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This was the slide Lixun shared with KM dept regarding KM moving forward.

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Useful of strategic planning exercise

- Clarify purpose (mission, vision, values)

- Use multiple data sources (demographics, perceptions, learning, processes)

- Identify root causes of gaps

- Develop targeted improvement plans

- Monitor progress and adjust strategies

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Data can be categorised into 4 domains:

Demographics

Perceptions

Student Learning

School Processes

The model encourages schools to look at how these data sets intersect, giving deeper insights into why certain results occur.

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From the reading of Bernhardt Victoria.

1) The reading makes me reflect about our vision: Learners of Character and Leaders in Action.

Are there KPIs that show we are progressing towards this? Such indicators would help us assess if we are growing in this direction.

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Mission:

To promote a culture where data is used purposefully to strengthen leadership, enhance teaching and support the holistic development of every student.

Vision:

A school where data empowers informed decisions, drives continuous improvement and ensures that every learner thrives.

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Traver, R. (2000). Creating data-driven schools. Educational Leadership, 57(5). Retrieved from https://www.ascd.org/el/articles/creating-data-driven-schools​

The article emphasizes that schools and districts can make more effective decisions by systematically using student data. Many schools underutilize data, missing opportunities to evaluate program impact or ensure equity. A data-driven culturerequires intentional effort and coordination, where data informs decisions about curriculum, instruction, and resource allocation.

Application: One powerful way is to show how a department can use data to effect changes. (eg. social connectogram)

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Many school districts underutilize one of the most powerful and common symbol systems available to them-numbers-to monitor, evaluate, and revise programs and policies. For example, in a school system committed to educating both sexes equally, no one bothers to figure out the ratio of boys to girls enrolled in advanced placement classes in science and mathematics to ensure gender equity. Or, on a larger scale, a school district that significantly increases its professional development budget makes no effort to determine quantitatively whether the extended professional development increases teacher knowledge and morale or improves student achievement.

Imagine instead that district decision makers pay close attention to numerical patterns to help determine how well schools are doing and what they should do next. As a result, schools recognize gender disparity, commit themselves to gender parity, and develop action plans to remedy the problem. In addition, decision makers request that the building-level administrators and teachers continue monitoring the representation and success of boys and girls in mathematics and science courses, charting the results periodically to measure policy effectiveness. They use a similar approach to determine and adjust the district professional development plan. When districts and schools use data to make decisions, they have the makings of a data-driven school culture.

A Data-Driven School Culture

What are the components of a data-driven school culture? What are examples from schools?

In Sound Public Schools (which has 4,500 students, 38 percent of whom are eligible for free or reduced lunch), the school district analyzed 4th grade scores from the high-stakes Massachusetts statewide exam, the Massachusetts Comprehensive Assessment System (MCAS). The district noticed that in each content area (mathematics, science, and English/language arts), writing fluency correlated strongly with overall proficiency. Specifically, the district compared the percentages of students who did not answer open-response questions, which require composing descriptions and explanations, with the percentages of multiple-choice response rates. For each of the three content areas, 35 to 45 percent of students did not even attempt to answer the open-response questions, whereas only 1 to 3 percent did not respond to the multiple-choice items.

This discrepancy prompted the district to examine student performance on other standardized assessments. To further diagnose student readiness for the 10th grade MCAS, Sound administered the Terra Nova exam at grade 9. An analysis of scores showed a close association between reading proficiency on the Terra Nova and subject matter knowledge as measured by the MCAS.

This close association led the school to implement performance assessments in reading and writing to students at each grade level. Teachers score the written work of students on an eight-level literacy scale, enter the information onto spreadsheets for their own analysis, and forward the data to the central office for use in the district's student database. After the reading and test trends are quantitatively documented, teachers provide extra support for students with the weakest skills. This helps teachers meet a crucial goal in the district improvement plan: reduce the lowest level of literacy by 25 percent each year.

Even relatively simple data patterns can suggest instructional responses. Sound's assistant superintendent reported that One major finding of the MCAS analysis was that many of our students were simply not familiar with the vocabulary of the assessment. For example, they didn't know that the word sum meant add. A systemwide list of necessary vocabulary has been prepared for classroom use at specific levels.

