Discuss about the Report on Integration of Technology?
Data-driven decision making has become an important topic linked to school improvement, accountability and educational reforms. Education policy maker pronounces “cool” to data. Use of data can never be a passing fad for which the educationalists will close their doors and assume that it is enough to use it until new innovative idea will appear. Technology plays a major role in data driving. Technology is integrated to individual as well as with the organizations also. Decision making for driving data is an innovative process but in education process data driving is not new. Extremely operative school and institutes educators have been using statistics for long era and identify the value to notify their task across all stages of the learning system. Objective of this report is to gain and understand the needs, requirements and different perspectives of efforts required for development, introduction and to offer learning involvements on data-driven decision making. It also required recognizing various perceptions on the several senses of data-driven decision making and exemplifying the aids involved in learning decision making. A shift for teachers is represent by the example for data- driven decision making- a day to day shift that trains teachers and emphasizes them to process and deliver the classroom lectures that is dedicated to achievement of results. Practices of education are evaluated in flame because of their direct effect on students. Schools and institutes are new focus of data driving.
Concept of Data-driven education:
A most important task for instructors is divide data driven decision making into five elements as follows:
Good referred data
Instructional goal should be measurable
Formative assessment should be frequent
Focused instructional interventions
Collecting and Analyzing Summative Data:
An assessment to improve the learning of student utilizes data from year wise summative. Educators are required to be able to get their hand on the data from year wise summative assessment that will help them to improve the instructional exercises.
When the baseline information is forwarded to the teachers they are required to work with the managers to choose main pointers of success for their classroom. Teachers are required to be perfect in assessment of literature concepts. Teachers also need to provide feedback to founding and higher-level administrators about the practicality of the figures or reports that are received by them.
Analysis of the test scores and achievements of the student:
Now these forward thinking regions across the country are using data-driven decision making methods to analyze the test scores and achievements of the student as well as :
Fulfill the gap between the student subgroups
It improves the quality of teachers
Curriculum of teaching is improved
Best practices are shared between the schools and other regions
Communication of educational issues
Involvement of parents is promoted in the education process
In education community dialogues are increases.
Figure shows the full-scale data-driven decision making model. A good technology uses all the features of this model. Information can be collected from diverse sources of databases. These systems are Student Information Systems (SIS), Finance and transport and human resources based. Many regions also have materials kept in a huge array of specific catalogues, collecting information on items like different specific educational programs. Inventory audits are most important way of collecting data. In a simple manner data-driven decision making is to collect data elements and to explore the factors for both student and teacher for positive and negative contribution. Correlation of data element is not possible without collection.
Data-driven decision making is a powerful process for different regions of the country. Key of success of data-driven decision making is to understands the vision and analyze school performances. It recognizes various perceptions on the several senses of data-driven decision making and exemplifying the aids involved in learning decision making. A shift for teachers is represent by the example for data- driven decision making- a day to day shift that trains teachers and emphasizes them to process and deliver the classroom lectures that is dedicated to achievement of results. The technologies required for data driving are described above in form of the concept of data-driven education, analysis of summative data and analysis of the test scores and achievements of the student.
McIntire, T., (2002), The Administrator’s Guide to Data-Driven Decision Making, Technology &
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(2003), Making Sense of the Data. Overview of the K-12 Data Management and Analysis Market.
A report produced by Eduventures.
(2002), Using Data to Improve Schools: What’s Working. A report produced by the American
Association of School Administrators.