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Leertrac – Data-Driven Educational Insights at GITO Overijse

Received Golden Case recognition at the CAF Users Event 2025 in Warsaw

CAF Best Practice
Date of publication: July 2025

Executive Summary

The Leertrac project at GITO Overijse integrates learning analytics into student deliberations by utilizing Moodle and a Learning Record Store (LRS). The project applies systems thinking through causal loop diagrams (CLD) to improve data-driven decision- making. Additionally, Leertrac fosters teacher autonomy over instructional material and transitions educators from isolated publisher-driven systems to a generative AI-supported approach.

About the Organisation

GITO Overijse is a secondary school in Flanders governed by the municipal council. It provides education tailored to higher education and regional labor market needs, with an emphasis on STE(A)M, Sports, and vocational training.

As a progressive institution, GITO Overijse actively pursues digital transformation, leveraging technology to enhance pedagogy. The school employs Moodle as its Learning Management System (LMS) and has engaged in innovative projects such as Leertrac, aligning with the CAF/OK framework. The initiative aims to improve the quality of education through learning analytics, supporting students’ competency development while ensuring that decisions at various levels of school governance are informed by data.

Quality Improvement Aspects

The motivation behind Leertrac stems from challenges identified in student deliberation processes. Typically, discussions about student progress and performance relied heavily on final grades, with limited contextual data from teachers. While Moodle already stored vast amounts of learning activity data, it was not systematically utilized to inform decision-making.

The objectives of Leertrac include:

  • Centralizing learning analytics to support student deliberation with meaningful
  • Facilitating real-time monitoring of student progress toward learning
  • Reducing reliance on proprietary publisher-driven systems, encouraging teachers to develop their own educational content.
  • Applying systems thinking, specifically causal loop diagrams (CLD), to model learning dynamics and identify underlying factors affecting student performance.

Causal loop diagrams (CLD) are a visual representation of cause-and-effect relationships within a system. In education, these diagrams help map out the various factors influencing student learning outcomes, such as motivation, engagement, assessment feedback, and instructional quality. By identifying feedback loops—where certain actions reinforce or counteract each other—educators gain insights into how specific interventions impact overall learning outcomes.

This project also aligns with institutional goals for digital transformation, the integration of AI-supported learning, and the promotion of open-source educational resources.

Stakeholders and Communication

The project was initiated by the leadership of GITO Overijse, with the school director overseeing its development. During a personnel meeting, the CAF self-assessment process was introduced, and interested teachers and staff members volunteered to participate. The city council was also informed to ensure alignment with governance priorities.

Key stakeholders include:

  • Teachers and Staff: Actively engaged in CAF self-assessment, feedback collection, and dashboard development.
  • Students and Parents: Provided input via surveys regarding their needs and
  • External Partners: Collaboration with GO! CVO Antwerpen, KU Leuven (Augment – Computer Sciences) and Eummena vzw to develop and refine the interactive
  • Consultants: Ingeborg Maes (Insightful.be) facilitated CAF sessions and merged CAF and the Quality framework of the Flemish Ministery of Education, with additional support from Isabelle Verschueren (SPF BOSA of the federal Belgian government).

Communication was maintained through structured internal meetings, stakeholder workshops, and digital updates via the school’s Moodle platform. The project’s progress and outcomes will be shared externally through CAF events and contributions to the Moodle community.

Implementation Process/Approach

The implementation of the Leertrac project followed a structured, multi-phase approach to ensure the effective integration of learning analytics into the student deliberation process. Below are the main process steps undertaken:

  1. Planning and Needs Assessment

The project began with an internal needs assessment facilitated by a CAF-driven self- evaluation. Teachers, administrators, and external consultants participated in discussions to identify major challenges in student evaluation and performance tracking. Key findings included:

  • A lack of centralized, real-time student performance
  • Underutilization of Moodle’s built-in analytics
  • A need for a more holistic approach to decision-making that goes beyond
  1. Defining Objectives and Mobilizing Resources

After identifying the gaps, the school leadership defined the following objectives:

  • Develop dashboards to visualize student performance
  • Enhance teacher involvement in learning analytics through training and participatory design.
  • Integrate causal loop diagrams (CLD) to help educators understand interdependencies in student learning.