This example shows that in a data-driven culture, there is an institutionalized willingness to use numbers systematically to reveal important patterns and to answer focused questions about policy, methods, and outcomes. A central data team relies on input from teachers, who are ultimately responsible for student success. The team and the teachers link to the district's central office so that their work is not separate from other efforts to collect and use data. In other words, a data-driven culture is both localized and systemic.

In addition, a data-driven culture requires a few knowledgeable staff members, supportive administrators, and institutionalized procedures for distributing data-collection instruments, retrieving data, writing reports, and informing decision making. Many important queries require only paper, a pencil, and a calculator. More ambitious projects require desktop statistical software and a staff member who knows how to use it. Happily, such software is becoming more affordable, user-friendly, and powerful.

Regardless of whether the effort is small or large, the school district can become more informed and confident about the progress and impact of program policy and methods. With that knowledge and confidence, administrators, teachers, and stakeholders can respond proactively, rather than reactively, to demands for accountability.

Measuring the Impact of Curriculum Adoption

Beyond diagnostics, when a district wants to measure the impact of a large-scale initiative, it needs a data-driven culture. For example, in the Halogen Public School District (which has 2,500 students, 11 percent of whom are eligible for free or reduced lunch), numbers inform the district's decisions about curriculum and instruction. In monthly meetings, teachers, principals, and curriculum coordinators examine student performance on the MCAS, plan professional development, recommend curriculum revisions, and create strategies to engage parents.

The director of Halogen's elementary mathematics and science curriculum states the need: When committees of teachers began to align current curriculum and instructional practice with state expectations, it became clear that we had significant content gaps and inconsistencies between schools and teachers, and we needed to shift from didactic to inquiry-based pedagogy. In addition, we saw the need for student assessment data that reflected the content and instructional methodology envisioned in the state curriculum frameworks.

In another series of meetings, elementary teachers met in cross-grade clusters to review school- and classroom-level reports, as well as individual student data from the MCAS. Staff then met by grade level to develop plans for revising the curriculum to improve student performance on the MCAS. Middle and high school teachers also met monthly to map strengths and weaknesses of student performance on the MCAS and Terra Nova tests against current instructional content. Teachers then completed a self-assessment survey in which they specified what they teach in their classrooms and what their perceived level of expertise is in particular subjects. The data from the survey helped the district examine the alignment of its curriculum with the demands of the MCAS, locate gaps and redundancies, plan professional development, and recommend changes for content and instructional sequence.

Halogen has turned to its increasingly sophisticated data-driven culture to examine the impact of using a commercial standards-based curriculum in mathematics. Schools within the district are at different stages of implementing the curriculum. In all schools, scores on the California Achievement Test showed significant gains in mathematical reasoning and problem solving. But the district dug more deeply into its analysis by comparing demographically similar schools that were in different stages of the curriculum implementation.

One school has used the curriculum for three years; the other has only recently come on board. In the first school, 50 percent of the students were either proficient or advanced on the grade 4 MCAS, whereas 36 percent of the students at the second school were proficient or advanced. In fact, the first school outperformed the second school in every area of mathematics, with the greatest differences in patterns and relationships, algebra/mathematical structure, and open response.

Information from the Terra Nova test supported the finding that the first school was gaining ground. Mathematical performance in the first school exceeded national averages by a substantial 16 to 17 percent in communication, geometry, number sense, problem solving, reasoning and measurement-areas of instruction frequently neglected in more traditional mathematics curriculums.

Overall, with this kind of sophisticated data, the district is prioritizing its challenges more knowledgeably, allocating its resources more efficiently, and documenting its work to all stakeholders.

Data-driven school cultures do not arise in a vacuum. They need a major motivator and technical and financial support. In Massachusetts, much of this has come from the Department of Education. The MCAS has riveted the attention of school districts because it is a high-stakes exam, it reports results at classroom and student levels, and it requires student-composed answers to its open-response and essay questions. Before the exam was mandated, student-composed answers were not a common instructional focus in Massachusetts schools.

