The idea for this innovation emerged while developing a competency-based personalized learning model in the Forvardas school in Lithuania. In such environments students move through the curriculum at different speeds and demonstrate competence when ready, while traditional grading provides only occasional snapshots and reveals little about the learning process.
After more than ten years of practice it became clear that schools lack systematic data about how learning unfolds over time. Teachers can see results but rarely the learning process that leads to them, and learning stagnation often becomes visible only after it has already affected outcomes. This challenge is especially evident in subjects such as mathematics, where students may perform procedures correctly without developing deep conceptual understanding.
This led to a broader insight: assessment systems define the informational field of schools. The data generated by assessment determine what teachers can observe and what pedagogical decisions they can make.
The Intraindividual Progression Metric (IPM) was developed to address this gap. It is a curriculum-based learning analytics model that makes learning progression visible as a trajectory of competency level, pace and stability. By generating structured progression data, the model helps teachers understand learning processes, support personalized pathways and make earlier pedagogical decisions. These data can also support AI-assisted teaching.
In practice, the model is implemented through a competency-based personalized learning system. The curriculum is structured into competency matrices that map learning progression across topics and levels of mastery, making the learning pathway visible.
Students begin with a diagnostic assessment that identifies their current level of knowledge. Based on this, they start learning from the appropriate point in the competency matrix. Students work through topics sequentially and progress only after demonstrating mastery.
Each topic is assigned progression points based on its complexity and expected learning effort. The total number of points is aligned with the time allocated to the subject in the national curriculum. As students complete topics, they accumulate points that represent their learning progression.
This creates continuous learning data that allows teachers to observe learning progression over time. Instead of seeing only occasional grades, teachers can monitor competence level, learning pace and progression stability.
These data are used in regular mentoring conversations with students to reflect on learning progress, set goals and plan next steps. The model therefore functions as a school-level learning analytics system that makes the learning process visible and supports data-informed pedagogical decisions.
The model has primarily been developed and implemented in the Forvardas school in Lithuania, where competency-based personalized learning has been practiced since 2016. Over time the school has refined the structure of competency matrices, progression points and the Intraindividual Progression Metric (IPM) as a system for monitoring learning progression.
The innovation has mainly spread through school practice, teacher collaboration and ongoing development of the learning model. Teachers use progression data in mentoring conversations with students and in planning personalized learning pathways.
The development of the model has also been documented through academic research. The Intraindividual Progression Metric is currently being examined as part of a master's thesis at Vilnius University.
At the same time similar competency-based and personalized learning models are increasingly implemented in other European education systems, including schools in Germany and Switzerland as well as recent curriculum reforms in Austria. These developments indicate that many schools face the same challenge: how to monitor learning progression when students move through the curriculum at different speeds.
Although the IPM model has so far been implemented primarily in one school context, its design is based on national curricula and competency structures, which makes it transferable to other school contexts.
The innovation has evolved gradually through practical implementation in the school environment. The first versions of the model focused mainly on competency matrices and personalized learning pathways, allowing students to progress through the curriculum at their own pace.
Over time it became clear that personalized learning also requires better ways to observe and interpret learning progression. Early versions of the system relied mainly on accumulated progression points and teacher observations. However, this did not provide a systematic way to analyze learning progression over time.
This led to the development of the Intraindividual Progression Metric (IPM), introducing a structured way to interpret learning progression through three indicators: competency level, learning pace and progression stability. The model therefore moved from a simple tracking system to a more comprehensive learning analytics approach.
The system has also been gradually adapted to align with national curriculum structures and different subject areas, including mathematics and language learning. This iterative development continues through ongoing conceptualization and evaluation in academic research at Vilnius University.
Schools interested in trying the model can start by structuring the curriculum into clear learning progressions. The curriculum is organized into competency matrices that map how knowledge and skills develop across topics and levels.
The next step is to divide the curriculum into smaller learning units or topics and assign progression points based on their complexity and expected learning effort. The total number of points can be linked to the time allocated to the subject in the curriculum.
Students begin with a diagnostic assessment that identifies their current level of knowledge. Based on this, they start learning from the appropriate point in the progression matrix and move forward sequentially as they demonstrate mastery.
As students complete topics, they accumulate progression points. This creates continuous data that allows teachers to observe learning progression over time rather than relying only on occasional grades.
Teachers can then use this data in mentoring conversations with students to reflect on learning progress, set goals and plan the next learning steps. Even simple spreadsheets can be used initially to collect and visualize progression data before more advanced digital tools are developed. Because the model is based on curriculum structures rather than specific technologies, it can be adapted in different school contexts.
