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Transforming classrooms with learning analytics

Learning analytics is revolutionizing modern education by transforming data into actionable insights. It's the key to understanding how students learn, enabling educators to tailor educational experiences to individual needs and enhance overall student performance. As higher education becomes increasingly data-driven, the role of learning analytics has never been more critical.

In this blog, we will explore the core aspects of learning analytics, its profound impact on students' success, the role of educators in this data-centric approach, and the ethical considerations that come with it. Plus, for professionals looking to lead the charge in data-driven education, we’ll introduce how an M.S. in Learning Sciences can empower their career.

What is learning analytics?

In today's technology-centered world, information is constantly being gathered about the actions of individuals online and off, leading us into the age of Big Data. All this information is collected with such fervor because data has tremendous value. However, data is worthless without the skills to properly analyze it and put it to use. Data analytics converts raw information into actionable insights using a range of tools, technologies, and processes. 1 Using analytics research you can identify patterns, find trends and solve problems to improve decision-making, shape businesses, and, in the case of learning analytics: optimize education.

Learning analytics uses educational data mining to analyze students’ learning processes and extract valuable information that helps inform teaching strategies and improve student achievement. 2 Just as businesses use data analytics to learn more about human behavior and customer preferences, so too do higher education institutions and other educational organizations use learning analytics to uncover data about learners that the students themselves can't or don't articulate. This educational research can be used to alter the design of a learning management system, support students with additional learning tools, help an instructor better connect learning objectives to lessons, and more.

Benefits of learning analytics for student success

Learning analytics helps to bring a sense of human-centered design to the field of education and educational technology , offering a suite of benefits that can significantly monitor student progress, personalize learning, and optimize educational and instructional experiences.

Tracking student progress

Learning analytics tools can be used to track student progress via data like the amount of time the student spends watching lessons, using study tools, or participating in course discussion forums and assignment grades or exam performance. These data sets can be fed into dashboards, weekly emailed reports or the like, to help students pay attention to their own learning habits and warn them when they may be at risk for falling behind or getting a worse grade. 3

Personalized learning experiences

Given all the time and resources in the world, plenty of educators would create individual learning plans for each of their students if it meant helping them succeed. When it comes to most learning experiences, especially in higher education, that's not a practical dream. However, learning analytics and learning management systems have evolved to offer varying levels of personalization for learners so that different questions are posed, more or less frequent reviews are deployed, and certain lessons are recommended to be revisited when necessary. 3

Improving teaching practices

While learning analytics can't guarantee student success, they can help instructors intervene at key moments to avoid poor performance. Predictive analytics can help call attention to a student who might be at risk of a failed exam, poor grades, or even dropping out. One study created a model that combined demographics, academic records from historical data, prior scores and learning management system usage to predict whether students were likely to earn a grade of C or higher in a course. 3 With this data collected, educational institutions can step in before it's too late, to help a student make a plan for improvement.

Additionally, data analytics can help improve teaching methods for any instructors who know how to properly employ learning analytics research. As Alyssa Friend Wise notes in her 2023 report on learning analytics: 3

"Instructors can document their pedagogical intentions, describe activity patterns that indicate fulfillment of these intentions and then use learning analytics to evaluate the degree to which the patterns occurred."

Data collection and analysis process for learning analytics research

The backbone of learning analytics lies in its systematic approach to data collection, often called 'educational data mining' and analysis. There are a variety of methodologies used to gather and interpret educational data that enable a richer understanding and enhancement of the learning process.

Learning analytics data types

Researchers and educators today can collect large volumes of data with the proliferation of online learning and learning management systems. Through these platforms granular data can be collected to inform all aspects of instruction and lesson planning. Along with typical big data collected like demographics, learning analytics specific data might include: 3

• Amount of time spent on lessons/in a learning management system
• Student performance/grades
• Surveys and self-reflections
• Quiz/exam answers and answers series
• Discussion board posts and engagement
• Use of play/fast-forward/rewind controls on videos
• Student gaze/gestures/posture

Data analysis methods

After collecting data, educators or researchers can apply typical data science methods to their information points in order to perform their educational research.

Predictive modeling or predictive analytics are likely the implementation of learning analytics that first comes to mind for most people. Using historical data and machine learning to forecast student performance, predictive analytics provide a proactive approach to instruction helping to identify at-risk students early, informing interventions to support and improve educational outcomes, and tailoring the learning path to individual needs. 3

Structure discovery involves uncovering patterns within complex educational data without predefined models or hypotheses. This approach typically leverages unsupervised machine learning techniques such as clustering or principal component analysis to reveal inherent groupings, relationships, and structures within the dataset. By identifying these intrinsic patterns among variables such as student engagement, resource usage, or assessment results, educators can better understand the learning environment and devise strategies to enhance instructional design and student support systems. 3

Natural text mining or natural language processing can be used to analyze data like discussion posts from learning management systems for professors to quickly get summaries of topics covered or common questions. This might help an instructor determine what topics they need to dedicate more time to in their lesson plan. 3

A temporal approach to analysis intends to uncover previously undefined patterns in data as relating to the sequence and flow of events over time. This approach could help an educator pinpoint the correct amount of time to allow for lesson completion or help them in analyzing data to inform the order of lessons based on which sequence better aides in student comprehension. 3

Ethical Considerations for learning analytics in the classroom

Because learning analytics deal with sensitive student data, the same ethics concerns that accompany the use of any Big Data come into play here as well. Researchers and educators must prioritize protecting individual privacy, ensuring that all student data is collected and analyzed with informed consent and under strict governance protocols. Additionally, transparency is key and instructors should be clear with their students about their specific intentions with learning analytics including: what data will be gathered, how it's used and for what purposes. Maintaining strong data security measures and anonymizing datasets can help prevent security breaches and misuse. 4

Become a data-driven educator with an online M.S. in the Learning Sciences

Learning analytics is more than just a set of tools; it's a pathway to unlocking every student's potential and streamlining educational efficiency. If you're ready to harness data science for educational benefit and lead at the forefront of this dynamic field, the online Master of Science in the Learning Sciences offers a concentrated Learning Analytics specialization to gain the knowledge and skills necessary. Learn to navigate and innovate in a data-driven educational environment with courses like Introduction to Learning Analytics, Data Modeling and the Learning Sciences, Data, Education and Society, and Advanced Methods in Learning Analytics.

By choosing to advance your expertise at SMU, you have the opportunity to not only develop your own career but to improve the learning experiences of countless students. Explore the online program and join a community committed to excellence in education. For admissions or program inquiries, don't hesitate to schedule a call with an admissions outreach advisor and take the first step towards becoming a catalyst for meaningful change in education.

• Retrieved on August 5, 2024, from aws.amazon.com/what-is/data-analytics/
• Retrieved on August 5, 2024, from ieeexplore.ieee.org/document/10295479
• Retrieved on August 5, 2024, from researchgate.net/publication/328839735_Learning_Analytics_Using_Data-Informed_Decision-Making_to_Improve_Teaching_and_Learning_Maximizing_Student_Engagement_Motivation_and_Learning
• Retrieved on August 6, 2024, from scirp.org/journal/paperinformation?paperid=120025

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What is Situational Analysis in Education: What You Should Know

Table of Contents

Situational analysis in education is a critical process that plays a vital role in shaping learning strategies and aiding educational advancements. It involves conducting needs analysis and situation analysis, which are essential in designing a successful curriculum plan.

Needs analysis is the initial step in situational analysis, where information is gathered about students’ abilities, needs, and purposes for learning. It helps categorize these needs into necessities, lacks, and wants, providing valuable insights for instructional design.

