Traditional crime prediction techniques are slow and inefficient when generating predictions as crime increases rapidly \cite{r15}. To enhance traditional crime prediction methods, a Long Short-Term Memory and Gated Recurrent Unit model was constructed using datasets involving gender ratios, high school graduation rates, political status, unemployment rates, and median income by state over multiple years. While there may be other crime prediction tools, personalizing the model with hand picked factors allows a unique gap for the project. Producing an effective model would allow policymakers to strategically allocate specific resources and legislation in geographic areas that are impacted by crime, contributing to the criminal justice field of research \cite{r2A}. The model has an average total loss value of 70.792.30, and a average percent error of 9.74 percent, however both of these values are impacted by extreme outliers and with the correct optimization may be corrected.

Early warning systems (EWS) are predictive tools at the center of recent efforts to improve graduation rates in public schools across the United States. These systems assist in targeting interventions to individual students by predicting which students are at risk of dropping out. Despite significant investments in their widespread adoption, there remain large gaps in our understanding of the efficacy of EWS, and the role of statistical risk scores in education. In this work, we draw on nearly a decade's worth of data from a system used throughout Wisconsin to provide the first large-scale evaluation of the long-term impact of EWS on graduation outcomes. We present empirical evidence that the prediction system accurately sorts students by their dropout risk. We also find that it may have caused a single-digit percentage increase in graduation rates, though our empirical analyses cannot reliably rule out that there has been no positive treatment effect. Going beyond a retrospective evaluation of DEWS, we draw attention to a central question at the heart of the use of EWS: Are individual risk scores necessary for effectively targeting interventions? We propose a simple mechanism that only uses information about students' environments -- such as their schools, and districts -- and argue that this mechanism can target interventions just as efficiently as the individual risk score-based mechanism. Our argument holds even if individual predictions are highly accurate and effective interventions exist. In addition to motivating this simple targeting mechanism, our work provides a novel empirical backbone for the robust qualitative understanding among education researchers that dropout is structurally determined. Combined, our insights call into question the marginal value of individual predictions in settings where outcomes are driven by high levels of inequality.

Many higher-education institutions are now using data and analytics as an integral part of their processes. Whether the goal is to identify and better support pain points in the student journey, more efficiently allocate resources, or improve student and faculty experience, institutions are seeing the benefits of data-backed solutions. This article is a collaborative effort by Claudio Brasca, Nikhil Kaithwal, Charag Krishnan, Monatrice Lam, Jonathan Law, and Varun Marya, representing views from McKinsey's Public & Social Sector Practice. Those at the forefront of this trend are focusing on harnessing analytics to increase program personalization and flexibility, as well as to improve retention by identifying students at risk of dropping out and reaching out proactively with tailored interventions. Indeed, data science and machine learning may unlock significant value for universities by ensuring resources are targeted toward the highest-impact opportunities to improve access for more students, as well as student engagement and satisfaction.

Robotics is one of the fast-growing areas of technology that is opening doors to a wide range of industries such as security, automation, healthcare, consumer products, customized manufacturing, and interactive entertainment. According to the latest research of the U.S. The Bureau of Labor Statistics, Robotics Engineering is expected to grow 4% by 2028. The area of Robotics is likely to be in demand due to the emergence of new technologies. Since the future is of robots the demand and interest among robotics enthusiasts is also growing day by day. Here are the top online masters in robotics programs for robotic lovers.

College students are enrolled at each semester with either part time or full time status. While most of the students keep an overall constant enrollment status during their education period, some of them may frequently change their status between full time and part time from one semester to the next. The goal of this research is to exploit the historic patterns to estimate and categorize students$'$ strategy in three different groups of part time, full time and mixed, investigate the educational features of each group and compare their performance. Enrollment strategy refers to the student$'$s mindset for enrollment plan and in one way can be captured from the student$'$s historic enrollment status. Data is collected from the University of Central Florida from 2008 to 2017 and Hidden Markov Model is applied to identify different types of student strategy. Results show that students with Mixed Enrollment Strategy (MES) have features (ex. time to graduation and graduation and halt enrollment ratio) and performances (ex. cumulative GPA) relatively between students with Full time Enrollment Strategy (FES) and students with Part time Enrollment Strategy (PES).

A large public research university figured out how to tap the power of artificial intelligence and human intelligence to produce impressive gains in student success. Over the past ten years, the University of South Florida (USF) has experienced dramatic improvement in student success, as measured by the first-year persistence rate, the four-year graduation rate, and the six-year graduation rate. Each of those metrics has improved substantially. To promote persistence and completion, the university implemented a wide array of programs, practices, and policies based on a campus Student Success Task Force Report released in April 2010. These initiatives included many of the standard student success initiatives in place at many other colleges and universities, including living learning communities, the professionalization of academic advising, gradual increases in admissions requirements, course redesigns, expansion of on-campus housing, and promotion of student engagement.

Erick Trickey is a writer in Boston. For a year, Rodriguez has worked 40-hour weeks as an apprentice test technician, examining IBM mainframes to confirm they work before shipping them to customers. In January, she'll move to a permanent position with a future salary that she says is "definitely much more than I ever thought I'd be making at 19." Rodriguez's opportunities with IBM came to her thanks to her high school, Newburgh Free Academy P-TECH. It's part of an innovative public-school model that combines grade 9-12 education with internships and tuition-free community college. P-TECH, which stands for Pathways in Technology Early College High School, has spread to 10 states and 17 countries since its founding in Brooklyn in 2011. The P-TECH network is growing fast.

Today's classroom isn't just a place for education – it's also a laboratory, and teachers are expected to collect huge amounts of data, with the goal of improving learning outcomes. Despite the best intentions, however, this emphasis on educational data is especially onerous for already overworked teachers, meaning they need better tools to assist with collecting that data. That's where new recording strategies can help. Colleges were among the first to place a heavy emphasis on analytics because of their greater resources and research-driven agendas; and as such, they were the first to realize the value of educational data. For example, facing low graduation rates, colleges examined student records and discovered that students were struggling with English classes, even as they were thriving in other subject areas. Based on that data, colleges were able to address shortfalls in entering students' reading and writing skills and develop programs to enable them to succeed.

In January, Amazon opened Amazon Go, a high-tech, cashierless convenience store in Seattle. There are no checkout lines and few employees. The only requirement to shop is downloading an app. Customers just walk in, load up their bags, and go. There's no need to even scan purchases; cameras positioned overhead take note of items in customers' carts and add them to a virtual bill. Amazon Go is both an interesting novelty -- and a profound challenge to the livelihoods of the more than 3.5 million Americans who work as cashiers. Rumors of a coming wave of similar stores and robot-run factories have provoked apocalyptic predictions of mass unemployment among pundits and politicians.

While teachers may always be the best line of defense for students falling behind, busy schedules don't always permit the special attention and feedback that students need. That's where artificial intelligence–powered teaching assistants might come in handy. "These intelligent tools can adapt pacing based on the student's ability … and provide targeted, corrective feedback in case the student makes mistakes, so that the student can learn from them," states an eSchool News report released earlier this year. "These tools also gather actionable insights and information about a student's progress and report the data back to the teacher." Understandably, there is still some hesitation at the idea of using this technology, as education professionals fear the day robots will replace teachers.