Data plays a critical role in modern education. It allows educators to monitor student progress, identify areas for improvement, and make informed instructional decisions. By using data, teachers can: Track performance trends over time Identify struggling learners early Adapt teaching methods based on evidence rather than assumptions

Data supports, among other things:

• Personalizing the learning process
• Monitoring student progress
• Researching and assessing the effectiveness of teaching methods
• Supporting student development through feedback analysis
• Early identification of needs and interventions

Data allows teachers to better tailor materials and methods to individual needs, and students gain greater control over their learning.

Educational data comes in two main types:

• Quantitative: Numerical data such as test scores, attendance records, grades
• Qualitative: Descriptive data such as teacher observations, student feedback, classroom interactions

Combining both gives a holistic view of student learning.

• Quantitative data, allowing for statistical analysis:
  • Test scores
  • Attendance
  • Time spent on the platform
• Qualitative. Allowing for a better understanding of the context.
  • Teacher observations
  • Student opinions
  • Survey comments

With increased data usage comes responsibility. Educators must: Protect student privacy under regulations like GDPR Use anonymized data when sharing results Secure storage systems to prevent breaches Ensure compliance with data protection regulations, anonymize data when necessary, and secure data storage to maintain privacy and trust

Working with data requires compliance with security principles, primarily:

1. Data Confidentiality (GDPR)
2. Data Minimization, collecting data that is necessary and important for educational processes
3. Secure storage and access
4. Obtaining consent for data processing  

Data collection should begin with the implementation of privacy principles at the design stage of educational systems, which will help effectively protect student data. 

Privacy by Design Principle: Implementing this principle in education helps not only protect data but also build trust among students and their families. 

Privacy by Design Principle is based on key pillars:

• Education of teaching staff
• Data Minimization
• Secure Technologies

Privacy Policy: It is recommended to implement a privacy policy that clearly outlines how data is collected, stored, and used. This policy should be easily accessible to students, parents, and the entire school community.

• Engaging Students in the Privacy Protection Collaboratively establishing
• Other Methods: Anonymous Surveys, Electronic Journal Access Control

Common sources include: Assessments and tests Attendance records Digital learning platforms (e.g., Google Classroom, Moodle) Student self-assessments and surveys

Quantitative, qualitative, and mixed data in education are obtained from a variety of sources, such as:

• Electronic journals
• Tests and assessments
• LMS platforms (Moodle, Teams, Google Classroom)
• Mobile and e-learning applications
• Teacher observations
• Student feedback

Use standardized formats for consistency:

• Implement digital tools to reduce errors
• Schedule regular collection intervals (weekly, monthly)

Use standardized formats, digital tools, and regular intervals for collecting data.

AI automates many processes, for example: chatbots can collect student feedback, adaptive systems monitor activity and adjust pace, and algorithms organize data into clear dashboards.

Quantitative data collection methods:

• Statistical data analysis
• Experiments
• Tests, surveys, and questionnaires

Qualitative data collection methods:

• Observation
• Document analysis
• Group interviews
• Oral interviews

Mixed data collection methods:

• Content analysis combines quantitative and qualitative data, allowing for a more comprehensive picture of the reality being studied

Create categorized folders for easy access Use spreadsheets or databases for structured storage Apply backup protocols for data safety Organize data in categorized folders, use spreadsheets or databases, and implement backup protocols. Data should be stored in an organized format. This can be achieved with AI support, which can suggest the most important information, automatically categorize answers, or group students by progress level.

Modern technologies can be used for data storage, for example:

• Degoo App: A cloud-based data storage and sharing platform. Degoo is a cloud-based file storage tool that allows users to store, organize, and share their data securely. The app is available on various platforms, making it easy to access files from any internet-connected device.

Descriptive Statistics: Mean, median, mode Visualization: Graphs, charts for trend spotting Comparative Analysis: Comparing current and past results Descriptive Statistics:

Descriptive statistics in education are used to collect, organize, analyze, and present educational data, such as student achievement, attendance, and demographic data, to summarize and understand their characteristics. They are used to describe central tendency (mean, median, mode), dispersion (variance, standard deviation), and to create graphical and tabular data presentations (histograms, box plots). They help understand dominant values, how variance in data occurs, identify outliers, and analyze relationships within a sample, which provides a basis for decision-making in pedagogy and educational management. 