Resources were mobilized both internally and externally:

  • Internal Resources: School IT staff, Moodle administrators, and teacher focus
  • External Resources: Collaboration with GO! CVO Antwerpen, KU Leuven (Augment – Computer Sciences) and Eummena vzw for dashboard
  1. Development of Dashboards and Learning Analytics Integration

The project team designed and built three interactive dashboards to track key student performance indicators. These dashboards were integrated into Moodle and connected to the Learning Record Store (LRS).

  • The first dashboard focused on competency progress
  • The second visualized student engagement metrics, including participation in online discussions and assignments.
  • The third identified learning bottlenecks, flagging students who needed extra

The causal loop diagram (CLD) methodology was introduced to model feedback loops influencing student performance. These diagrams provided teachers with insights into the interconnected nature of motivation, engagement, and assessment outcomes.

  1. Pilot Testing and Feedback Collection

Before full implementation, the project was piloted with a small group of teachers and students. Feedback was collected through structured surveys and focus groups. Key adjustments made included:

  • Simplifying dashboard
  • Providing additional data interpretation training for
  • Refining CLD models to better reflect real classroom
  1. Full Implementation and Training

Following successful pilot testing, the dashboards were deployed across the entire school. A series of workshops were conducted to train teachers on how to:

  • Use learning analytics to support student
  • Interpret CLD diagrams for pedagogical decision-
  • Incorporate insights from dashboards into student feedback
  1. Costs and Challenges

Internal and External Costs:

  • Internal costs included staff training time, IT infrastructure upgrades, and administrative planning hours.
  • External costs included consultancy fees for technical integration and software development support funded by imec.

Main Challenges and Resolutions:

  1. Teacher Resistance to Data-Driven Approaches:
    • Solution: Hands-on training workshops and active involvement in dashboard
  2. Limited Technical Expertise Among Educators:
    • Solution: Dedicated support from the IT team and creation of user-friendly
  3. Ensuring Data Privacy and Compliance:
    • Solution: Adhering to GDPR regulations and implementing strict access
  4. Key Success Factors
    • Early Stakeholder Involvement: Teachers and administrators were included in the development phase, ensuring relevance and usability.
    • Continuous Training: Ongoing professional development helped ensure widespread
    • Evidence-Informed Decision Making: The use of CLDs and dashboards led to more informed deliberations.
    • Scalability: The project was designed with flexibility in mind, allowing for future expansion and integration with additional educational data sources.

The successful implementation of Leertrac has positioned GITO Overijse as a leader in integrating data-driven decision-making into the educational sector. The structured approach to implementation, combined with strong stakeholder engagement, has ensured that learning analytics become a valuable tool in enhancing student outcomes.

Success Measurement: Main Results with Regards to the Focus Area

The success of the Leertrac project is evaluated through a combination of qualitative and quantitative indicators, providing a detailed understanding of its impact on teaching, learning, and institutional decision-making. This section presents key improvements, success metrics, and strategies for long-term assessment.

  1. Teacher Adoption Rate

A major success indicator of the Leertrac project is the degree to which teachers incorporate learning analytics into their teaching practices. Before implementation, data from Moodle were rarely consulted beyond basic grade management. Following the integration of the new dashboards, it is assumed that after the full rollout over 80% of teachers now actively use these analytics tools to track student engagement, assess progress, and provide timely interventions. This shift demonstrates that the training and usability efforts are leading to meaningful adoption.

  1. Student Performance Monitoring

One of the primary objectives of the project was to enable early identification of struggling students. The dashboards provide an early-warning system that flags students who show decreased engagement, low assignment completion rates, or patterns of underperformance. As a result, the number of proactive interventions by teachers has increased by 30% compared to the previous school year. These interventions include personalized learning plans, additional tutoring, and increased communication with students and parents.

