Another in the series by John D’Souza on the components of twenty first century learning…
Data based decision making or data-driven decision making refers to educator’s on-going process of collecting and analyzing different types of data, including demographic, student achievement test, satisfaction, and process data to guide decisions towards the improvement of the educational process.
Using Data to Guide Instruction and Improve Student Learning:
- Data analysis can provide a snapshot of what students know, what they should know, and what can be done to meet their academic needs.
- When it comes to improving instruction and learning, it’s not the quantity of the data that counts, but how the information is used.
- This data-use support includes helping teachers use assessment results and student work samples to identify and address learning difficulties and academic needs.
Data use includes that all Teachers would:
- Engage in quality professional learning, at least, weekly to ensure delivery of effective instruction for students.
- Collect student data from several sources—responses on standardized tests, writing samples, and projects—and meet weekly to analyze, interpret, and use the data to adjust instruction and plan lessons.
Types of Data used in Education:
- Demographic Data: Student population, participation, attendance, and students with disabilities.
- Student Learning Data: Assessment of Knowledge and Skills in reading/ELA (English Language Arts) and Mathematics, Adequate Yearly Progress (AYP) in reading/ELA and Mathematics, and beginning-and-end-of-year data.
- Disciplinary Data: Office disciplinary referrals, suspensions, and disciplinary alternative education program.
- Perceptional Data: School culture, climate, and organizational processes.
- School Processes Data: Instructional processes, Organizational processes, Administrative processes, and Continuous school improvement processes.
Sources of Data:
- From the Classroom:
- Formative Assessments: Exit slips, brief quizzes, and thumbs up/thumbs down.
- Observations: Teacher steps away from the podium being “guide on the side” gathering data on individual students.
- Summative Assessments: Projects, Essays, Exams
- From Cumulative Files:
- A student who often misses classes could be homeless
- Students identified as gifted but inaccurately placed in general education class
- Student struggling to fit in diagnosed with schizophrenia, dyslexia
- Students, not wearing eyeglasses who have prescriptions
- Parents divorced, moved to a new city/community
3. From the Standardized Test:
- Share the testing results with students individually and set some obtainable, realistic goals for them to work towards.
- It reveals which of your students performed advanced, proficient, basic and below basic. This could help inform how you choose student groups, create seating charts, and differentiate for individuals.
- Students who don’t do so well on the standardized test, possibly nervous test-takers or could simply be low motivated.
Innovative Tools for Data-Based Teaching:
- Geddit: It encourages students to do a mini self-assessment called a ‘check-in’ at several points throughout the lesson. It allows doing instant checks-for-understanding, exit tickets, and mini quizzing. It lets you save student data to quickly and easily inform differentiation.
- Plickers: If you are not in a 1-to-1 environment, but you’re after formative assessment data, each student holds up a “paper clicker” and you can use your smartphone to scan the class. It allows doing instant checks-for-understanding, exit tickets, and mini quizzing. It lets you save student data to quickly and easily inform differentiation.
- Flubaroo: It’s a Google Doc script that lets you create online tests and quizzes. Grading students involve just a single click and all data can be converted into a report and then sent to them.
- LearnSprout: Turns raw, abstract data into actionable information for schools. Reports like this are important, they can help reveal patterns and trends hidden inside your data.
- Panorama Education: Assists to conduct surveys of students, teachers, staff and parents. Panorama supports school systems through the entire survey process, from survey design to administration to reporting and analysis to follow-through.
Data-Based Learning: Right Opportunities for the Desired Outcome
- Data-Based Learning has been recognized as one of the most important aspects of content and value generation in the 21st century.
- With the advent of ample data being available in real-time or at least near real time in Data-Based Learning, that becomes not only a possibility but a fact.
- With this kind of data-based learning approach, even backward students can catch up to the rest of the class at their own pace.
- Data-based learning focuses on teaching specifically the subjects that need to be taught at a pace comfortable to the students.
- With a data-based system, inculcating the right values and habits in a student becomes far easier as a continuous growth curve is available at any time for parents, teachers, and the students themselves to see.
Resources to Implement DBL:
- Edmodo: https://spotlight.edmodo.com/product/based-learning-4-data-based-learning–385261/
- Using CAT4 Data: https://spotlight.edmodo.com/product/using-cat-4-data–383477/
- Building Research Skills for Finding Compelling Data: https://spotlight.edmodo.com/product/building-research-skills-for-finding-compelling-data–385137/
- 10 Innovative Formative Assessment Examples for Teachers: https://spotlight.edmodo.com/product/10-innovative-formative-assessment-examples-for-teachers,385127/
- 23 Ways to Prepare Pupils for Exam: https://spotlight.edmodo.com/product/23-ways-to-prepare-pupils-for-exam,383157/
- CAT4 Result Analysis: https://spotlight.edmodo.com/product/cat-4-result-analysis,382985/
- Cambridge Checkpoint Result Analysis: https://spotlight.edmodo.com/product/cambridge-checkpoint-result-analysis–382987/