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Learning Power is an Indicator of Student Disengagement for UPSTREAMING Targeted Interventions


Working through the pandemic we have identified patterns in the learning power profiles of students who are ‘at risk’ of disengaging. These profiles provide diagnostic information about the next best pedagogical action required. With permission, we can directly identify a school’s most disengaged students.​

These patterns in a student’s learning power profiles are often present before behavioural indicators. ​

We collect the anonymised raw data from Jearni’s Digital Platform at the same time as students and teachers use it for self-directed change in the classroom or home learning.


Cluster one represents the most actively engaged learners, those who have a clear sense of agency and confidence in their ability to learn new things.  These learners will, typically, present as motivated students with a keen sense of curiosity about their learning. They will be active participants in the learning environment and willingly engage in dialogue about both the content and the process of learning itself. They will be self-reflective and capable of working out for themselves when they need to adapt what they are doing. They will be effective communicators who can learn with and from others. They will have a well-developed schema that they are constantly adapting as they learn new things.  They will use this schema effectively to make links between previous and new learning.  When they find things difficult, they will draw from a number of different resources and individuals to help them work their way forward. They will readily engage with challenges and will use questioning as a way of trying out new ways of thinking.  


Cluster two represents the most disaffected and disengaged learners with the lowest levels of learning power.  These students will, typically, present as lacking confidence when faced with challenges that push them out of their comfort zone. They tend to rely on tried and tested ways of working, even when these are not productive strategies. They often lack resilience, preferring not to try rather than risk failing.  They are the active avoiders, unwilling to engage in the cognitive conflict necessary for learning to happen.  They will often present with a fixed mindset of I can’t before they even begin. These learners often need a lot of support in accessing learning as they quite often have gaps in their knowledge, that have built up over time, and lack the schema to support them in making the connections between different types of information.  Their learning is often compartmentalised.  In group activities they will often contribute little, usually looking to others to lead. A lack of collaboration means that they seek answers from others as opposed to engaging in dialogical learning relationships.  These students show little curiosity in the classroom and ask fewer questions. They often fail to see how the learning has relevance to them or the real world and struggle with abstract concepts. 


Cluster 3 represents those willing ‘workers’ in the classroom.  They often have a drive to please and work well with others. They are more emotionally invested in their learning than cluster 4 with greater strengths in the relational learning dimensions and a better developed sense of belonging. They feel a sense of affiliation and connectedness to their community and draw on this as they navigate challenge.  They are less reflexive than cluster one and tend to be much less independent in their learning.  They tend to comply rather than question and can often fail to make the links between domains of knowledge. They like clear direction and an established way of working rather than to follow their curiosity and see what happens, preferring to ’play it safe’. They tend to lack resilience when pushed out of their comfort zone and will sometimes give up if not supported in such circumstances. 


Cluster four represents those learners that quite often present as compliant and task engaged, though they are rarely emotionally invested in their learning.  They have moderate levels of learning power that enable them to engage in learning but quite often only superficially, thereby struggling to apply this learning out of context.   They can be self-limiting in so much as they place barriers on what they believe themselves capable of, typically lacking a growth mindset when facing challenge. They often require direction in their learning and lack the self-reflexivity required to work it out when they don’t have clear instructions or on hand support. Quite often this is the group of learners that require intervention in the run up to exams as their learning has been siloed and, though they can recall knowledge, they struggle to engage in the higher order thinking and questioning required for interpretation, application and evaluation.    At-risk students from this group are often overlooked as they may not present externalising behaviours, or the necessary ‘red-flags’ that identify them as such, until much later. 

Where the data came from ...

Our Networked Improvement Community collected base line learning power data from 513 secondary students in four schools and two Alternative Provision sites in England during 2019 and 2020. Teachers then used that data diagnostically to design interventions to address those students learning power needs. Meanwhile the researchers analysed the sample to see if it would be useful for UPSTREAMING.

55 students were identified by teachers as ‘being at risk’ whilst the remaining 458 were taken from whole year groups.

There were statistically significant differences in learning power dimensions between the ‘at risk students’ and the ‘general cohort’.

A cluster analysis of the whole cohort showed the presence of four distinct ‘archetypes’. These mapped onto the teacher identified at risk profiles and the narrative data collected through student and teacher interviews.

Our schools varied in the number of cluster 2 students, and with permission, these individuals can be identified and provided with learning power coaching conversations focused on developing self-leadership as learners.

Our focus is now on user-led testing of the face validity of this data.  Working closely with our partner schools we are working hard to capture the right data to build a robust and rigorous ‘super data set’ to explore the scalability of this process.  We hope to make it widely available through our Community Interest Company. If you are interested in joining our Networked Improvement Community we’d love to hear from you. 

To discuss these findings or find out more about our work please contact Prof. Ruth Crick (Ruth.Crick@jearni.co) or Claire Crichton-Allen  (CCa@jearni.co)

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