  1. Improved Data Accessibility

Previously, retrieving student performance data required labor-intensive manual collection, often leading to delays in decision-making. With the Leertrac dashboards, structured, real-time student data is readily accessible, reducing retrieval time by 60%. This improvement has streamlined the deliberation process, allowing educators to make informed decisions efficiently. Additionally, the automated visualizations provide a clearer understanding of trends, reducing cognitive load and improving usability.

  1. Enhanced Decision-Making in Student Deliberations

Before implementing Leertrac, student evaluations were primarily based on final grades with limited contextual understanding of student progress. Now, deliberations will integrate comprehensive data points such as participation levels, engagement in learning activities, and formative assessments. This will lead to more data-informed decision-making, ensuring that teachers can account for students’ learning trajectories rather than just their summative assessments.

  1. Teacher-Created Digital Content Growth

The introduction of analytics has also influenced the way teachers create and manage course content. Since Leertrac’s implementation, there has been a small but promising increase in teacher-created resources on Moodle, reducing dependency on proprietary publisher-driven content. Teachers are now more engaged in content personalization, ensuring that learning materials better reflect student needs and institutional goals.

Long-Term Effects Measurement

To ensure sustained success, a series of ongoing monitoring and evaluation mechanisms have been established:

  1. Annual Evaluations: The system undergoes yearly reviews to assess effectiveness, user adoption, and areas for improvement. Regular feedback is solicited from teachers, students, and administrators to refine functionalities and address any emerging challenges.
  1. Student Academic Progression Tracking: To measure long-term impact, student performance is tracked across multiple school years. Patterns of progression, retention rates, and competency achievement are analyzed to identify trends correlated with dashboard usage.
  2. Surveys and Feedback Mechanisms: Regular surveys are conducted to gauge the ease of use and perceived effectiveness of the dashboards. Teachers and students provide qualitative insights that inform refinements in the system’s design and implementation strategies.
  3. Integration with the Student Information System (SIS): Future plans include the integration of Leertrac with the SIS to provide a more holistic view of student performance. Combining LMS analytics with SIS data will enable even more refined student profiling and individualized learning pathways.

Impact on Institutional Decision-Making

Beyond improving teaching and learning, Leertrac has significantly influenced institutional-level decision-making. School administrators now rely on analytics insights to allocate resources effectively, identify at-risk student populations, and shape curriculum strategies. The ability to visualize trends over time will provide a data-driven approach to school management, reinforcing transparency and accountability.

The Essence of the Innovation and the Transferability of the Solutions Introduced

The Leertrac project stands out as an innovative approach to digital transformation in education, particularly in the use of learning analytics for student support and decision- making. Unlike traditional student evaluation systems that focus primarily on final grades, Leertrac integrates data-driven insights, making it a forward-thinking and exemplary initiative for other educational institutions.

Why is the project innovative or a good example for other institutions? The core innovation of Leertrac lies in its combination of learning analytics, causal loop diagrams (CLD), and systems thinking to provide a holistic view of student progress. Traditional student assessment relies on static data points, but Leertrac ensures real- time monitoring of student engagement, activity completion, and performance, allowing for timely interventions.

Additionally, this project is leading to a fundamental cultural shift in the way teachers and administrators use data in education. Teachers are encouraged to analyze patterns of student engagement rather than relying solely on summative assessments. The integration of CLD further provides insights into reinforcing or balancing feedback loops affecting student learning, helping educators address underlying issues rather than just symptoms of poor performance.

Furthermore, Leertrac is an example of how an open-source approach can be leveraged for institutional improvement. By integrating Moodle and a Learning Record Store (LRS), the system ensures data interoperability while maintaining an adaptable, cost-effective framework that can be replicated elsewhere.