To date, the Partnerships Advancing the Learning of Mathematics and Science (PALMS) project, a National Science Foundation state systemic initiative, has been a major provider of funds and advice for setting up data-driven cultures. Grants to 26 school districts, including Sound, Halogen, and Unified (described below), range from $4,500 to $65,000. Although focused on mathematics, science, technology, and engineering, PALMS support extends to all areas of the curriculum and to the data-driven needs of both the district and the teacher.

No One Right Way

In brief, PALMS finds that data-driven school cultures take hold in one of two ways. In larger school districts, data-driven efforts reside within institutional research and development units. In small districts, data-driven efforts usually begin with the work of one person who has a quantitative bent, enough curiosity to look for patterns, and, most important, a willingness to share results with colleagues and to solicit their views. Occasionally, outside consultants augment these efforts.

Data-driven teams organize in response to different needs and opportunities. For example, some data teams start by enhancing or developing information management systems within their district. Others collect and analyze data related to specific research questions: How do curriculum and instruction relate to student achievement, or what specific professional development is needed? Many districts try to determine which input and process indicators influence student performance in math, science, and technology at the building level (fig. 1).

Figure 1. Establishing a Data-Driven School Culture Checklist

Figure 1. Establishing a Data-Driven School Culture Checklist

Upcoming district decisions.

Questions raised by teachers, administrators, or the community.

Conduct inventory of data currently compiled in the district and determine format (electronic or paper).

Assess technological capacity of the district to manage and analyze data.

Determine the extent to which personnel in the district have time, skills, and willingness to engage in data-driven projects.

Identify indicators of input, process, and outcomes variables related to goals.

Determine which additional data are reasonable to collect.

Train staff to collect and use data.

Analyze and disaggregate date.

Establish benchmarks and measure progress toward goals over time.

Develop action or school improvement plans.

The Importance of Teachers

Teachers are essential members of a data-driven school culture. Much quantitative information in schools derives directly from their work with students. Teachers, in turn, use the analysis of data to improve their practice (fig. 2).

Figure 2. How Teachers Can Be Data Savvy

1. Identify questions related to student performance. You are likely to be most interested in information related to your classroom, but schoolwide, districtwide, or statewide patterns can also be informative.

2. Identify data and gather necessary information. Take into account demographic information, such as gender, race, eligibility for free or reduced lunch, and language spoken at home. Looking at student responses to individual test questions tells you things that you can't learn from a single test score.

3. Examine and use data. Look at student performance in focused areas to target particular groups for assistance. Examine data from previous years. Regardless of how convincing patterns look, do not jump to conclusions. Look beneath the surface and ask more questions.

How does performance for individuals and groups related to state standards?

Is there variation across content areas?

How does the performance compare with other like groups, such as among students, schools, districts, states and across the nation?

Are there existing initiatives in the school, district, or classroom that might help improve student performance? On the basis of what evidence?

What are the implications for your instructional practices or for your curriculum?

Do your findings suggest that you need more professional development?

How might other stakeholders benefit from this information?

A teacher doesn't need to be a statistician to be data-driven. Simple calculations, such as frequencies, averages, and percentages, provide enough information to answer many important questions. Advanced statistical procedures should be assigned to trained individuals.

An Urban Example

Unified Public Schools (which has 23,000 students, 45 percent of whom are eligible for free or reduced lunch) created a student information management system. The information system currently helps the district address two important issues: It provides quick and tailored responses to building and staff requests for student information; and it enables teachers to design classroom instruction that accounts for the needs of individual students on the basis of their reading, writing, and numeric proficiencies. For example, the district can monitor transience more efficiently and meet the needs of students who move among district schools within the year.

The cornerstone of the system is a student-specific, numerical ID. The ID tracks students through the school system. Each record contains student and parental background, coded as variables, as well as information about attendance, scheduling, grades, and disciplinary action. The new system all but replaces the cumbersome paper-based filing system previously maintained by the district.