Situation analysis, on the other hand, goes beyond students’ needs and focuses on identifying key factors that may influence the implementation of the curriculum plan. These factors can be social, economic, political, educational, or institutional in nature. Examining classroom factors such as class size, teacher availability, time, and learner motivation is equally important in this process.

By conducting a thorough situational analysis, educators can ensure that the course is suitable, practical, and realistic. It enables them to tailor learning experiences based on the unique characteristics and challenges present in the educational context.

Key Takeaways:

• Situational analysis in education involves needs analysis and situation analysis.
• Needs analysis focuses on gathering information about students’ abilities, needs, and purposes for learning.
• Situation analysis identifies key factors that may impact the implementation of the curriculum plan.
• Factors considered in situation analysis include social, economic, political, educational, and institutional aspects.
• Classroom factors such as class size, teacher availability, time, and learner motivation are also taken into account.

For more information on situational analysis in education and other educational strategies, visit Exquisitive Education .

The Importance of Situational Analysis in Education

Situational analysis is crucial in education as it provides valuable insights into the current educational environment and helps identify areas for improvement. By conducting a thorough analysis, educators can gain a comprehensive understanding of the factors that affect the implementation of the curriculum plan. This includes social, economic, political, educational, and institutional factors, as well as classroom-specific variables such as class size, teacher availability, time constraints, and learner motivation. Taking all these factors into account is essential to ensure that the course design is suitable, practical, and realistic.

One of the major benefits of situational analysis in education is its ability to inform the decision-making process. By gathering and analyzing relevant data, educators can make informed choices about curriculum design, resource allocation, teaching strategies, and assessment methods. This helps to create a learning environment that meets the needs of students and promotes their overall success.

Moreover, situational analysis in education is not only limited to the planning phase but also extends to the ongoing evaluation of educational programs. By regularly assessing the current state of education, educators can identify any gaps or areas that require improvement and implement necessary changes. This continuous analysis ensures that the educational system remains adaptive and responsive to the evolving needs of students.

Uses of Situational Analysis in Education

Situational analysis in education has versatile uses that extend beyond the planning and evaluation of educational programs. It can also aid in the identification of best practices, the development of targeted interventions, and the understanding of student outcomes. By analyzing the existing educational landscape, educators can identify successful models and strategies that can be replicated to improve learning outcomes. Additionally, situational analysis helps identify challenges and barriers that students may face, allowing for the development of targeted interventions to address these issues effectively.

In conclusion, situational analysis plays a vital role in education by providing valuable insights into the current educational environment and helping educators identify areas for improvement. By conducting a thorough analysis, educators can make informed decisions, create practical learning experiences, and ensure that education remains relevant and adaptive to the needs of students.

Steps of Situational Analysis in Education

Conducting a situational analysis in education involves specific steps and techniques to ensure accurate data collection and analysis. This process allows educators to gain a comprehensive understanding of the current educational landscape and make informed decisions for course design.

The first step is to identify the objectives of the analysis and establish the scope of the study. This includes defining the target audience, such as students, teachers, or administrators, and determining the specific aspects of education to be examined.

Next, data collection methods are selected and implemented. These can include surveys, interviews, observations, and document analysis. By gathering data from multiple sources, educators can obtain a holistic view of the educational environment and identify any gaps or areas for improvement.

Once the data has been collected, it must be analyzed and interpreted. This involves organizing the information, identifying patterns and trends, and drawing meaningful insights. Data visualization techniques, such as tables and charts, can be used to present the findings in a clear and concise manner.

In conclusion, conducting a situational analysis in education is a crucial step in course design. By following the steps outlined above, educators can gather relevant data, analyze it effectively, and make informed decisions to create practical and realistic learning experiences. To learn more about situational analysis in education, visit Exquisitive Education .

Factors Involved in Situational Analysis

Situational analysis in education involves addressing challenges and utilizing appropriate tools to effectively understand the educational landscape. It is essential to examine various factors that can impact the implementation of the curriculum plan. These factors can be classified as social, economic, political, educational, or institutional in nature. By thoroughly analyzing these elements, educators can ensure that the course design is tailored to meet the specific needs and requirements of the learners.

One of the main challenges in situational analysis is identifying and overcoming obstacles that may hinder the success of the educational program. These challenges could include limited resources, lack of funding, or time constraints. By acknowledging and addressing these challenges, educators can develop strategies to mitigate their impact and create a more conducive learning environment.

Furthermore, situational analysis in education involves the use of various tools to gather and analyze relevant information. These tools can include surveys, interviews, observations, and data analysis techniques. By employing such tools, educators can gain valuable insights into the current state of education and make informed decisions about the course design.

In conclusion, situational analysis plays a crucial role in education by considering the challenges involved and utilizing the appropriate tools to understand the educational landscape. By conducting a thorough analysis, educators can ensure that the course design is practical, suitable, and realistic. It empowers them to address challenges, overcome obstacles, and create effective learning experiences for students.

Examples of Situational Analysis in Education

To illustrate the effectiveness of situational analysis in education, here are some examples showcasing how it has been implemented in various educational settings.

Example 1: In a primary school located in a low-income community, a situational analysis was conducted to identify the specific needs and challenges faced by the students. Through surveys and interviews with the students, teachers, and parents, it was revealed that many students lacked access to basic learning resources and struggled with language barriers. Based on these findings, the school implemented targeted interventions such as providing additional reading materials and offering language support programs. As a result, the academic performance of the students improved, and their overall engagement in learning increased.

Example 2: A university planning to launch a new online course conducted a situational analysis to understand the existing online learning landscape. The analysis involved reviewing competitor programs, analyzing market trends, and collecting feedback from potential students. The findings revealed a demand for flexible, self-paced learning options and a need for more comprehensive course materials. With this information, the university was able to design a course that aligned with the needs and preferences of the target audience, leading to high enrollment rates and positive student feedback.

Example 3: Contextual Keyword Term

Situational analysis plays a crucial role in ensuring that educational institutions and courses are tailored to the specific needs and contexts of learners. By understanding the challenges, opportunities, and unique circumstances of each educational setting, educators can design effective learning experiences that meet the expectations of students and deliver measurable outcomes.

For more information on situational analysis in education and its practical applications, visit Exquisitive Education .

Understanding Different Analysis Methods

Situational analysis employs a range of analysis methods such as needs assessment, SWOT analysis, and benchmarking to gather comprehensive data. These methods are crucial in understanding the current state of education and identifying areas for improvement.

Needs assessment: This method involves gathering information about students’ abilities, needs, and purposes for learning. By categorizing needs into necessities, lacks, and wants, educators can tailor their teaching strategies to meet students’ individual requirements.

SWOT analysis: This analysis examines the strengths, weaknesses, opportunities, and threats within the educational landscape. By identifying these factors, educators can make informed decisions about curriculum development, resource allocation, and strategic planning.

Benchmarking: This method involves comparing educational performance against predetermined standards or other institutions. By benchmarking against high-performing schools or districts, educators can gain valuable insights and set realistic goals for improvement.

Examples of Situational Analysis Methods

Other analysis methods used in situational analysis include context analysis, environmental scan, readiness assessment, gap analysis, problem analysis, root cause analysis, current state assessment, landscape review, background research, analytics, and diagnostics. These methods provide different perspectives and data points that contribute to a holistic understanding of the educational context.

By utilizing these analysis methods, educators can gain deep insights into the educational context, enabling them to develop targeted strategies and interventions. Understanding the current state of education is essential for creating meaningful and impactful learning experiences for students.

The Role of Situational Analysis in Course Design

Situational analysis plays a pivotal role in course design, ensuring that educational programs are tailored to meet the needs and preferences of the learners. By conducting a thorough situational analysis, educators can gather valuable information about their students, the learning environment, and the broader context in which the course will be delivered. This analysis allows for a comprehensive understanding of the factors that may influence the successful implementation of the curriculum plan.

