Why Is Data Important For School Leadership?


Why Is Data Important For School Leadership
Multiple Measures of Data – One of the most important jobs of a bold instructional leader is determining what are the most important things to garner their attention. This can be difficult with all the information and questions in front of us each day.

  1. In addition, some things, like students’ well-being, may be difficult to assess.
  2. However, Everything You Do in Schools is Quantifiable – Really by Victoria Bernhardt shares the importance of developing a culture of inquiry that includes measures other than just test scores when trying to access the results.

By reviewing data in areas such as demographics, student learning, perceptions, and processes, a leader can get a clearer picture of what is happening at the school ensure that the needs of ALL students are being met. It has become popular for administrators to have a “data dashboard” and it is a good idea to find a way to highlight what is most important to you. Why Is Data Important For School Leadership “Exceptional leadership requires us to choose the right things to focus on and then devote our ongoing efforts to them with ‘simplicity and diligence’ (Mike Schmoker, Leading With Focus; p.25).”
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What are the benefits of data-driven decision-making in education?

Data-driven decision-making can also help schools identify areas where instructional practices may need to be adjusted to better meet the needs of students. By analyzing data on student performance, educators can identify areas where students are struggling and adjust instruction to meet their needs.
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What is the importance of leadership in the school system?

1. School leaders establish great teaching practices – School leaders can support educators and their pupils by establishing effective teaching practices, They can harness the talents and motivations of teachers, students, and parents; develop inclusive and inspiring learning cultures for the whole school ; and provide intensive, individualised, and sustainable teacher training.

  1. The impact of strong school leadership on education is clear; a recent study spanning 65 countries found that students led by the top 25 per cent of school leaders receive the equivalent of three extra months of learning every year compared to those led by the bottom 25 per cent.
  2. There is considerable interest in targeting school leaders amid efforts to improve outcomes for students in a cost-effective manner, with one study finding that a one-point increase in scoring on school management practices is associated with a 10 per cent increase in student performance.

In order for leaders to develop the best teaching practices for their schools, it is essential that they are not burdened by unnecessary administrative responsibilities (they commonly spend less than 25 per cent of their time managing student learning activities), and that they receive appropriate training.

  1. Where training for school leaders is limited, there are a number of free resources available online.
  2. For example, the UNESCO Institute for International Capacity Building in Africa ‘s training manuals for school leaders – which were deployed in Guinea, Lesotho, Nigeria, and Sierra Leone – can help plug these gaps in training.

We have also published this Toolkit to help school leaders support and protect teachers and education support staff in the return to school following COVID-19.
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Why is data literacy important for teachers and students?

Fostering data literacy includes fostering mathematics, statistics, quantitative, media, and discipline specific literacy to equip students with the knowledge and resources needed to make sound, informed decisions and to solve problems arising in their personal and professional lives as members of society.
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What are the advantages of data-driven strategy?

2. Clarity on business opportunities. – Data-driven strategy streamlines the entire process of market research. It makes your efforts more informed and more powerful. You can leverage data to detect emerging threats and changes in the industry. That allows you to adapt faster.
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Why is data important?

Data allows organizations to more effectively determine the cause of problems. Data allows organizations to visualize relationships between what is happening in different locations, departments, and systems.
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How do teachers use data to improve learning?

Formative assessments provide immediate feedback on lesson plans – A final, and perhaps most important, data set for teachers is collected through formative assessments. These are informal, low-stakes assessments using a thumbs up or thumbs down, the stoplight method, exit slips, or brief quizzes to measure learning immediately after lectures or classroom activities.

Information gleaned from this process allows for quick modification to the next class’s plan and identifies learning gaps long before they show up in a summative assessment or become an issue in standardized testing. By itself, data cannot solve America’s education problems; however, the collection of data at the standardized, formal, and informal assessment levels gives teachers a way to understand student needs, group students based on strengths and weaknesses, and design (and adjust) lesson plans to ensure that students continuously improve.