At the classroom level, teachers have direct access to individual student data through student performance software. Previously, only district-level data specialists had access to student data and issued reports only when time permitted. The software provides teachers with a comprehensive quantitative profile of each student based on cumulative grade-by-grade records. A Unified social studies teacher who is facilitating this approach with colleagues affirms that Teachers can research areas of difficulty for various students. At weekly meetings, these teachers can target classwide areas of weakness and adjust their classroom instruction to address these problems. At the same time, teachers can address individual problem areas with those students through one-on-one instruction. We have found that making this connection between [software] in the lab and the regular in-class instruction has been very powerful for our students. With the help of software consultants, the district ensures the informed use of student quantitative records by training teachers for the different assessments.

At the building level, the information system produces reports with real-time data. This is extremely important to administrators, who regularly deal with student movement among buildings. The district ensures the requisite building-level expertise by training at least one individual in each school. This approach is powerful because it puts information analysis directly in the hands of those most responsible for making and effecting decisions. This strategy also frees up time for more complex analyses by the two data specialists who are employed districtwide. The fact that Unified is the highest scoring urban district in the state may be due, in part, to its growing data-driven culture.

Future Successes

More than anything else, a data-driven school culture means that the district actively uses quantitative patterns to make decisions, especially those related to programs, curriculum and instruction, and resource allocation. Administrators, faculty, and other key stakeholders support this work by allocating resources to establish and maintain a data-collection system and by training personnel who are as close to the classroom as possible. Typically, data teams carry out special projects related to a larger data system and to districtwide goals. Any district can expect gains in student achievement over time when it becomes data-driven.

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School districts and teachers can use student data to help make decisions about school policy, curriculum, and instruction.

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O'Toole, T. (2020, March 2). What's the best approach to data analytics? Harvard Business Review. https://hbr.org/2020/03/whats-the-best-approach-to-data-analytics​

The article outlines 5 common approaches

1. "We’re here to help" – Data teams are hired without clear direction, resulting in low impact due to lack of alignment with business needs.

2. "Boil the ocean" – Overambitious plans to transform the entire company at once lead to diluted efforts and minimal results.

3. "Let a thousand flowers bloom" – Business units independently apply analytics, yielding mixed success and fragmentation.

4. "Three years and $10 million from now" – Long-term plans with unclear outcomes lose momentum due to slow, process-heavy implementation.

5. "Start with high-leverage business problems" – The most effective approach: identify specific, impactful problems and apply analytics to solve them quickly and visibly.

#5 Best approach; in the same way, we can identify high leverage business problems. apply analytics to solve problems.

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joe daniel price/Getty Images

In practicing data analytics for more than 30 years, and leading, advising, interviewing and teaching executives in many industries on data analytics for five years, I've observed that their approaches generally fall into one of five scenarios: two that typically fail, two that sometimes work partially, and one that has emerged as best. Let's take a look at each:

1. We're here to help - do you have any problems to solve?

This scenario often starts with the CEO (sometimes prompted by the board) deciding to hire a data scientist and establish a data analytics group. The data team sets out in the organization with great aspirations but without specific guidance to find business problems to solve. The data scientists, however, don't have a practical understanding of the business, and the business leaders don't know what, exactly, the data analysts are supposed to do or how to use them. As a senior executive of a very large enterprise said, "Our CEO hired a data scientist who reports to me, but I'm not sure what he does or what to do with him." As the business leaders and the data scientists try to figure out how to relate, not much business value is created.

2. Boil the ocean.

Well-intended enthusiasm for putting data science to use can lead to overly ambitious aspirations to impact the entire company at once. The reality, however, particularly in large companies, is there are too many legacy data systems, too many practical issues, and too few people on the data science team to produce a significant business lift across the whole company in short order. Business results typically fall well short of high expectations. As an executive of a multinational European manufacturing company observed, "We've been at this for three years, and spent millions of euros, but we don't have much to show for it." In the end, not much business value is actually realized.

3. Let a thousand flowers bloom.

The third scenario has promise: C-level leaders direct that data analytics should be adopted throughout the company. The practical use is left to the discretion of each business unit leader or function head. While data analytics gets grounded in and kept close to the business, much depends on if and how individual business heads choose to use it. Some embrace it and achieve significant results; others aren't sure what to do or else avoid it. "Data analytics" often becomes just enhanced business reporting. Databases, systems and tools proliferate. With fragmented efforts, it is difficult to scale the resultant activities and determine how much business value is being created.