Gathering Data through Needs Analysis

One key aspect of situational analysis is needs analysis, which involves gathering information about students’ abilities, needs, and purposes for learning. By categorizing needs into necessities, lacks, and wants, educators can prioritize the content and activities that are most relevant and meaningful for the learners. This data-driven approach ensures that the course design aligns with the specific requirements and goals of the target audience.

Identifying Key Factors through Situation Analysis

In addition to needs analysis, situation analysis helps identify key factors that may positively or negatively impact the implementation of the curriculum plan. This analysis takes into account various dimensions such as social, economic, political, educational, and institutional factors. Classroom-specific factors like class size, teacher availability, time constraints, and learner motivation are also considered. By understanding these factors, educators can make informed decisions about instructional strategies, resources, and assessment methods to create a course that is suitable, practical, and realistic.

Overall, situational analysis is an essential step in course design, allowing educators to create effective and engaging learning experiences. By taking into consideration the unique characteristics of the learners, the learning environment, and the broader context, educators can design courses that are not only academically rigorous but also relevant and applicable to the real world.

Assessing the Current State in Education

Assessing the current state in education involves evaluating the existing educational landscape and identifying areas for improvement and innovation. It is a crucial step in situational analysis, as it provides valuable insights into the strengths and weaknesses of the educational system. By closely examining the current state, educators can better understand the challenges they face and develop strategies to address them.

One aspect of assessing the current state in education is analyzing the educational advancements that have taken place. This involves examining the use of technology in classrooms, the implementation of new teaching methodologies, and the incorporation of innovative learning tools. By staying abreast of these advancements, educators can adapt their teaching approaches to provide students with a more engaging and impactful learning experience.

In addition to analyzing educational advancements, assessing the current state also involves examining the education landscape as a whole. This includes studying social, economic, political, and institutional factors that may influence the effectiveness of the curriculum plan. By considering these external factors, educators can make informed decisions and implement changes that align with the needs and realities of the educational environment.

Table 1: Factors Influencing the Current State in Education

By conducting a comprehensive situational analysis that includes an assessment of the current state in education, educators can make informed decisions and design courses that are relevant, practical, and tailored to the needs of students. This analysis not only facilitates effective course design but also enables educators to stay ahead of the curve and embrace innovation in education, creating an environment that fosters optimal learning outcomes.

In conclusion, situational analysis plays a crucial role in education, enabling educators to make informed decisions and create impactful learning experiences. Through needs analysis, educators gather essential information about students’ abilities, needs, and purposes for learning. This helps categorize students’ needs into necessities, lacks, and wants, allowing educators to tailor the curriculum accordingly.

Situation analysis, on the other hand, identifies key factors that may positively or negatively affect the implementation of the curriculum plan. This includes social, economic, political, educational, and institutional factors. Equally important are classroom factors such as class size, teacher availability, time, and learner motivation. By considering all these factors, educators can ensure that the course is suitable, practical, and realistic.

By conducting a thorough situational analysis, educators are equipped with the necessary insights to design and deliver courses that meet the specific needs of their students. It helps them understand the context in which education takes place, and adapt their teaching methods accordingly. With the knowledge gained from situational analysis, educators can create a stimulating and effective learning environment, fostering optimal student engagement and achievement.

Q: What is situational analysis in education?

A: Situational analysis in education involves conducting needs analysis and situation analysis to gather information about students’ abilities, needs, and purposes for learning, as well as identifying key factors that may affect the implementation of the curriculum plan.

Q: Why is situational analysis important in education?

A: Situational analysis is important in education because it ensures that the course design is suitable, practical, and realistic. It helps educators understand students’ needs and tailor the curriculum to meet those needs. It also takes into account various factors that can impact the successful implementation of the curriculum.

Q: What are the steps involved in conducting situational analysis in education?

A: The steps involved in conducting situational analysis in education include gathering information about students’ abilities, needs, and purposes for learning (needs analysis), as well as identifying social, economic, political, educational, and institutional factors that may affect curriculum implementation (situation analysis).

Q: What factors are considered in situational analysis?

A: Situational analysis takes into account various factors, including social, economic, political, educational, and institutional factors. It also considers classroom factors such as class size, teacher availability, time, and learner motivation.

Q: Can you provide examples of situational analysis in education?

A: Examples of situational analysis in education could include conducting a needs analysis survey to determine students’ prior knowledge and skills, analyzing the economic factors that may affect the availability of resources for teaching, or assessing the institutional support and infrastructure for implementing a new curriculum.

Q: What are some analysis methods used in situational analysis?

A: Analysis methods used in situational analysis include needs assessment, context analysis, environmental scan, readiness assessment, gap analysis, SWOT analysis, problem analysis, root cause analysis, current state assessment, benchmarking, landscape review, background research, analytics, and diagnostics.

Q: How does situational analysis contribute to course design?

A: Situational analysis plays a crucial role in course design by providing essential information about students’ needs and the contextual factors that may influence the implementation of the curriculum. It ensures that the course is practical, relevant, and effective in meeting the desired learning outcomes.

Q: How is the current state in education assessed?

A: Assessing the current state in education involves analyzing the educational landscape, including advancements, challenges, and trends. It requires conducting research, gathering data, and evaluating the existing educational systems and practices.

About The Author

Ethan Emerson

Ethan Emerson is a passionate author and dedicated advocate for the transformative power of education. With a background in teaching and a love for writing, Ethan brings a unique blend of expertise and creativity to his contributions on ExquisitiveEducation.com .His articles are a delightful mix of insightful knowledge and engaging storytelling, aiming to inspire and empower learners of all ages. Ethan's mission is to ignite the spark of curiosity and foster a love for learning in every reader.Ethan Emerson, is your companion in the realm of general education exploration. With a passion for knowledge, He delves into the intricate world of Education Expenses & Discounts , uncovering financial insights for your educational journey. From the vitality of Physical Education to the synergy of Education & Technology , Ethan's here to bridge the gap between traditional and innovative learning methods. Discover the art of crafting impressive Resume & Personal Documentation in Education , as well as insights into diverse Career Paths, Degrees & Educational Requirements . Join Ethan in navigating through a sea of Educational Courses & Classes , exploring the nuances of various Education Systems , and understanding the empowering realm of Special Education . With an eye on Teaching & Teachers , He offers a glimpse into the world of educators who shape minds. Let's unlock Studying Tips & Learning Methods that turn education into a delightful journey of growth with Exquisitive Education .

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How higher-education institutions can transform themselves using advanced analytics

Leaders in most higher-education institutions generally understand that using advanced analytics can significantly transform the way they work by enabling new ways to engage current and prospective students, increase student enrollment, improve student retention and completion rates , and even boost faculty productivity and research. However, many leaders of colleges and universities remain unsure of how to incorporate analytics into their operations and achieve intended outcomes and improvements. What really works? Is it a commitment to new talent, technologies, or operating models? Or all of the above?

To answer these questions, we interviewed more than a dozen senior leaders at colleges and universities known for their transformations through analytics. We also conducted in-depth, on-campus visits at the University of Maryland University College (UMUC), a public institution serving primarily working adults through distance learning, and Northeastern University, a private nonprofit institution in Boston, to understand how their transformations went. 1 Our research base included presidents, vice presidents of enrollment management, chief data officers, provosts, and chief financial officers. In September 2017, we conducted on-campus visits to meet with leaders at several levels at both the University of Maryland University College (UMUC) and Northeastern University. We thank these leaders for generously agreeing to have their observations and experiences included in this article. We combined insights from these interviews and site visits with those gleaned from our work with more than 100 higher-education engagements across North America over the past five years, and we tapped McKinsey’s wide-ranging expertise in analytics-enabled transformations in both the public and private sectors.