Monica Fuglei is a graduate of the University of Nebraska in Omaha and a current adjunct faculty member of Arapahoe Community College in Colorado, where she teaches composition and creative writing.


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Are we motivating students with data?

Are We Motivating Students with Data? November 1, 2015 Vol.73 No.3

Motivating or Demotivating? A Study of Teacher Practices Data-Use Practices Supporting a Performance Orientation Data-Use Practices Supporting a Mastery Orientation Questions to Guide Practice

From “data chats” to “Data Fridays,” teachers are involving their students in looking at data. Does your approach increase student motivation—or decrease it?

Motivating or Demotivating? A Study of Teacher Practices Data-Use Practices Supporting a Performance Orientation Data-Use Practices Supporting a Mastery Orientation Questions to Guide Practice

On one “Data Friday,” Ms. Mendoza tries to encourage healthy competition by displaying her 7th period’s performance on the most recent interim assessment compared with that of her other classes. Down the hall, Mr. Williams passes out individual results on the assessment and asks students to take out their data binders to graph their own progress, reflect on their data, and determine action steps.

Like Ms. Mendoza and Mr. Williams, practitioners and policymakers around the country have expressed considerable enthusiasm for engaging students with data. Some see it as a way to encourage students to exert extra effort; others believe that students who look at their own data gain a better understanding of their strengths, their weaknesses, and how to improve.

How do teachers commonly examine data with their students? And what does research tell us about how these practices are likely to affect student motivation? Motivation research identifies classroom practices and activities that shape students’ orientation toward goals (Dweck, 2010; Pintrich, 2003).

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A performance orientation directs students’ attention to grades and achievement and encourages them to compare themselves with others (Ames, 1992; Pope, 2010). Performance-oriented goals are generally associated with negative student outcomes (Meece, Anderman, & Anderman, 2006). Although some students may be motivated by a performance orientation, others may balk at difficult tasks and give up when faced with difficulty (Pintrich, 2003).

Teachers promote a performance orientation when they make most decisions for students, reward achievement relative to others, use rewards to control behavior, provide boring or repetitive tasks, and divert attention from tasks and learning to achievement (Ames, 1992; Epstein, 1988).

  • In contrast, a mastery orientation, in which students focus on developing new skills and improving their competence, is associated with self-regulation, increased effort, autonomy, and the belief that effort will lead to academic success (Ames, 1992; Pintrich, 2003; Seifert, 2004).
  • Teachers foster a mastery orientation when they focus on individual improvement, recognize and reward effort, evaluate students privately, involve students in decision making, foster students’ sense of responsibility and independence, provide meaningful and interesting learning activities, and encourage students to set short-term, self-referenced goals (Ames, 1992; Epstein, 1988).

To understand how teachers use data with their students, we conducted in-depth fieldwork in six middle schools in four districts in 2011–12, including interviews, focus groups, and observations with teachers, coaches, and school and district administrators (Marsh, Farrell, & Bertrand, 2014).

  1. The teachers in our study, like most educators in the United States, faced increasing demands from school and district administrators to engage in data-driven practices.
  2. Their schools had data walls and regularly scheduled data chats focused on standardized test results.
  3. They received new technology to obtain quick results from assessments, generate color-coded displays, and assist with analyses.

Teachers also had data coaches, instructional coaches, and professional learning communities to support this work. It was only a matter of time before the demand for data use extended to students. We met many teachers who believed that having students analyze data would motivate them to learn, and we identified 50 instances where teachers engaged students with their data.

  • Overall, we found that many teachers set up performance-oriented classrooms that may actually have been de motivating for students. Mrs.
  • Landen, a 7th grade language arts teacher, frequently posted graphs of test results on her data wall following each common grade assessment or district benchmark assessment.

These bar graphs compared the results across her classes, which were homogeneously leveled by students’ performance on the prior year’s state test. Mrs. Landen felt that students were motivated by competition: Yes, it’s to motivate, to encourage my “basic” kids that they’re “this close” to honors, and then it’s to motivate my honors kids.

