mg C m −3 and was dominated by nanophytoplankton (40%, excluding Phaeocystis spp.), Phaeocystis spp. (30%) and cryptophytes (12%). The smaller fraction of the community biomass comprised picophytoplankton (9%), coccolithophores (3%), Synechococcus (3%), dinoflagellates (1.5%), ciliates (1%) and diatoms (0.5%). Over the experimental period, total biomass increased significantly by 90% to ∼305 mg C m −3 in the high CO 2 treatment while the ambient pCO 2 control showed no net gains. Phaeocystis spp. exhibited the greatest response to the high CO 2 treatment, increasing by 330%, from ∼50 mg C m −3 to over 200 mg C m −3 and contributing ∼70% of the total biomass. Taken together, the results of our microcosm experiment and analysis of the time series suggest that a future high CO 2 scenario may favour dominance of Phaeocystis spp. during the spring bloom. This has significant implications for the formation of hypoxic zones and the alteration of food web structure including inhibitory feeding effects and lowered fecundity in many copepod species

Learning regexes to extract router names from hostnames

We present the design, implementation, evaluation, and validation of a system that automatically learns to extract router names (router identifiers) from hostnames stored by network operators in different DNS zones, which we represent by regular expressions (regexes). Our supervised-learning approach evaluates automatically generated candidate regexes against sets of hostnames for IP addresses that other alias resolution techniques previously inferred to identify interfaces on the same router. Conceptually, if three conditions hold: (1) a regex extracts the same value from a set of hostnames associated with IP addresses on the same router; (2) the value is unique to that router; and (3) the regex extracts names for multiple routers in the suffix, then we conclude the regex accurately represents the naming convention for the suffix. We train our system using router aliases inferred from active probing to learn regexes for 2550 different suffixes. We then demonstrate the utility of this system by using the regexes to find 105% additional aliases for these suffixes. Regexes inferred in IPv4 perfectly predict aliases for ≈85% of suffixes with IPv6 aliases, i.e., IPv4 and IPv6 addresses representing the same underlying router, and find 9.0 times more routers in IPv6 than found by prior techniques

STEM Hub Capacity Building to Support Evaluative Thinking and Continuous Improvement: An interim report prepared for Oregon’s Chief Education Office

The Educational Policy Improvement Center (EPIC) and the Center for Research on Lifelong STEM Learning at Oregon State University (OSU) are collaborating on a yearlong capacity-building research project in order to better understand the common and unique features and strengths of Oregon’s STEM Hubs. The project is intended to examine the growth and success of the STEM Hubs and their backbone organizations, and to build capacity for evaluative thinking by supporting Hubs in data-driven decision making and continuous improvement. The project is the first of its kind to systematically examine multiple layers of collaboration between publicly funded STEM-focused organizations, partner organizations, and their broader communities. STEM Hubs are multisector partnerships that link local P–20 educators, workforce and economic development groups, community-based organizations, and business/industry representatives in a collaborative effort to transform the landscape of STEM (Science, Technology, Engineering, and Mathematics) and CTE (Career and Technical Education) teaching and learning. STEM Hubs are implementing strategies that include (amongst others) educator professional development on best practices in STEM instruction; in-and out-of-school, hands-on STEM learning experiences for students; and connections to fast-growing STEM employment opportunities in Oregon

Network hygiene, incentives, and regulation: Deployment of source address validation in the internet

The Spoofer project has collected data on the deployment and characteristics of IP source address validation on the Internet since 2005. Data from the project comes from participants who install an active probing client that runs in the background. The client automatically runs tests both periodically and when it detects a new network attachment point. We analyze the rich dataset of Spoofer tests in multiple dimensions: across time, networks, autonomous systems, countries, and by Internet protocol version. In our data for the year ending August 2019, at least a quarter of tested ASes did not filter packets with spoofed source addresses leaving their networks. We show that routers performing Network Address Translation do not always filter spoofed packets, as 6.4% of IPv4/24 tested in the year ending August 2019 did not filter. Worse, at least two thirds of tested ASes did not filter packets entering their networks with source addresses claiming to be from within their network that arrived from outside their network. We explore several approaches to encouraging remediation and the challenges of evaluating their impact. While we have been able to remediate 352 IPv4/24, we have found an order of magnitude more IPv4/24 that remains unremediated, despite myriad remediation strategies, with 21% unremediated for more than six months. Our analysis provides the most complete and confident picture of the Internet's susceptibility to date of this long-standing vulnerability. Although there is no simple solution to address the remaining long-tail of unremediated networks, we conclude with a discussion of possible non-technical interventions, and demonstrate how the platform can support evaluation of the impact of such interventions over time

Distributed modeling of ablation (1996–2011) and climate sensitivity on the glaciers of Taylor Valley, Antarctica

The McMurdo Dry Valleys of Antarctica host the coldest and driest ecosystem on Earth, which is acutely sensitive to the availability of water coming from glacial runoff. We modeled the spatial variability in ablation and assessed climate sensitivity of the glacier ablation zones using 16 years of meteorological and surface mass-balance observations collected in Taylor Valley. Sublimation was the primary form of mass loss over much of the ablation zones, except for near the termini where melt, primarily below the surface, dominated. Microclimates in ~10 m scale topographic basins generated melt rates up to ten times higher than over smooth glacier surfaces. In contrast, the vertical terminal cliffs on the glaciers can have higher or lower melt rates than the horizontal surfaces due to differences in incoming solar radiation. The model systematically underpredicted ablation for the final 5 years studied, possibly due to an increase of windblown sediment. Surface mass-balance sensitivity to temperature was ~−0.02 m w.e. K−1, which is among the smallest magnitudes observed globally. We also identified a high sensitivity to ice albedo, with a decrease of 0.02 having similar effects as a 1 K increase in temperature, and a complex sensitivity to wind speed