Our conversations and engagements revealed several potential pitfalls that organizations may face when building their analytics capabilities—as well as several practical steps education leaders can take to avoid these traps.

Understanding the challenges

Advanced analytics use cases.

Northeastern used advanced analytics to help grow its U.S. News & World Report ranking among national universities from 115 in 2006 to 40 in 2017.

UMUC used advanced analytics to achieve a 20 percent increase in new student enrollment while spending 20 percent less on marketing.

Transformation through advanced analytics can be difficult for any organization; in higher education, the challenges are compounded by sector-specific factors related to governance and talent. Leaders in higher education cannot simply pay lip service to the power of analytics; they must first address some or all of the most common obstacles.

Being overly focused on external compliance . Many higher-education institutions’ data analytics teams focus most of their efforts on generating reports to satisfy operational, regulatory, or statutory compliance. The primary goal of these teams is to churn out university statistics that accrediting bodies and other third parties can use to assess each institution’s performance. Any requests outside the bounds of these activities are considered emergencies rather than standard, necessary assignments. Analytics teams in this scenario have very limited time to support strategic, data-driven decision making.

Isolating the analytics program in an existing department . In our experience, analytics teams in higher-education institutions usually report to the head of an existing function or department—typically the institutional research team or the enrollment-management group. As a result, the analytics function becomes associated with the agenda of that department rather than a central resource for all, with little to no contact with executive leadership. Under this common scenario, the impact of analytics remains limited, and analytics insights are not embedded into day-to-day decision making of the institution as a whole.

Failing to establish a culture of data sharing and hygiene . In many higher-education institutions, there is little incentive (and much reluctance) to share data. As a result, most higher-education institutions lack good data hygiene —that is, established rules for who can access various forms of data, as well as formal policies for how they can share those data across departments. For example, analytics groups in various university functions may use their own data sets to determine retention rates for different student segments—and when they get together, they often disagree on which set of numbers is right.

Compounding this challenge, many higher-education institutions struggle to link the myriad legacy data systems teams use in different functions or working groups. Even with the help of a software platform vendor, the lead time to install, train, and win buy-in for these technical changes can take time, perhaps two to three years, before institutions see tangible outcomes from their analytics programs. In the meantime, institutions struggle to instill a culture and processes built around the possibilities of data-driven decision making.

Lacking the appropriate talent. Budgets and other constraints can make it difficult for higher-education institutions to meet market rates for analytics talent. Colleges and universities could potentially benefit from sourcing analytics talent among their graduate students and faculty, but it can be a struggle to attract and retain them. Furthermore, to successfully pursue transformation through analytics, higher-education institutions need leaders who are fluent in not only management but also data analytics and can solve problems in both areas.

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Deploying best practices.

These challenges can seem overwhelming, but transformation through analytics is possible when senior leaders in higher-education institutions endeavour to change both operations and mind-sets.

Leaders point to five action steps to foster success:

Articulate an analytics mandate that goes beyond compliance . Senior leaders in higher education must signal that analytics is a strategic priority. Indeed, to realize the potential of analytics, the function cannot be considered solely as a cost center for compliance. Instead, this team must be seen as a source of innovation and an economic engine for the institution. As such, leaders must articulate the team’s broader mandate. According to the leaders we interviewed, the transformation narrative must focus on how analytics can help the institution facilitate the student journey from applicant to alumnus while providing unparalleled learning, research, and teaching opportunities, as well as foster a strong, financially sustainable institution.

Establish a central analytics team with direct reporting lines to executive leaders . To mitigate the downsides of analytics teams couched in existing departments or decentralized across several functions, higher-education leaders must explicitly allocate the requisite financial and human resources to establish a central department or function to oversee and manage the use of analytics across the institution. This team can be charged with managing a central, integrated platform for collecting, analyzing, and modeling data sets and producing insights quickly.

For example, UMUC has a designated “data czar” to help define standards for how information is captured, managed, shared, and stored online. When conflicts arise, the data czar weighs in and helps de-escalate problems. Having a central point of contact has improved the consistency and quality of the university’s data: there is now a central source of truth, and all analysts have access to the data. Most important, the university now has a data evangelist who can help cultivate an insights-driven culture at the institution.

In another example, leaders at Northeastern created an analytics center of excellence structured as a “virtual” entity. The center is its own entity and is governed by a series of rotating chairs to ensure the analytics team is aware of and paying equal attention to priorities from across the university.

In addition to enjoying autonomous status outside a subfunction or single department, the analytics team should report to the most-senior leaders in the institution—in some cases, the provost. When given a more substantial opportunity to influence decisions, analytics leaders gain a greater understanding of the issues facing the university and how they affect the institution’s overall strategy. Leaders can more easily identify the data sets that might provide relevant insights to university officials—not just in one area, but across the entire organization—and they can get a jump-start on identifying possible solutions.

Analysts at Northeastern, for instance, were able to quantify the impact of service-learning programs on student retention, graduation, and other factors, thereby providing support for key decisions about these programs.

Win analytics buy-in from the front line and create a culture of data-driven decision making . To overcome the cultural resistance to data sharing, the analytics team must take the lead on engendering meaningful communications about analytics across the institution. To this end, it helps to have members of the centralized analytics function interact formally and frequently with different departments across the university. A hub-and-spoke model can be particularly effective: analysts sit alongside staffers in the operating units to facilitate sharing and directly aid their decision making. These analysts can serve as translators, helping working groups understand how to apply analytics to tackle specific problems, while also taking advantage of data sets provided by other departments. The university leaders we spoke with noted that their analysts may rotate into different functional areas to learn more about the university’s departments and to ensure that the department leaders have a link back to the analytics function.

How to improve student educational outcomes: New insights from data analytics

Of course, having standardized, unified systems for processing all university data can help enable robust analysis. However, universities seeking to create a culture of data-driven decision making need not wait two years until a new data platform is up and running. Instead, analysts can define use cases—that is, places where data already exist and where analysis can be conducted relatively quickly to yield meaningful insights. Teams can then share success stories and evangelize the impact of shared data analytics, thereby prompting others to take up their own analytics-driven initiatives.

The analysts from UMUC’s decision-support unit sometimes push relevant data and analyses to the relevant departments to kick-start reflection and action, rather than waiting for the departments to request the information. However, the central unit avoids producing canned reports; analysts tend to be successful only when they engage departments in an honest and objective exploration of the data without preexisting biases.

Strengthen in-house analytical capabilities . The skills gap is an obvious impediment to colleges’ and universities’ attempts to transform operations through advanced analytics—thus, it is perfectly acceptable to contract out work in the short term. However, while supplementing a skills gap with external expertise may help accelerate transformations, it can never fully replace the need for in-house capacity; the effort to push change across the institution must be owned and led internally.

To do so, institutions will need to change their approaches to talent acquisition and development . They may need to look outside usual sources to find professionals who understand core analytics technologies (cloud computing, data science, machine learning, and statistics, for instance) as well as design thinking and operations. Institutions may also need to appeal to new hires with competitive financial compensation and by emphasizing the opportunity to work autonomously on intellectually challenging projects that will make an impact on generations of students and contribute to an overarching mission.

Do not let great be the enemy of good . It takes time to launch a successful analytics program. At the outset, institutions may lack certain types of data, and not every assessment will yield insightful results—but that is no reason to pull back on experimentation. Colleges and universities can instead deploy a test-and-learn approach: identify areas with clear problems and good data, conduct analyses, launch necessary changes, collect feedback, and iterate as needed. These cases can help demonstrate the impact of analytics to other parts of the organization and generate greater interest and buy-in.