Applications of descriptive statistics in education include: Assessing learning outcomes Monitoring student progress Understanding group characteristics Presenting data clearly Supporting management decisions

Basic techniques used in descriptive statistics in education:

• Measures of central tendency
• Arithmetic mean: Provides a general idea of ​​a typical score
• Median: The middle value, less susceptible to the influence of extreme values
• Mode: The most frequently occurring value

Measures of Dispersion:

• Variance: Shows the mean squared deviation from the mean, providing an idea of ​​variation
• Standard Deviation: The square root of the variance, expressing variation in units of data
• Graphical Techniques
• Histograms: Illustrate the distribution of data, showing which values ​​are most common
• Box Plots: Great for detecting outliers and visualizing data dispersion

Look for: Consistent low performance across subjects Attendance-performance correlation Gaps between assessment and participation Data enables informed decision-making. Whether discussing student progress, academic achievement, or school district results, data underpins every decision school leaders and teachers make.

• Pattern recognition – This is defined as the act of taking raw machine learning data and taking further action based on the data's category.Learning analytics is an emerging field that focuses on measuring, collecting, analyzing, and reporting data about students and their contexts to improve learning.
• Learning analytics helps educators, institutions, and organizations improve student experiences by leveraging the power of big data and making data-driven decisions. In the digital age, students generate vast amounts of data as they interact with various platforms and learning systems. Analyzing this data can provide valuable insights into learner behaviour, patterns, and outcomes.

Google Sheets Excel Power BI / Tableau Learning Management System (LMS) analytics dashboards The use of new AI-powered technologies for data acquisition and educational analytics expands the possibilities of delivering targeted data, such as prediction (e.g., predicting the risk of course dropout), pattern recognition (e.g., common test errors), and automated reporting (e.g., weekly summaries in the form of charts and recommendations).

Artificial intelligence enables the analysis of vast amounts of educational data.

• AI-based systems can track student progress, identify their strengths and weaknesses, and provide personalized recommendations. Teachers have access to detailed reports that help identify areas requiring more attention. This enables personalized attention for each student, tailored to their needs and learning pace. AI in educational data analysis also enables the identification of trends and patterns in learning, which can help improve teaching processes.
• Tools: Excel and Google Data Studio – provide basic analysis
• Power BI – a system for delivering advanced reporting
• Learning analytics systems in LMS – conduct advanced analysis of student activity
• AI models (e.g. ChatGPT) – help with data interpretation and recommendations

Data helps: Group students by skill level Assign differentiated tasks Provide targeted feedback Personalization comes from recognizing and fully utilizing learners' potential by adapting elements of the learning process to their individual needs and predispositions. It requires time and attention. It requires understanding the individual and their predispositions.

• Personalization using AI. Data collected and processed by AI enables: adapting materials to individual needs, monitoring progress in real time, recommending additional exercises and resources, and identifying students requiring additional support.
• Implementing adaptive learning: Learning tailored to individual needs. Students follow adaptive learning paths, practicing tasks they are struggling with. An example is the EDUBOT platform, which allows for the identification of challenges in math learning that students are struggling with throughout their learning journey.
• Personalized learning allows students to practice at every stage, based on their individual needs. Identifying the appropriate challenge for a student's goal and then providing them with opportunities for conscious practice with feedback are effective strategies for personalized learning.

• Early intervention for struggling students
• Regular progress monitoring meetings

Digital technologies play a key role in increasing student engagement. By integrating modern educational tools, such as e-learning and testing platforms, schools can not only increase the interactivity of the teaching process but also provide students with easy access to educational materials and learning support tools.

• Adaptive systems – digital platforms or programs that adapt materials to students' level and pace. These solutions allow students to learn at their own pace, giving them a sense of greater control over their own development. For example, AI can support this process by automatically adapting content to student needs and providing personalized support in real time, allowing for faster resolution of learning problems.
• Autonomy in Learning Students who have the freedom to choose their subjects are more engaged in their studies because they can tailor their education to their interests and career goals, which increases their motivation. The school can offer personalized courses, helping students better plan their educational path according to their needs and aspirations. This approach allows for greater flexibility in learning, further increasing the engagement and effectiveness of the educational process.

Set measurable goals

Monitor and adjust strategies based on results

Data in education is used to plan development by analyzing student progress and learning needs, as well as to evaluate activities by measuring the effectiveness of teaching strategies and programs. Key data types include student achievement data (e.g., test scores), demographic data, program data (e.g., assessment of the effectiveness of teaching methods), and perception data (e.g., student and teacher opinions). Systematic collection and analysis of this data allows teachers and principals to adapt teaching, identify strengths and weaknesses, create individual development paths, and implement improvements in the education system.

Data that supports the development and evaluation of development plans and activities

• Student achievement data
• Demographic data
• Curriculum data
• Perception Data

Data that supports the creation of development plans

• Needs Identification: Data allows for the identification of specific learning needs of students and classes, identifying priority topics for teaching
• Adapting Instruction: Teachers can use data to differentiate instruction, creating individual paths for students or grouping them based on shared needs
• Setting Goals: Data analysis allows for the establishment of realistic development goals for students and the entire educational institution