I even lie to my honors kids and tell them, “You know, on this test our neighbor teacher’s honors class scored a certain percentage.” The graphs themselves emphasized not only the state’s performance levels (with an emphasis on a proficiency goal), but also the direct comparison to others. Students were not involved in analyzing their data, and there was very little guidance on how they could improve or reengage with material to fill in gaps in their knowledge.

Mrs. Landen’s data use typified the performance orientation we saw in one-third of all instances in our study. These teachers

Believed that if students saw their data, they would work harder and take assessments more seriously. Presented data as status-based information. For instance, one teacher posted the names of all students scoring proficient or advanced on district assessments on the classroom wall and had those students sign the list. Publicly shared group-level data or even individual results in the belief that social comparison motivated students. Used extrinsic rewards like prizes and parties when students had moved to a certain proficiency status to ensure student investment in assessment results. Provided limited opportunity for student involvement, instead showing prepared data displays and telling students how to interpret the information. Teachers provided little guidance about what students should study or revisit.

Ms. Santos, a veteran 8th grade language arts teacher, began the school year worried about the low performance of one of her classes. She worked closely with the school’s literacy coach to design routines to engage students in analyzing assessment data and setting goals.

  1. She provided students with copies of their multiple-choice answer sheets, and together they corrected the results.
  2. Students then privately analyzed how well they had mastered each standard, and they chose how they could close their own gaps in learning.
  3. In one instance, Ms.
  4. Santos designed state standardized test “clinics” where student choice was central: I have five different groups by strand, and I have a different activity for them to do.

They chose which strand was their weakest and which one they wanted to work on for six weeks. They bought into that since they were able to see their own strengths and weaknesses. In small-group settings, Ms. Santos provided students with specific feedback based on each student’s needs.

Embraced a learning perspective —a belief that examining and reflecting on the data would help students identify weaknesses, what contributes to them, and how they could address gaps. Focused on growth-related information, articulating a clear relationship between effort and outcomes and encouraging students to consider their progress. Shared individual-level data privately with students in ways that focused student attention on how they were performing relative to their own past performance or how close they were to reaching standards. Sometimes used intangible rewards like praise and discussion of positive results to emphasize key messages about progress. Involved students in analysis, goal setting, and follow-up. For instance, students had opportunities to graph their own results and identify topics on which to focus their reflection. Were highly involved in supporting students’ next steps. In whole-group or individual interventions, teachers did not simply repeat the same content and approach but instead tried multiple ways to reteach the material.

Finally, one-third of the instances of data-use practices we observed were mixed—that is, a hybrid of mastery and performance practices. For instance, in one school, classrooms were equipped with a “scan-cam,” a tool students used to immediately learn their individual scores on multiple-choice questions.

  1. A 7th grade teacher, Mr.
  2. Wilson, implemented this tool in such a way that students were actively involved in analyzing and identifying gaps.
  3. However, identifying a response to the results was teacher-driven, and it rested largely on test-taking skills rather than on helping students reengage with the content.

The environments in which teachers worked—characterized by school-level polices, routines, type of leadership, district-level expectations, and the broader accountability context—greatly shaped how they used data with students. In some cases, these factors pressed teachers to focus on performance; in others, they created opportunities for teachers to focus on mastery.

  1. First, school-level policies and routines like data talks and data walls appeared to define acceptable student data-use practice for teachers.
  2. In one school with a high proportion of teachers who had a performance-oriented approach, a schoolwide policy required teachers to post comparative class data with a focus on proficiency goals to encourage competition between teachers and classes.

Classroom structures then tended to replicate the components of the data-wall policy, such as attention to status and class comparisons. Messages from school leaders—administrators, coaches, and teacher leaders—also framed the discourse around data use.