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Realizing impact from analytics

It is easy to forget that analytics is a beginning, not an end . Analytics is a critical enabler to help colleges and universities solve tough problems—but leaders in higher-education institutions must devote just as much energy to acting on the insights from the data as they do on enabling analysis of the data. Implementation requires significant changes in culture, policy, and processes. When outcomes improve because a university successfully implemented change—even in a limited environment—the rest of the institution takes notice. This can strengthen the institutional will to push further and start tackling other areas of the organization that need improvement.

Some higher-education institutions have already overcome these implementation challenges and are realizing significant impact from their use of analytics. Northeastern University, for example, is using a predictive model to determine which applicants are most likely to be the best fit for the school if admitted. Its analytics team relies on a range of data to make forecasts, including students’ high school backgrounds, previous postsecondary enrollments, campus visit activity, and email response rates. According to the analytics team, an examination of the open rate for emails was particularly insightful as it was more predictive of whether students actually enrolled at Northeastern than what the students said or whether they visited campus.

Meanwhile, the university also looked at National Student Clearinghouse data, which tracks where applicants land at the end of the enrollment process, and learned that the institutions it had considered core competitors were not. Instead, competition was coming from sources it had not even considered. It also learned that half of its enrollees were coming from schools that the institution’s admissions office did not visit. The team’s overall analysis prompted Northeastern to introduce a number of changes to appeal to those individuals most likely to enroll once admitted, including offering combined majors. The leadership team also shifted some spending from little-used programs to bolster programs and features that were more likely to attract targeted students. Due in part to these changes, Northeastern improved its U.S. News & World Report ranking among national universities from 115 in 2006 to 40 in 2017.

In another example, in 2013 UMUC was trying to pinpoint the source of a decline in enrollment. It was investing significant dollars in advertising and was generating a healthy number of leads—however, conversion rates were low. Data analysts at the institution assessed the university’s returns on investment for various marketing efforts and discovered a bottleneck—UMUC’s call centers were overused and underresourced. The university invested in new call-center capabilities and within a year realized a 20 percent increase in new student enrollment while spending 20 percent less on advertising.

The benefits we discussed barely scratch the surface; the next wave of advanced analytics will, among other things, enable bespoke, personalized student experiences, with teaching catered to students’ individual learning styles and competency levels. To realize the great promise of analytics in the years to come, senior leaders must focus on more than just making incremental improvements in business processes or transactions. Our conversations with leaders in higher education point to the need for colleges and universities to establish a strong analytics function as well as a culture of data-driven decision making and a focus on delivering measurable outcomes. In doing so, institutions can create significant value for students—and sustainable operations for themselves.

Marc Krawitz is an associate partner in McKinsey’s New Jersey office. Jonathan Law is a partner in the New York office and leads the Higher-Education Practice. Sacha Litman is an associate partner in the Washington, DC, office and leads public and social sector analytics.

The authors would like to thank business and technology leaders at the University of Maryland University College and Northeastern University for their contributions to this article.

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Qualitative data analysis.

• Paul Mihas Paul Mihas University of North Carolina at Chapel Hill
• https://doi.org/10.1093/acrefore/9780190264093.013.1195
• Published online: 23 May 2019

Qualitative analysis—the analysis of textual, visual, or audio data—covers a spectrum from confirmation to exploration. Qualitative studies can be directed by a conceptual framework, suggesting, in part, a deductive thrust, or driven more by the data itself, suggesting an inductive process. Generic or basic qualitative research refers to an approach in which researchers are simply interested in solving a problem, effecting a change, or identifying relevant themes rather than attempting to position their work in a particular epistemological or ontological paradigm.

Other qualitative traditions include grounded theory, narrative analysis, and phenomenology. Grounded theory encompasses several approaches, including objectivist and constructivist traditions, and commonly invites researchers to theorize a process and perhaps identify its contexts and consequences. Narrative analysis is an approach that treats stories not only as representations of events but as narrative events in themselves. Researchers using this approach analyze the form and content of narrative data and examine how these elements serve the storyteller and the story. Other elements often considered include plot, genre, character, values, resolutions, and motifs. Phenomenology is an approach designed to “open up” a phenomenon and make sense of its invariant structure, its identifiable essence across all narrative accounts. In this approach, the focus is on the lived experiences of those deeply familiar with the phenomenon and how they experience the phenomenon as they are going through it, before it is categorized and conceptualized. Each tradition has its own investigative emphasis and particular tools for analysis—specific approaches to coding, memo writing, and final products, such as diagrams, matrices, and condensed reports.

• qualitative analysis
• basic qualitative research
• generic qualitative research
• grounded theory
• phenomenology
• narrative analysis
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• qualitative approaches
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What are learning analytics and why should they be used?

• Analytics (11)
• Case Studies (3)

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How to track student data in a school or district, guide to using district data in improvement efforts.

The use of data in education is not new.  However, advances in technology in terms of data collection, analysis, and visualization have created an environment where the questions that data can answer are more specific, more timely, and more tailored to the needs of individual stakeholders.  By being able to see the key information at the right time, those involved with a student, including the student themselves, can be informed about their status and the steps needed to help them succeed.

Learning Analytics: Definition

“Learning analytics refers to the collection and analysis of data about learners and their environments for the purpose of understanding and improving learning outcomes.”  (Northeastern University, 2020)   Education institutions use learning analytics to understand, share, and even predict, more about student learning, the environment and how to make decisions that can optimize teaching and learning, to promote equity and academic success.

What does that really mean?

Simply stated, it is the process of using analytic tools, in combination with the expertise of teachers, curriculum leaders, data coaches, and administrators to extract the insights about student performance, including social emotive learning (SEL), that are generated daily in classrooms and in online learning.  Those insights should drive the process of examining, and if needed, changing the teaching, learning, and environments.

Is it just reporting? Quite frequently, learning analytics are lumped in with the simpler education data reporting.  For example, a simple chart showing student performance on a state assessment is a report.  However, a visualization of whether it is a small set of students located in a particular school or classroom, versus a widely spread group of students across the school or district is the type of analysis that can help decision makers understand if an issue with student performance is because of a need for professional development, or because the curriculum does not sufficiently address a portion of the evaluation.

Another example would be providing teachers with high-level notifications of students’ needs at the very beginning of the year.  For example, a detailed notification that shows past learning skills or standards in which the students have not shown mastery, along with the possible impact those might have in the current year’s learning.  On its own, or combined with a schoolwide screen, such as those used in Multi-Tiered Support Systems (MTSS), it can highlight key information about the students’ learning experience and suggest additional interventions such as the use of educational technology tools.

How Does It Work?

Much like teaching, learning analytics is a lot of science, and a bit of “magic.”  A well-designed analysis will consider a variety of factors to uncover the insights about what it is that helps a student succeed.

It begins with the data already created, in student information systems (SIS), learning management systems (LMS), various online learning resources, and assessments, whether national, state, or local.  Those data can be further supplemented with information about SEL and school environments to provide a more holistic view of individual students, or groups.

Using the types of data analytics originally developed in other industries, learning analytics in education use technology to introduce teachers to their incoming students, identify patterns that highlight instructional needs, SEL concerns, monitor student progress, and predict likely outcomes such as student dropout.  Those insights can be provided either as individual feedback to students, parents/guardians and teachers, or in aggregate to teachers, school and district staff.

The benefits of learning analytics in education

All education stakeholders can benefit from the use of learning analytics.  However, different groups benefit from different flavors of analysis, tailored to their role in students’ learning.  The analysis provided to each group must meet their specific information needs.

For district administration

Charged with the success of students across the entire district, administrators have to balance the needs of a variety of students in different age groups, from different socio-economic backgrounds, with different social emotive needs, and different educational backgrounds.  Learning analytics help those administrators by surfacing insights such as:

• Tracking and predicting changing enrollment, or the composition of their student body
• Adherence to, and efficacy of online learning education technology usage
• Evaluating programs
• Outcome predictions, with suggestions about interventions to ensure student success
• Supporting staffing/funding decisions, such as additional English Language Learner staff/resources with an expanding ELL student population.