4. Three years and $10 million from now, it's going to be great.

This rational approach is undertaken with all the right intentions - that data analytics can create business value, but require commitment, investment, and time. The problem is this approach typically results more in process than business outcomes. Often it involves a series of workshops, committees, and meetings that drag on without much to show for it. Multiyear investments are difficult to sustain without any business results in the face of competing budget demands and changing business conditions. A large industrial company, for example, has been planning, developing, and discussing their data analytics initiatives for years, but executives wonder where the effort is headed and when it will show business value. Despite a promising start, too much time passes without business results, and support wanes.

5. Start with high-leverage business problems.

Finally, the approach that works best: Identify a small number of "high-leverage" business problems that are tightly defined, promptly addressable, and will produce evident business value, and then focus on those to show business results. The specific business problem drives the team to identify the data needed and analytics to be used. Quick wins demonstrate business value. For example, a company that operates medical imaging clinics saw a high-leverage problem in patient "no shows." The company set out to predict and reduce no-shows for the benefit of all involved: patients, doctors and technicians. Reducing "no shows" directly improves the bottom line. There's no substitute for business results to build credibility for data analytics and sustain commitment.

Best Practices for Data Analytics

As we look across these scenarios, best practices become clear, including:

Data science can't happen in a silo. It must be tightly integrated into the business organization, operations and processes.

There needs to be joint prioritization. Business leaders and data scientists should jointly decide which business problems to focus on. If there is any question about priority, the final call should go the business heads.

Leaders need to be conversant in data science. Business leaders don't need in-depth expertise in data science, but they require a basic, working understanding. Being conversant enables business leaders to work effectively with their data science teams.

You may need to accept "inconvenient outcomes." Data inevitably creates transparency and reveals business insights that can be unexpected, uncomfortable, and unwelcome. Data analytics will unearth inefficiencies and misconceptions that complicate leadership and disrupt conventional thinking. Business leaders who crush or ignore answers they don't like will rapidly undercut the value of data analytics.

By observing the different approaches taken by a wide range of companies, we can see what works and what doesn't to connect data analytics to creating real business value. Because if your data analytics isn't adding real value to the business, it's not going to be successful or sustainable.

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What's the Best Approach to Data Analytics?

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In practicing data analytics for more than 30 years, and leading, advising, interviewing and teaching executives in many industries on data analytics for five years, I've observed that their approaches generally fall into one of five scenarios: two that typically fail, two that sometimes work partially, and one that has emerged as best.

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Initiate Lasting Change: How to Build a Data-Driven Culture in Schools

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In order to build a data-driven culture in schools, it’s crucial to integrate data into everyday decision-making as well as provide professional development. Here are best practices to ensure you succeed.

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Read articles.

share readings.

think about vision.

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When I thot about DRC - I wish...

Reduce - errors, wasted time

Reuse - 1 idea, 1 data many uses

Recycle - familiar and similar friendly process to teachers

Do something that really makes life better for our colleagues in school :-)

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To read and share on what is the purpose of data driven, and its relevancy to our school as a whole

What data do we have?

What do you mean by data driven culture ?

Would it be better if we can identify areas to use data as feedback?

How do we ensure that we are not data-fatigue? ( is there such word? :()

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1) What constitutes a data driven school?

2) How will this data driven culture be useful for the school community?

3) What are the current data that the school already has? Are they being used effectively?

4) With these data on hand, how can we make improvement to the school?

5) How will these data impact school decisions and subsequently school practices?

6) Maybe we should brainstorm, based on our own department, what data will be useful for us to improve on certain structures and analysis?

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1. How does a good data driven culture look like?

2. What are some KPI for this?

3. Any institutions which are good in this area that we can learn from?

4. What maybe some courses relevant.

5. Does it mean that with data - driven culture, we need sub-task forces to collate data?

6. Do we also need to find out what data that the school needs.

7. What skills must have to contribute effectively in this task force.

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How does NYPS look like when we say we have achieved DRC?