In one school with the highest proportion of mastery-oriented observations, the assistant principal communicated mastery-focused messages to teachers: I’ve tried to be really consistent in my message about how important goal setting is for kids, to teach them how to set goals, how to monitor their goals, how to assess their goal achievement and then recalibrate their goals based on how they’ve accessed them.

Several teachers at this school mentioned how the assistant principal’s vision helped them think about their own practices. District policies and norms also shaped data-use practices. The literacy curriculum one district adopted fostered mastery-oriented practices with learning-focused, formative assessments, such as readers’ notebooks and conference logs.

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In contrast, the culture and norms in another district promoted competition among schools. During monthly meetings with principals, district administrators regularly reported and compared schools’ scores on the district benchmark assessments. Finally, the broader national and state accountability environment in the United States promotes a performance-based orientation.

Federal accountability policy (embodied in No Child Left Behind) has long emphasized status measures of student achievement and assumed that public reporting of information on performance, coupled with consequences, will motivate individuals to work harder to improve performance.

It’s not surprising that many educators replicate the orientations promoted by the broader structures within which they operate. Educators are unlikely to have control over the broader state and federal accountability messages that shape data-use practices. But with an eye for what’s realistic and possible, we offer some questions that educators who want to engage students with data should ask themselves: For teachers: What is your purpose in engaging your students with data, and how do you structure your classroom practices to meet these goals? What elements of your data-use practice are inadvertently emphasizing performance? How could you reorganize or refine your data-use practices to reflect a mastery orientation? For school and district administrators: How are school or district policies and routines framing messages around data for your teachers that might translate to a mastery or performance orientation within classrooms? Do school or district policies and programs emphasize the values of performance, status, and extrinsic rewards; or do these policies recognize effort and growth? As we work toward the laudable goal of involving students in data use, we want to make sure that our data-use practices support and motivate students, rather than deflate or demotivate them.

Authors’ note: Teachers’ names are pseudonyms. Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84 (3), 261–271. Epstein, J.L. (1988). Effective schools or effective students: Dealing with diversity.

In R. Haskins and D. MacRae (Eds.), Policies for America’s public schools: Teachers, equity, and indicators (pp.89–126). Norwood, NJ: Ablex. Marsh, J.A., Farrell, C.C., & Bertrand, M. (2014). Trickle-down accountability: How middle school teachers engage students in data use. Educational Policy, 1–38. Meece, J.L., Anderman, E.M., & Anderman, L.H.

(2006). Classroom goal structure, student motivation, and academic achievement. Annual Review of Psychology, 57, 487–503. Pintrich, P.R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95 (4), 667–686. Doing Data Right : Are We Motivating Students with Data?
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What is the most important role of a student leader?

WHAT IS STUDENT LEADERSHIP? – Leadership is a process of social influence, which maximises the efforts of others, towards the achievement of a goal. It has nothing to do with seniority or one’s position in the hierarchy of a community or a group; this is why everyone has the ability to harness their leadership skills.

Everyone has influence. Each person can lead in their own way. However, many young people require guidance and space to exercise and hone their leadership skills. Learning a range of leadership skills and techniques from a young age can provide students with an excellent head start in life and allows them to develop confidence and improves their overall mental wellbeing.

Leadership is a multifaceted construct involving a range of interrelated skills, identified 12 student leadership skills:

Project planning Reflection Problem-solving Team building Decision making Goal setting Time management Project management resource allocation Effective communication networking Conflict resolution Diversity awareness Self-confidence

Self-confidence is necessary for leaders to take risks and accomplish their goals. Self-confidence can be described as an ability to be certain about one’s competencies and skills. It includes a sense of self-esteem and self-assurance and the belief that one can make a difference.

Leaders who are self-confident tend to deal immediately and directly with problems and conflicts, rather than procrastinating, ignoring, or passing problems to others. Leadership involves influencing others and self-confidence allows the leader to feel assured their attempts to influence are appropriate and right.