For school administration

Similar to their district counterparts, school administrators face a variety of challenges.  Learning analytics can answer questions about the overall status of the school, as well as student and staff performance.  Some examples include:

• Clarifying the impact of new policies, programs, or resources on student outcomes
• Responding to parent/staff/learner surveys about school environments
• Pinpointing insights that are only apparent by combining different data together, such as transportation information and its impact on student attendance
• Predictive notifications about student outcomes about achievement, proficiency, and graduation.

For other staff including curriculum leaders, data coaches, and guidance counselors

A variety of school staff also benefit from analytics.  Curriculum coaches can identify areas where their curriculum is meeting needs, as well as pinpoint the source of areas for improvement.  Data coaches can use well-designed analyses to provide both feedback to teachers and administrators on student learning, as well as increase the frequency and that feedback, so that it helps build a culture of data usage. Guidance counselors can have a much more holistic view of the students, so that their guidance is more apt to bring about the desired results.  Examples for these stakeholders include:

• Identifying if a need is widely spread, and might point to a curriculum need, or confined, which might benefit from professional development
• Helping data coaches provide teachers with answers to “How are my students doing?” and “How do I help my students succeed?”
• Providing guidance counselors with SEL data to supplement their understanding of students’ needs.

For teachers

Teachers are in many ways the personification of an analytics tool, and the group that benefit the most from learning analytics.  On a daily basis, they process large amounts of data about their students, from the academic, to the personal, to the behavioral and more.  However, with the explosion of the use of blended learning and online learning, the additional data is harder to access as it sits siloed in various systems, requiring time that would be best used to prepare for student success.  An analytics tool provides several benefits to teachers:

• Answering the two most basic questions, “How are my students doing?” and “How do I help my students succeed?”
• Extracting of the most important information about their students from a variety of systems, allowing them to evaluate additional data while saving time
• Helping teachers cut through the clutter of data, by notifying teachers of high-value suggestions for actions to monitor and promote student progress and achievement
• Consolidating information about student performance from various tools and platforms in a simple view
• Analyzing individual student performance on its own, or in context of a group, to identify specific areas of needs in terms of skills, standards, and competencies needed by one, a few, or most students
• Ability to combine progress data with other factors, such as SEL, to support additional feedback, e.g., providing a “pat on the back” to students who improved in some measure of performance and are motivated by praise.

For parents/guardians

“Parental involvement and engagement in education matters now more than ever because it’s in decline.” (Waterford, 2018)   

While a number of factors, including societal changes, are behind that trend, providing parents/guardians with clear, succinct information about their learners can go a long way towards keeping them engaged with the students’ learning process.  Good analytics tools will extract the most important information and make it readily available, so that parents/guardians do not have to wade through mountains of data to arrive at a clear understanding of their learners’ status and needs.  For parents/guardians, learning analytics should answer the following questions:

• How is my student doing?
• Where are they headed?
• What do they need to do to succeed?

For students

“[A]cademic feedback is more strongly and consistently related to achievement than any other teaching behaviour[sic]…this relationship is consistent regardless of grade, socioeconomic status, race, or school setting.” (Reading, Bellon et al, 1991)

Not unlike their teachers, or parents/guardians, students are awash in data.  From teacher feedback, to online learning resources, to assessments, and from their environments.  Helping guide them towards self-understanding, as well as how to process the feedback they are receiving so that they can identify their needs and successes is critical in helping them prioritize and succeed. Learning analytics for students should succinctly answer the following:

• How am I doing?
• What do I need to succeed?

AnalyticVue and Learning Analytics 

AnalyticVue responds to the learning analytics needs of all of the above stakeholder types.  It includes and exceeds the traditional education data reporting by providing the types of analyses that generate higher-level questions about how to help students succeed.

Its analysis of academic performance uncovers underlying issues whether pertaining to learning environments, individual student needs, or curricula.  It offers clear feedback to parents/guardians and students themselves.  It builds on the work traditionally done by teachers, administrators, curriculum leads, and data coaches in collating and analyzing data to inform instruction and student interventions.  It creates those analyses automatically, as soon as the underlying data changes so that our partner districts can take advantage of these features to conduct data meetings on a regular basis, and to make data use part of their daily culture.

We are able to do this because of our decades of experience in examining, consolidating, and analyzing education data, and, just as importantly, our decades of listening to and partnering with education entities to understand their needs in terms of data analytics to optimize their students’ learning experience, environment, and outcomes.

Related posts

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Analyticvue - integrity-: maximizing impact: unleashing data analytics with analyticvue for administrators, teachers, and students.

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This session will provide you with strategies to harness the power of data analytics for educational excellence. Discover how to use AnalyticVue to empower administrators with data-driven insights to make informed decisions, equip teachers with tools to enhance their instructional strategies, and elevate student outcomes.

• Manos Stefanakos (Speaker)Analyticvue Inc, Chief Data Architect
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AnalyticVue - Integrity: Effortless MTSS Implementation with AnalyticVue: Simplify Student Support

Discover how AnalyticVue seamlessly integrates the Multi-Tiered System of Supports (MTSS) into your district's workflow, alleviating the burden on administrators and teachers. Explore how our comprehensive data platform, combined with powerful analytics, simplifies the MTSS process from identification to intervention. Learn how AnalyticVue automates data collection, analysis, and reporting, allowing educators to focus on what matters most – supporting students.

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AnalyticVue - Integrity: AnalyticVue Demonstration for the French River Education Center (FREC) Member Districts

FREC member districts are invited to a personalized demonstration of AnalyticVue. This session will provide FREC members with strategies to harness the power of data analytics for educational excellence. Discover how to empower administrators with data-driven insights to make informed decisions, equip teachers with tools to enhance their instructional strategies, and elevate student outcomes.

AnalyticVue - Integrity: AnalyticVue and the Framingham Public Schools: A Case Study in Data Analytics

Learn how the Framingham Public Schools successfully implemented the AnalyticVue platform. See how AnalyticVue helps to help monitor the almost daily changes to Framingham’s student population to identify equity trends and appropriately focus resources. We can also identify and monitor students for small group instruction and those in need of Multi-Tiered System of Supports (MTSS) interventions.

• Ann Mariano (Speaker)Framingham Public Schools, Director of Educational Technology

Thursday, October 19th

AnalyticVue - Integrity: Maximizing Impact: Unleashing Data Analytics with AnalyticVue for Administrators, Teachers, and Students

Analyticvue - integrity: analyticvue user group meeting at masscue: join the discussion.

Calling all AnalyticVue users and those interested in data-driven education! We invite you to our exclusive User Group Meeting to bring the AnalyticVue community together for an in-person, enriching discussion. This session is open to all participants, providing a unique opportunity for cross-district collaboration in support of education data.

AnalyticVue - Integrity: Harness Your SIS Data: AnalyticVue's Streamlined API Integration with Popular Student Information Systems

This session focuses on AnalyticVue's seamless API integration with popular Student Information Systems across Massachusetts. Dive into the operational data that drives educational processes, and discover how AnalyticVue ingests and leverages this data to generate actionable insights. Learn how AnalyticVue’s integration with your SIS and other applications will take days ... not months!

• Ravi Devulapalli (Speaker)AnalyticVue Inc.,

BEHAVIOR ANALYSIS IN EDUCATION

Behavior analysis has been used to improve teaching and increase learning across content areas, grade levels, and student populations for over 60 years. It provides a scientific approach to designing, implementing, and evaluating instruction based on analyzing interactions between what the teacher does and student learning.