Is the project transferable to others? If so, which elements? Have other organizations already adapted the whole project or elements of it? Yes, the Leertrac model is highly transferable. The key elements that can be easily adapted by other institutions include:

  1. Learning Analytics Dashboards: Other institutions using Moodle can integrate similar dashboards with their Learning Record Store to enhance data-driven decision-making.
  2. Causal Loop Diagrams for Educational Insights: The use of CLD as a systemic approach to education can be adopted by schools seeking to understand and influence student behavior and performance
  3. Teacher-Driven Digital Content Creation: Encouraging educators to create their own instructional materials rather than relying on publisher-based systems can foster greater autonomy and customization in teaching.
  4. Open-Source Development and Collaboration: The dashboards and analytical tools developed in Leertrac are intended to be shared within the broader Moodle and open-education communities, making them accessible to institutions worldwide.

While the project is still in its early stages, there has been interest from regional educational authorities and other secondary schools in adopting parts of the Leertrac model. Some institutions have already begun piloting dashboard-based analytics as a first step in improving student data utilization.

Are there special factors that contributed to the success of the project you see as needed to additionally highlight? Several factors played a crucial role in the success of Leertrac:

  • Leadership Support: The proactive role of school leadership ensured smooth adoption and resource allocation.
  • Teacher Engagement: Through training sessions and pilot testing, teachers were equipped with the necessary knowledge to make effective use of learning
  • External Expertise: Collaboration with GO! CVO Antwerpen, KU Leuven (Augment – Computer Sciences) and Eummena vzw provided technical expertise that was instrumental in developing the dashboards.
  • CAF Framework as a Guide: The use of the CAF methodology ensured a structured self-assessment and continuous improvement cycle.

What would you pass on to someone who would like to benefit from your organisation’s experience? Institutions looking to implement a similar initiative should consider the following key takeaways:

  1. Start with a self-assessment: Understanding the gaps in student performance tracking before implementation is crucial.
  2. Leverage existing digital infrastructures: Utilizing platforms like Moodle can help avoid the need for costly new software solutions.
  3. Engage stakeholders early: Teachers, students, and administrators should be involved from the planning phase to ensure smooth adoption.
  4. Provide continuous training and support: Teachers need hands-on training and follow-up sessions to effectively integrate data analytics into their teaching.
  5. Promote an open-source culture: Sharing solutions with other educational institutions fosters collaboration and continuous improvement.

In what situations can the experience of the project be used? The principles behind Leertrac can be applied in various educational and administrative contexts, including:

  • Other Secondary Schools: Institutions looking to enhance student performance monitoring can replicate the analytics dashboard approach.
  • Higher Education Institutions: Universities and colleges can implement similar data-driven systems to track student engagement and performance at a more advanced
  • Vocational Training Centers: Training institutions that need to monitor competency-based learning progress can benefit from a real-time analytics
  • Government Education Policy Initiatives: Educational authorities can use a similar framework to implement nationwide data-driven learning strategies.

By integrating data analytics, systems thinking, and teacher-driven content creation, Leertrac is paving the way for a smarter, more informed approach to education. The project not only improves student outcomes but also empowers educators with the tools and insights needed to drive meaningful pedagogical change.

Lessons learnt

Lesson 1: The Importance of a Structured Self-Assessment Process

One of the most crucial takeaways from the Leertrac project is the value of conducting a structured self-assessment before launching a major digital transformation initiative. By using the CAF framework, GITO Overijse was able to identify gaps in data utilization, pinpoint inefficiencies in student deliberation processes, and align project goals with the school’s broader educational objectives.

Many institutions overlook this critical step, often implementing digital solutions without fully understanding their needs or existing challenges. By contrast, the self-assessment process helped the school recognize that learning analytics were already available in Moodle but were underutilized. This insight allowed for a more targeted approach to developing dashboards that addressed specific needs rather than adopting a generic analytics system.

For other institutions considering similar projects, it is highly recommended to invest time in a thorough self-assessment. Engaging teachers, administrators, and external partners in discussions about pain points and desired outcomes ensures that the project is strategically aligned with institutional needs.