Self-esteem expert Jack Canfield notes 80% of children entering the first grade scored high on the self-confidence inventory, By the fifth grade, only 20% of the children were scoring high, and by the time they graduated from high school, that number was down to just 5%. Why Is Data Important For School Leadership
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How can leadership have a positive impact on the school?

Promoting collaboration among staff members, with proper focus and leadership, creates a positive environment in which teachers can share best practices that are responsive to student needs. Thus, principals can positively influence their school culture through the use of strategies that encourage collaboration.
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What is the concept of leadership in school management?

Leadership is a process of influence leading to the achievement of desired purposes. It involves inspiring and supporting others towards the achievement of a vision for the school which is based on clear personal and professional values.
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What is the importance of data and assessment in education?

Classroom teachers – Classroom teachers manage the teaching and learning process. They monitor students’ learning by informal means, such as quizzes and games, and formative tests. Teachers use the data to assess a student’s performance, strengths, weaknesses, and progress.
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Why is it important for students to understand data?

Published in SEDL Letter Volume XXII, Number 2, Linking Research and Practice A picture may be worth a thousand words, but in education, information speaks volumes. 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. Research has shown that using data in instructional decisions can lead to improved student performance (Wayman, 2005; Wayman, Cho, & Johnston, 2007; Wohlstetter, Datnow, & Park, 2008).

No single assessment can tell educators all they need to know to make well-informed instructional decisions, so researchers stress the use of multiple data sources. Generally, schools collect enormous amounts of data on students’ attendance, behavior, and performance, as well as administrative data and perceptual data from surveys and focus groups.

But when it comes to improving instruction and learning, it’s not the quantity of the data that counts, but how the information is used (Hamilton et al., 2009). SEDL’s Southeast and Texas Comprehensive Centers offer technical assistance and professional development throughout their respective regions to help educators use data effectively.
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How does data improve decision making?

Data-driven decision making is important because it helps you make decisions based on facts instead of biases. If you’re in a leadership position, making objective decisions is the best way to remain fair and balanced.
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How can data and statistics help us in decision making?

1500.0 – A guide for using statistics for evidence based policy, 2010 HOW GOOD STATISTICS CAN ENHANCE THE DECISION MAKING PROCESS Statistics are a vital source of evidence as they provide us with clear, objective, numerical data on important aspects of Australian life including the growth and characteristics of our population, economic performance, levels of health and wellbeing and the condition of our surrounding environment.

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The Australian Bureau of Statistics (ABS) plays an important role in this process by providing data ‘to assist and encourage informed decision making, research and discussion within governments and the community, by leading a high quality, objective and responsive national statistical service’ (ABS Mission Statement).

When we are able to understand and interpret this data correctly, our ability to identify key areas which require change are enhanced, and our proposals for change are likely to respond to the ‘real’ needs of the Australian community. Statistics can also aid the decision making process by enabling us to establish numerical benchmarks and monitor and evaluate the progress of our policy or program.

This is essential in ensuring that policies are meeting initial aims and identifying any areas which require improvement. Statistics can be used to inform decision making throughout the different stages of the policy-making process. The following framework has been adapted from different approaches to the policy making cycle, outlined in Disability Services, Queensland, 2008; Edwards, 2004; Othman, 2005.

The framework highlights the importance of using statistical information at each of the stages of the policy cycle.

STAGE 1 Identify and understand the issue
The first phase involves identifying and understanding the issue at hand. Statistics can assist policy makers to identify existing economic, social or environmental issues that need addressing. For example, statistical analysis could identify issues concerning the aging of the population or the implications of rising inflation. They are also vital for developing a better understanding of the issue by analysing trends over time, or patterns in the data.


STAGE 2 Set the agenda Statistics provide a valuable source of evidence to support the initiation of new policy or the alteration of an existing policy or program. Once an issue has been identified, it is then necessary to analyse the extent of the issue, and determine what urgency there is for the issue to be addressed. Statistics can highlight the relevance and severity of the issue in numerical terms, and thus demonstrate the importance of developing policy or programs to address the issue as quickly as possible.


