A Look at the Effectiveness of High School Chemistry Curriculum in Preparing Students for ACT

In the ever-changing world, students are challenged with cultivating the skills and knowledge needed to handle the pace and level of understanding required to excel in their future. The foundation for a student\u27s future begins in their formative years, but high school is a prime environment for nurturing the applied, critical thinking, and problem-solving skills needed to move forward into independent, adult life. Mississippi schools are ranked by an accountability score, which is used to determine fund allocation and the development of improvement plans. This score is compiled by looking at various state-tested courses, College and Career Readiness Standards (MS CCRS) scores (including the ACT), and graduation rates. Chemistry is not an accountability subject, but students who take chemistry also take the ACT in the same year. In this case, the ACT serves as a tool for accountability and a tool for predicting college readiness and success (ACT.org, 2016). Given that the skills needed to succeed in chemistry are also needed to succeed on the ACT, it seems prudent to find ways to help students understand the chemistry content while simultaneously strengthening the skills to do well on the ACT Science sub-test. To address this, a two-tiered study was conducted over five years to determine if integrating an Inquiry-Based (IBL) method, specifically Process Oriented Guided Inquiry Learning (POGIL), would benefit student chemistry success and increase scores on the ACT. The first two years looked at the effects of POGIL integration by comparing 3 assessment scores (Pre-test, Post-test, and ACT science sub-test). Years 3-5 sought to establish a difference between teaching methods by comparing the effects of POGIL integration versus non-POGIL integration The POGIL and non-POGIL classes were taught by two different teachers, and the scores were compared through the 3 same assessments (Pre-test, Post-test, and ACT Science sub-test). The research significantly impacts student ACT Science scores over a five-year period. The two-tiered study indicated that students were better prepared to be successful on the ACT science test. The change came through using critical thinking in the chemistry classroom in controlled environments and helping students build capacity with those skills

Flight Data of Airplane for Wind Forecasting

This research solely focuses on understanding and predicting weather behavior, which is one of the important factors that affect airplanes in flight. The future weather information is used for informing pilots about changing flight conditions. In this paper, we present a new approach towards forecasting one component of weather information, wind speed, from data captured by airplanes in flight. We compare NASA’s ACT-America project against NOAA’s Wind Aloft program for prediction suitability. A collinearity analysis between these datasets reveals better model performance and smaller test error with NASA’s dataset. We then apply machine learning and a genetic algorithm to process the data further and arrive at a competitive error rate. The sliding window approach is used to find the best window size, and then we create a forecasting model that predicts wind speed at high altitudes 10 mins ahead of time. Finally, a stacking-based framework was used for better performance than individual learning algorithms to get root means square error (RMSE) of the best combination as 0.674, which is 98.4% better than the state-of-the-art approach

Academic College Readiness Indicators of Seniors Enrolled in University-Model Schools® and Traditional, Comprehensive Christian Schools

This correlational study examined the relationship between type of high school a senior attends (University-Model School® [UMS®] or traditional, comprehensive Christian) and academic college readiness, when controlling for prior academic achievement and gender. The study compared archival data of Christian school students from six Texas schools. The Stanford-10 controlled for prior academic achievement. SAT and ACT scores measured academic college readiness. Results of three sequential multiple regressions, controlling for confounding, found school type to be a statistically significant predictor for the SAT Composite score, but not for the SAT Writing score or the ACT Composite score. Although the UMS® seniors averaged higher scores than traditional, comprehensive Christian school seniors on all three exams, only the SAT Composite score was found to be statistically significant. The standardized regression coefficient of the three scores did not find practical significance for the relationship between school type and academic college readiness

On the Actionability of Outcome Prediction

Predicting future outcomes is a prevalent application of machine learning in social impact domains. Examples range from predicting student success in education to predicting disease risk in healthcare. Practitioners recognize that the ultimate goal is not just to predict but to act effectively. Increasing evidence suggests that relying on outcome predictions for downstream interventions may not have desired results. In most domains there exists a multitude of possible interventions for each individual, making the challenge of taking effective action more acute. Even when causal mechanisms connecting the individual's latent states to outcomes is well understood, in any given instance (a specific student or patient), practitioners still need to infer -- from budgeted measurements of latent states -- which of many possible interventions will be most effective for this individual. With this in mind, we ask: when are accurate predictors of outcomes helpful for identifying the most suitable intervention? Through a simple model encompassing actions, latent states, and measurements, we demonstrate that pure outcome prediction rarely results in the most effective policy for taking actions, even when combined with other measurements. We find that except in cases where there is a single decisive action for improving the outcome, outcome prediction never maximizes "action value", the utility of taking actions. Making measurements of actionable latent states, where specific actions lead to desired outcomes, considerably enhances the action value compared to outcome prediction, and the degree of improvement depends on action costs and the outcome model. This analysis emphasizes the need to go beyond generic outcome prediction in interventional settings by incorporating knowledge of plausible actions and latent states.Comment: 14 pages, 3 figure