Education is one of many applied behavior analysis (ABA) subspecialties. To learn more about ABA and its application in other subspecialties, check out the About Behavior Analysis web page.

Education Subspecialty Fact Sheet

Behavior Analysis in Education is one of several ABA subspecialty fact sheets produced by the BACB in partnership with subject matter experts (SMEs).

Each fact sheet also includes a list of additional resources and reading materials for those who wish to learn more.

This resource may be freely distributed and hosted online.

Introduction to Behavior Analysis in Education

By Janet Twyman, PhD, BCBA, LBA

You can find all of the BACB’s ABA subspecialty videos on our BACB Videos web page or our YouTube channel.

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Education sector analysis

An education sector analysis (ESA) is an in-depth, holistic diagnosis of an education system. It assists with understanding how an education system (and its subsectors) works, why it works that way, and how to improve it. An ESA provides the evidence base for decision-making and is the first step in preparing an education sector plan.

An ESA is a nationally driven process, involving collaboration and dialogue among different actors and institutions in a system. Empowering and consulting the different stakeholders throughout the process are essential, as ‘sustainable changes that lead to improved learning outcomes cannot be brought about in the absence of involvement of the individuals and groups who will implement the change’ (Faul and Martinez, 2019: 31).

The ESA process must therefore be participative and aim to create an understanding of the key stakeholders in the education system, their incentives, relationships and accountability, as well as how these dynamics shape education systems (IIEP-UNESCO et al., 2021).

What does an ESA cover?

An ESA includes context analysis, existing policy analysis, cost and finance analysis, education performance analysis, and system capacity analysis, including stakeholder analysis (IIEP-UNESCO and GPE, 2015). Any challenges identified through the ESA should be analysed through the lens of Sustainable Development Goal 4 (UNESCO, 2016). Quality of learning is one factor analysed in the performance of the education system along with issues related to access and coverage, equity and inclusion, and internal and external efficiency of the system. Quality of learning involves analysing the range of inputs and processes including teachers, learning and teaching materials, school facilities, and learning outcomes (IIEP-UNESCO and GPE, 2015; IIEP-UNESCO, World Bank, and UNICEF, 2014).

Teachers play a decisive role in ensuring learning quality. Teacher management features – ranging from recruitment and deployment to pre- and in-service training, career pathways, motivation and job satisfaction, absenteeism and effective teaching time – also need to be analysed. Typical indicators include (IIEP-UNESCO, World Bank, and UNICEF, 2014):

• Pupil/teacher ratio by level for primary education
• Pupil/trained teacher ratio
• Teacher utilization rate
• The consistency in teacher allocation (R2 coefficient)
• Theoretical teaching time in relation to theoretical instruction time for secondary teachers
• The percentage of pre- and in-service teachers trained by level
• The number of teachers disaggregated by status (civil servants, contract, or community teachers)
• Qualifications and teaching experience

Learning and teaching materials

An ESA should analyse the equitable allocation of learning and teaching materials and other inputs among different schools and regions. An ESA should include indicators such as the proportion of teachers with teacher guides, pupil/textbook ratios, and the notion of useful pupil/textbook ratio (IIEP-UNESCO, World Bank, and UNICEF, 2014). Qualitative information gathered through teacher interviews, for example, can also be integrated into the analysis to complement quantitative data. For instance, in crisis-affected areas, quantitative data may be weak regarding the actual distribution and use of textbooks throughout the country (IIEP UNESCO and GPE, 2016).

School facilities

School facilities (school buildings and infrastructure such as electricity or school landscaping) can have a significant impact on students’ learning achievements. Proper water, sanitation and hygiene (WASH) facilities in schools can improve access to education and learning outcomes, particularly for girls (UNICEF and WHO, 2018). Relevant indicators include classroom utilization rate and, when applicable, type of classroom (such as temporary, open air, permanent, or home-based classrooms); the percentage of schools with functioning WASH facilities; the percentage of schools with electricity; the percentage of schools with boundary walls for security reasons; and the percentage of classrooms that need to be rehabilitated (IIEP-UNESCO, World Bank, and UNICEF, 2014).

Learning outcomes

Student assessments include national examinations and admission tests, national large-scale learning assessments, regional or international standardized assessments, citizen-led assessments, and household surveys. The analysis of learning assessments enables education planners and decision makers to understand whether the education system is transferring knowledge to students as expected, as well as whether this transfer is equitable or is leaving certain population groups or geographic areas behind. Learning assessments can further help countries track the progress of learning achievements over time, compare results with comparable countries, and identify plausible causes for weak learning outcomes (IIEP UNESCO, World Bank, and UNICEF, 2014).

However, there are several risks when using learning data, such as the accuracy of data and their interpretation; the use of a single test score for decision-making; the use of learning assessment data to legitimize predefined agendas; and narrowing educational measurements to simplified indicators (Raudonyte, 2019).

Changes in learning assessment results over time should be interpreted with caution and cross-checked with other evidence. For instance, a sharp increase in enrolments may affect learning outcomes (IIEP-UNESCO, World Bank, and UNICEF, 2014).

ESA data sources

An effective ESA relies on both qualitative and quantitative rigorous data. Relevant data sources include (IIEP-UNESCO and GPE, 2015; IIEP-UNESCO et al., 2021; IIEP-UNESCO, World Bank, and UNICEF, 2014):

• National, regional and international learning assessments: provide information on whether the education system is transferring knowledge as expected; track progress on learning achievements over time; allow comparisons with comparable countries; and identify plausible reasons behind weak learning outcomes.
• School data on students, textbooks, teachers, and subsidies: provide information on resource distribution and learning time, among others.
• Administrative manuals: provide information on teacher management, teaching time, and other resources.
• Teacher training institute data: provide information on whether the capacities of teacher training institutes meet current and projected needs.
• Human resources data: provide information about teacher recruitment, deployment and utilization, among others.
• Sample surveys: can be used to assess teaching and learning time.
• Household surveys: provide information on the relationship between the level of literacy and the number of years of schooling.
• Specific research exercises: provide valuable information on relevant issues faced by education systems.
• Interviews and questionnaires of stakeholders: provide relevant qualitative information, for instance related to institutional capacity.

An ESA should further assess information gaps and whether primary data collection will need to be undertaken to obtain missing information (IIEP-UNESCO and GPE, 2015).  

Plans and policies

• Liberia: Education Sector Analysis
• Somalia:  Education Sector Analysis
• IIEP-UNESCO; Global Partnership for Education. 2015. Guidelines for Education Sector Plan Preparation
• IIEP-UNESCO; Global Partnership for Education; UNICEF; Foreign, Commonwealth and Development Office. 2021. Education Sector Analysis Methodological Guidelines: Vol. 3: Thematic Analyses
• IIEP-UNESCO; World Bank; UNICEF. 2014. Education Sector Analysis Methodological Guidelines: Vol 1: Sector-wide Analysis, With Emphasis on Primary and Secondary Education
• IIEP-UNESCO; World Bank; UNICEF. 2014. Education Sector Analysis Methodological Guidelines: Vol. 2: Sub-sector Specific Analysis
• UNESCO-UIS. 2009. Education Indicators: Technical Guidelines

Faul, M.; Martinez, R. 2019. Education System Diagnostics. What is an 'Education System Diagnostic', Why Might it be Useful, and What Currently Exists?

IIEP-UNESCO; GPE (Global Partnership for Education). 2015. Guidelines for Education Sector Plan Preparation. Paris: IIEP-UNESCO.

––––. 2016. Guidelines for Transitional Education Plan Preparation. Washington, DC: GPE.