STAGE 3 Formulate policy Once an issue has been identified and recognised as an important policy issue, it is then necessary to determine the best way to respond. This stage requires careful and rigorous statistical analysis and thorough consultation with key stakeholders to establish a clear understanding of the true extent of the problem. This will help to determine the most appropriate policy or program options to address the issue, and the best strategy for implementing these. During this stage, clearly defined aims and goals should be developed with quantifiable indicators for measuring success. Benchmarks should also be established to ensure that progress is measurable following the implementation of the policy/program.


STAGE 4 Monitor and evaluate policy The policy process does not end once the policy/program is up and running. It is essential that the progress of a policy/program is regularly monitored and evaluated to ensure it is effective. An evaluation of the success of the policy/ program in quantifiable terms can be measured against benchmarks which were established at an earlier stage to accurately measure progress. This enables an assessment to be made as to whether the policy is meeting initial aims and objectives, as well as providing insight and identification of areas that require improvement. The process should then be repeated, by beginning the cycle again.

1500.0 – A guide for using statistics for evidence based policy, 2010
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What are 3 benefits to having a data management strategy?

Visibility – Data management can increase the visibility of your organization’s data assets, making it easier for people to quickly and confidently find the right data for their analysis. Data visibility allows your company to be more organized and productive, allowing employees to find the data they need to better do their jobs.
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What assessment data do you use to improve student performance?

What Types of Data are Available to Teachers? – Assessment data is important but is only one piece necessary for effective data-driven decision making in education. It provides a snapshot of a student’s ability at that moment and does not provide an all-around picture of a child.

  1. To get the best understanding of how to support student growth, educators must review and analyze a variety of meaningful data.
  2. Student interest surveys can be completed early in the school year to give new teachers an idea of who their children are before getting too far into the curriculum.
  3. Teachers should take care not to etch students’ interests in stone, though, because we know what interests them today may be old news tomorrow! However, the information gained from the surveys are valuable nuggets that can be used to begin building relationships and when planning engaging lessons.

Attendance data, although often more of a focus for administration, can also give teachers information about a student. occur for many reasons, and it’s not always the reason for the absences that’s important. The specific content and how much of it students miss when they are absent can help explain why students are having difficulty mastering skills.

Keeping up with the days students are absent can help teachers plan individual teaching opportunities or assign students to the appropriate small groups. Discipline data, like attendance data, provides a much different view of a student. Paying attention to when and how often a reasons allows teachers to properly prepare instruction for the student’s return.

In addition, keeping track of the day of the week and time of day a student tends to misbehave (i.e., more often in the morning or always during math) helps teachers and administrator identify patterns and devise an intervention plan to address the antecedents that lead to the behaviors.

  • Allow teachers to collect data about student learning and make decisions about instruction.
  • The goal of formative assessment is to provide the teacher with ongoing information about student comprehension of the content being taught before they have finished covering the content.
  • This allows them to monitor learning needs and progress.

These types of data give teachers instant knowledge of what a student knows and doesn’t know, and provides the opportunity to make immediate corrections to a student’s understanding. Formative assessments can be formal like completed concept maps and quizzes, or informal like classroom discussions and student conferences.

Some of the best formative assessment data can be collected by checking for understanding through purposeful questioning, which can be used to create effective data-driven instruction. take place after content has been taught and give data on student mastery of content. State assessments, unit tests, and final projects are examples of summative assessments.

While important, summative assessment results are often received too late to inform instruction, and, in isolation, do not provide much valuable information about a student. Without a pre-formulated plan for re-teaching content based on formative assessment data, the data yielded from summative assessments is less valuable than other data.
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What types of data are used to assess student achievement?

Achievement data provide information on student learning and achievement. These data include standardized test scores, classroom-based assessments, rubric-scored writing portfolios, and class grades.
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