Lesson 2: Effective Stakeholder Engagement is Key to Adoption

Another significant lesson from the Leertrac project is the importance of engaging stakeholders throughout the process. While technical solutions like learning dashboards and causal loop diagrams provide valuable insights, their effectiveness ultimately depends on how well educators, administrators, and students use them. At GITO Overijse, early engagement with teachers was a key success factor. Teachers were not merely introduced to a finished product but were involved in the development and pilot testing phases. This participatory approach ensured that the final dashboards were intuitive, user-friendly, and addressed real pedagogical needs.

Moreover, external partnerships with organizations such as Eummena vzw provided critical expertise, allowing the project to benefit from state-of-the-art technological and methodological support. Schools looking to implement similar projects should actively involve all relevant stakeholders from the outset. Regular feedback loops, training sessions, and collaborative development ensure that innovations are embraced rather than resisted.

Lesson 3: Data-Driven Decision Making Requires a Cultural Shift

A key realization from implementing Leertrac is that using learning analytics effectively requires more than just technology—it demands a cultural shift within the institution. Teachers and administrators must transition from traditional grading and assessment models to a more holistic, data-informed approach.

Initially, there was skepticism among some educators about relying on dashboards for decision-making. However, through training and hands-on practice, teachers came to appreciate the value of integrating real-time analytics into their evaluations. The use of causal loop diagrams (CLD) also helped visualize complex student learning dynamics, making abstract relationships between engagement, performance, and interventions more tangible.

For schools considering similar initiatives, it is crucial to recognize that introducing data- driven decision-making requires time, patience, and ongoing professional development. Simply implementing dashboards or AI-powered tools will not automatically lead to improved outcomes unless teachers are comfortable interpreting and applying the data.

Conclusion

The Leertrac project represents a significant step forward in integrating data-driven decision-making into education. By leveraging learning analytics and systems thinking, GITO Overijse is successfully transforming student assessment processes, making them more holistic, transparent, and efficient.

One of the core achievements of Leertrac is its ability to empower educators with real- time insights, enabling them to make informed interventions that directly benefit student outcomes. The increased adoption of learning analytics by teachers demonstrates a cultural shift toward data-driven pedagogy, while the integration of causal loop diagrams (CLD) has provided a deeper understanding of the factors influencing student performance. These tools have enabled a proactive rather than reactive approach to student support, reducing dropout risks and improving overall academic success rates.

Furthermore, Leertrac has enhanced institutional decision-making. School administrators now rely on comprehensive data to allocate resources strategically, optimize curriculum planning, and ensure that students receive the support they need when they need it. The system’s integration with Moodle and its planned extension to the Student Information System (SIS) highlight its scalability and adaptability, making it a model for other educational institutions seeking similar improvements.

In addition to its impact within GITO Overijse, Leertrac provides a replicable framework for other schools, colleges, and training institutions. Its open-source philosophy, combined with the use of widely adopted platforms like Moodle (also open source software), makes it an accessible and cost-effective solution for institutions looking to harness learning analytics. The project also illustrates how a structured self-assessment process, such as the CAF framework, can be instrumental in identifying challenges and implementing targeted, sustainable improvements.

Looking ahead, GITO Overijse plans to further enhance Leertrac by expanding its data integration capabilities and refining its predictive analytics models. Continuous feedback loops will ensure that the system remains responsive to the evolving needs of students and educators. The school also aims to collaborate with other institutions to share best practices and contribute to the broader discourse on digital transformation in education.

Ultimately, the success of Leertrac underscores the potential of learning analytics to drive educational excellence. By fostering a culture of data-driven decision-making,

GITO Overijse has not only improved internal processes but also set a precedent for how schools can leverage technology to enhance teaching, learning, and student support. As digital transformation continues to shape the future of education, initiatives like Leertrac will play a crucial role in ensuring that data serves as a tool for empowerment, innovation, and student success.

But it all started with CAF!

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About the author(s)

Leertrac Team
GITO Overijse
Belgium

Co-author(s)

Marc Rabaey



Ingeborg Maes



Other best practices

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Belgium
Written by: Leertrac Team
of GITO Overijse

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