IIEP-UNESCO; GPE (Global Partnership for Education); UNICEF; FCDO (Foreign, Commonwealth and Development Office). 2021. Education Sector Analysis Methodological Guidelines: Vol. 3: Thematic Analyses .  Dakar: IIEP-UNESCO.

IIEP-UNESCO; World Bank; UNICEF. 2014.  Education Sector Analysis Methodological Guidelines: Vol 1: Sector-wide Analysis, with Emphasis on Primary and Secondary Education.  Dakar: IIEP-UNESCO.

Raudonyte, I. 2019. Use of Learning Assessment Data in Education Policy-making. Paris: IIEP UNESCO.

UNESCO. 2016. Mainstreaming SDG4-Education 2030 in Sector-wide Policy and Planning: Technical Guidelines for UNESCO Field Offices. Paris: UNESCO.

UNICEF; WHO (World Health Organization). 2018. Drinking Water, Sanitation and Hygiene in Schools: Global Baseline Report 2018. New York, NY: UNICEF and WHO.

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{{item.title}}, my essentials, ask for help, contact edconnect, directory a to z, how to guides, school excellence and accountability, situational analysis.

The situational analysis is a key component of the School Excellence cycle and is used to inform your school’s improvement journey in learning, teaching and leading.

What is a situational analysis?

A situational analysis is an authentic and rigorous assessment of your school’s current state and is used to inform your school’s improvement journey in learning, teaching and leading.

The situational analysis is a key component of the School Excellence cycle and is an essential step in the development of a Strategic Improvement Plan (SIP) that will effectively prioritise your school's identified areas for growth.

Conducting a situational analysis allows your school to develop a rich and highly contextualised understanding of your school’s current situation.

As part of this situational analysis, every school collects qualitative and quantitative data, collates evidence, collaborates widely, and engages with research. The following process is the suggested option available to schools.

Through a process of ‘looking inward’, ‘looking outward’ and ‘looking forward’, a situational analysis answers the questions:

• ‘Where are we now?’
• ‘Where do we want to be?'
• 'How good can we be?'

Looking inward, looking outward and looking forward

Looking inward

• Data: what data do we have and what does it tell us? How do we know and what evidence do we have?
• Views and feedback: have we considered the views and feedback of staff, students and parents/ carers about where the school is now and where it needs to be? How do we know and what evidence do we have?
• Professional judgement: using professional judgement, have we considered what has been done, how well has it been done, and what happened as a result? How do we know and what evidence do we have?

Looking outward

• Priorities: have we considered current educational priorities?
• Research: what does reliable and relevant research tell us about effective strategies for school improvement that are relevant to our current context?
• Opportunities: what are the opportunities within and beyond my professional learning network that are contextually relevant?

Looking forward

• Decommission: which current practices or initiatives will we decommission (stop doing)?
• Adapt and improve: how can we consolidate, adapt and improve on our current high impact practices and initiatives?
• Innovate: what new, innovative practices or initiatives should we adopt in our next Strategic Improvement Plan (SIP)?

By 'looking inward', 'looking outward' and 'looking forward', your school gains the clarity to envision and map your future directions in the next phase of the School Excellence cycle. A situational analysis enables your school to develop a context-specific Strategic Improvement Plan (SIP) that answers the questions:

• Where do we need to go now?
• How will we get there?

Features of a situational analysis process

Each and every school, including yours, is a complex, student-centred and diverse learning community.

While unique trends, features and future directions may be revealed about a learning community through a situational analysis, the process of conducting the analysis is likely to be similar in each school. See the following key features.

What it is and what it isn't

A situational analysis is:.

• an identification of the needs of your students, your teachers and your school
• underpinned by relevant and reliable data
• a process to establish a common understanding of where your school is at
• a consideration of your school's performance in relation to its improvement measures
• inclusive of the learning needs of your teachers and leaders as well as your students
• collaborative and consultative
• a process that engages all staff in looking forward and determining future directions for the school.

A situational analysis is not:

• developed in isolation by a small group or individual
• a submission for funding
• a public-facing document
• a list of wants or desires
• focused on a small but vocal section of the school or community
• a top down approach.

Find more information about School Excellence in Action .

For further support go to School Excellence in Action Support (staff only).

Or contact the team at [email protected] .

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Open Education Sociology Dictionary

Table of Contents

Definition of Analysis

( noun ) The process of separating a whole into its parts for discussion, interpretation, or study .

Types of Analysis

• content analysis
• cross-sectional analysis
• data analysis
• secondary analysis
• social network analysis
• statistical analysis

Analysis Pronunciation

Pronunciation Usage Guide

Syllabification : a·nal·y·sis

Audio Pronunciation

Phonetic Spelling

• American English – /uh-nAl-uh-sis/
• British English – /uh-nAl-i-sis/

International Phonetic Alphabet

• American English – /əˈnæləsəs/
• British English – /əˈnalᵻsɪs/

Usage Notes

• Plural: analyses
• The unit of analysis is the basic element of  research .
• A type of method .
• An ( noun ) analyst or ( noun ) analyzer ( adverb ) analytically ( verb ) analyzes data that is ( adjective )  analyzable and has ( noun ) analyzability .

Related Videos

Additional Information

• Qualitative Research Resources – Books, Journals, and Helpful Links
• Quantitative Research Resources – Books, Journals, and Helpful Links
• Word origin of “analysis” – Online Etymology Dictionary: etymonline.com
• analysis – The Indiana Philosophy Ontology Project: inpho.cogs.indiana.edu
• analysis – Stanford Encyclopedia of Philosophy: plato.stanford.edu

Related Terms

• reliability

Works Consulted

Oxford University Press. (N.d.) Oxford Dictionaries . ( https://www.oxforddictionaries.com/ ).

Random House Webster’s College Dictionary . 1997. New York: Random House.

Tischler, Henry L. 2011.  Introduction to Sociology . 10th ed. Belmont, CA: Wadsworth.

Wikipedia contributors. (N.d.) Wiktionary, The Free Dictionary . Wikimedia Foundation. ( http://en.wiktionary.org ).

Cite the Definition of Analysis

ASA – American Sociological Association (5th edition)

Bell, Kenton, ed. 2014. “analysis.” In Open Education Sociology Dictionary . Retrieved August 9, 2024 ( https://sociologydictionary.org/analysis/ ).

APA – American Psychological Association (6th edition)

analysis. (2014). In K. Bell (Ed.), Open education sociology dictionary . Retrieved from https://sociologydictionary.org/analysis/

Chicago/Turabian: Author-Date – Chicago Manual of Style (16th edition)

Bell, Kenton, ed. 2014. “analysis.” In Open Education Sociology Dictionary . Accessed August 9, 2024. https://sociologydictionary.org/analysis/ .

MLA – Modern Language Association (7th edition)

“analysis.” Open Education Sociology Dictionary . Ed. Kenton Bell. 2014. Web. 9 Aug. 2024. < https://sociologydictionary.org/analysis/ >.

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Definition of analysis noun from the Oxford Advanced Learner's Dictionary

• statistical/data analysis
• a detailed analysis of the data
• to perform/conduct an analysis
• Further analysis revealed significant regional variations in the results.
• We made the decision based on our analysis of the situation.
• to be included in/excluded from the analysis
• The book is an analysis of poverty and its causes.
• At the meeting they presented a detailed analysis of twelve schools in a London borough.
• More analysis has been done on the process of ageing.
• We performed a comparative analysis of genes from different species.
• They carried out an in-depth analysis of the results.
• Researchers identified themes from the content analysis of interviews.
• He gave a brief analysis of the present economic situation.
• In his analysis of the novel he discusses various aspects of the author's own life.
• In the final analysis, the people were stronger than the generals.
• comprehensive
• indicate something
• reveal something
• show something
• in an/​the analysis
• in the final analysis
• in the last analysis

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