Teacher Leader- Data decision making
Data-driven decision-making (DDDM) is a strategy of teaching and managing procedures where teachers obtain better information on certain students. The system uses students’ appraisal data and relevant background information in order to come up with decisions that correlate with instructional strategies at classroom, institutional, regional, and at individual levels (Kowalski et al., 2008). Furthermore, in the system of DDDM, the key elements that characterise it are: instructional aims that are attainable and can be measured; baseline data that is excellent; focused instructional involvements; specialized learning communities; and frequent decisive assessments (Kowalski et al., 2008).
Knowledge based decision making is a system that is based on strategic thinking where the focus is pursuing the right questions that are directed to a student (Preuss, 2007). The questions in this system are followed by an in-depth discussion that is well organised to come up with as many possibilities as possible. Decisions that are arrived at in this system are not known to be right but are based on the information that is obtained in the discussion. In this system, the key elements characterising it are: dialoguing before reflection; planning and thinking tactically; and trust and respect between the teacher and student (Preuss, 2007).
In looking at the definitions of the two systems of decision making in a school set up, there are differences that result in terms of methodology. Data-based decisions are made based on information collected over time while in knowledge-based, decisions are achieved through discussions on set questions. The out-put of the two systems are also different where in the case of data based, information is the only one obtained (Bernhart, 2007). In knowledge based, information, decisions, answers, expert opinions, and recommendations are obtained (Bernhart, 2007). Lastly, there is also a difference in duration that is taken in carrying out the systems where data-based takes more time but more accurate information is acquired.
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Continuous Improvement for Teaching and Learning
As a teacher leader, there are various important procedures that must be implemented to make sure that learning and teaching in the institution are in constant improvement. These procedures can be divided into seven domains in accordance to the model teacher leader standards of 2010 (Kowalski et al., 2008). Firstly, the teacher leader has to see and view adults as learners so as to use the information to promote a culture of shared accountability that would result in maximum effectiveness of teachers, and drive continuous improvement in teaching and learning.
Secondly, there should be deeper understanding and utilization of research so as to improve practice and student achievements. This can be attained through: working with other teachers in evaluating data; smoothen the analysis of student data; and encouragement of research methods in teaching. Thirdly, there is promotion of professional learning so that learning is made continuous. This can be done by: assessing the learning needs of colleagues and responding to them; preparing colleagues to analyze and interpret data of students; and providing feedbacks for improving teaching practice and learning.
Fourthly, there should be improvement of student learning and instruction through: promotion of instruction systems that are student-centred; collection of data that would upgrade the curriculum and instruction; and being a team leader to other colleagues. Fifthly, the teacher leader should strictly use appraisals and data for improvements of the school and district. Sixthly, the teacher leader should find ways to integrate the community with the school by: arranging social functions that will bring together families, the community, and the school; and guiding colleagues to identify resources in the community that will improve learning. Lastly, the teacher leader should campaign for student learning and teaching profession by: sharing information on national and state policies impact on learning and trends; advocating for usage of professional resources; and collaborate with colleagues in identifying opportunities that promote rights of students.
Types of Data Used
The types of data that can be used entail: demographic; process; student learning; and perception data. They are all used for the sole purposes of: screening students; diagnosing of problems in learning; monitoring performances; and measuring the outcome of an institution or student (Bernhardt, 2007). In the case of demographic data they are used for the screening students where a concern is identified and then the level of concern put in place. Demographic data that can be used are: age; gender; ethnicity; achievements up to date; and even grade level. These data can be obtained when students enter into an institution and some recorded over certain duration.
Data on student learning can be used for monitoring individual student progress data and can be obtained regularly on a daily basis. In monitoring things that are considered are whether a system is working or if the gap between performance rates is closing. Examples of these data are: standardized assignments; proficient levels; entrance exams of colleges; and progress levels.
Data on process is used for diagnostic reasons where lacking skills are identified and what needs to be taught identified. Process data is filled in daily and looked at in case a problem arises. Example of process data are: retention, attendance, graduation, drop-out, and disciplinary rates.
The last data on perception is important in schools for gauging the outcome of services being offered. The outcome is obtained by predicting on data obtained from screening and monitoring in the prior data. Examples of perception data are perception of: student before and after graduation; teachers and staff; and from parents among others.
Bernhardt, V. L. (2007). Translating data into information to improve teaching and learning. New York: Eye on Education. Pp. 23-43
Kowalski, T. J., Lasley, T. J., & Mahoney, J. W. (2008). Data-driven decisions and school leadership: best practices for school improvement. New Jersey: Pearson/ Allyn and Bacon. Pp. 13-105
Preuss, P. G. (2007). Data-based decision making and dynamic planning: a school leader’s guide. New York: Eye on Education. Pp. 7-23