How Data Driven Decision Making Essay

With data driving instructional practices students achieve more, and with students achieving more school move closer

The major emphasis in education for the 21st century is on data driven accountability measured by student performance on standardized testing. National and state expectations require students to demonstrate mastery of curriculum objectives. Instructional objectives are the focus of the building principals to show measurable student progress. The improvements are evaluated based on data and monitoring of the curriculum.

The district leaders rely heavily on data basis that gather sort and produce usable data to help in the decision making process. Like the individuals in the cases study, leader can become comfortable with the data without checking the validity and the reliability of the data. If the data is coming from MSIS database, which in managed, by Mississippi Department of Education, it is often taken for granted that the information is valid. However, data can be received from testing companies, educational assistance companies and district personnel. Therefore as the educational leaders make decisions they have to consistently check the source of the data. The leadership team should make sure that all data that is being used is valid and reliable. They should also ensure that each department knows what the other departments are doing and their efforts are aligned vertically and

Teachers are able to target the learning gaps by developing a plan of action based on the needs for our students. Verbiest (2014) and Hershkovitz (2015) argue data is used to tailor (how we sever students, how we offer support, types of support, what resources we need to invest on, whether we take a student to students needs with our school psychologist) instruction for students in all content areas in an effort to increase student achievement. As a result, the school can provide specific professional learning, support, and resources to teachers based on the needs and areas of weakness of our students (Fox, 2001). As lifelong learners, teachers recognize that their professional practice continues to evolve as they reflect and act on new information. If teachers have information that helps them confidently identify the root of educational challenges and track progress, they can more readily develop action plans that will have a positive impact on their students’ achievement (Halverson et al.,

Based on the article, my views about data-driven instruction has not changed. I am open to using data to help my students succeed. Meaningful, relevant data alleviates the mystery in learning. As educators, our instructional

The Wallace Foundation is conducting a 5-year study on the following large school districts: Charlotte-Mecklenburg Schools, North Carolina; Denver Public Schools, Colorado; Gwinnett County Public Schools, Georgia; Hillsborough County Public Schools, Florida; New York City Department of Education, New York; Prince George’s County Public Schools, Maryland. The study is called the Wallace Principal Pipeline Initiative (Wallace Foundation, 2013). According to the foundation, “The initiative’s theory of change holds that when

Data analysis is a procedure of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. There are multiple facts and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains in data analysis. For data analysis we have to mine the data first for our purpose such that the data we can handle easily. Basically for data analysis our first thing to do our planning, how we are going to collect the data, our going data going to make sense or not, actually data will be meaningful for our object, after

Information is data that has been processed so that it has meaning and value to a recipient,

Love, N., Stiles, K., Mundry, S., & DiRanna, K. (2008). The data coach’s guide to improving

20). Without a clear understanding of expectations, followers will have a difficult time making sense of goals, anticipated outcomes, and success criteria. As evidenced in observation data, Mr. Smith, Director of Elementary Education, provided direction for school administration, teachers, and instructional leaders at central office. He outlined professional development plans for elementary leadership for the 2016-2017 school year, developed a roll-out plan for standards-based reporting K-5, coordinated next steps for observation data related to school improvement planning, and established expectations for reporting student growth in literacy on a quarterly basis. Evident in his dialogue with teachers, administrators, and central office staff was his ability to listen to those in the trenches at the school level. While outlining expectations, goals, and success criteria, Mr. Smith understood that building a sense of community was critical; this leads to the next task of leadership – creating

Data comprises of factual information. Data are the facts from which information is derived. Data is not necessarily informative on its own but needs to be structured, interpreted, analysed and contextualised. Once data undergoes this process, it transforms in to information. Information should be accessible and understood by the reader without needing to be interpreted or manipulated in any way.

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

Most data-driven decisions are made through Analytics here at SJU. For example, I was recently involved in preparing the schedule for Department of Finance for fall semester and we used data analytics to do so. We ran a query that gave us a list of all students currently enrolled as Finance majors/minors. With this information, we could get the number of students and their classification of what year they were in. This information was then used to get the data about all the courses they have taken and what more courses they are required to take in the upcoming semester. With that information in hand, a schedule offering the required courses was prepared and resources were allocated.

The objective of this chapter is to describe the procedures used in the analysis of the data and present the main findings. It also presents the different tests performed to help choose the appropriate model for the study. The chapter concludes by providing thorough statistical interpretation of the findings.

Due to the rapid growth in the use of Internet and its connected tools, an enormous amount of data are being produced on a daily basis. The concept of big data arrives when we were unable to manage this huge data with traditional methods. Big data is a mechanism of capturing, storing and analyzing the big datasets and also an idea of extracting some value from it. It is very handful while determining the root causes of failures, issues and defects in near-real time, creating coupons and other sales offers according to the customers shopping patterns, detecting any suspicious and fraudulent activities in real-time. As it is very advantageous, it also has some issues. Some of the common issues can be characterized into heterogeneity, complexity, timeless, scalability and privacy. The most important and significant challenge in the big data is to preserve privacy information of the customers, employees, and the organizations. It is very sensitive and includes conceptual, technical as well as legal significance.