What is the shift from differentiation to adaptive teaching? Alex Quigley made the argument that differentiation is dead, and that the nail in the coffin came from the 2019 Department of Education’s Early Career Framework. It was here that we finally see that differentiation has lost its usefulness as a concept, despite being so ingrained as a core component of teaching. Instead, there is now a movement towards adaptive teaching. Adaptive teaching does not insist that teachers artificially create distinct tasks for different groups of pupils or set lower expectations for particular pupils. The key to adaptive instruction is to respond to students’ individual needs with scaffolding, targeted questioning and prompts.
This provides opportunities for all students to experience success- while maintaining high expectations. The foundational belief is that all pupils should have the freedom and inducement to meet high expectations.
As Quigley writes “Adaptive teaching, then, offers a language for existing practices that were previously tricky to explain: flexible groupings, reteaching content for a struggling pupil and generally being responsive to pupils’ learning. The term describes practices that have been around for some time. Nevertheless, there seems to be a consensus that adaptive teaching is a positive step forward for inclusive teaching and learning.”
This is practical and effective, it makes lessons easier to plan. As Andy Tharby and Shaun Allison write in Making Every Lesson Count “We believe that much of what is promoted as good differentiation practice is both unmanageable and counterproductive: it is not humanly possible to personalise planning for each and every child.” In my early career I expended a lot of effort creating three different learning objectives (or more) and three different versions of a task (or more). If there is an easy choice a student may go for the bare minimum, sometimes because they think that is what a teacher expects from them. When we have an ‘all, most, some’ objectives we sometimes lower expectations. We can meet individual needs without creating unnecessary workload, here are some thoughts based on the recommendations inspired by the Early Careers Framework and others:
- Investing in great formative assessment. Embedding this is best informed by the work of Dylan Wiliams.
- Intervene with individuals and small groups. Set a task and let pupils struggle while observing- waiting for pupils to ask, encouraging and prompting so that they hit the proximal zone of development- this can be more efficient and effective than planning different lessons within lessons for different groups of pupils. We can talk less to the whole class and listen more to individual pupils.
- Flexibly grouping pupils within a class to provide more tailored support can be effective, with care taken to monitor its impact on engagement and motivation, particularly for low-attaining pupils. All whilst maintaining high expectations for all, so that all pupils have the opportunity to meet expectations.
- Making use of well-designed resources, textbooks, and primary texts. We do not need to reinvent the wheel and look for three different versions of a resource. It is tempting to rewrite a resource, simplifying it and removing the nuance- this strips the layers of complexity. We can identify pupils who need new content further broken down, but all students deserve exposure to high-level content.
- Instead of reframing resources, reframe the questions to provide greater stretch when you observe students who could use more challenge. For more on probing questions, I’d suggest this one minute piece by Tom Sherrington and Sara Stafford for the Chartered College, which includes these prompts:
- Can you explain how you worked that out?’
- ‘Can you give an example?’
- ‘Is that always true?’
- ‘Does anyone disagree?’
- ‘Can you think of a case where this would be different?’
- ‘How does that example compare to this example?’
- ‘Which of those factors is the most important?’
The Game Changer- Adaptive Software
It would not be hyperbole to state that adaptive teaching software that can give specific, actionable, immediate feedback to students is a game-changer. This is real-time personalised assessment that enables students to track their progress and for teachers to adjust learning strategies. What has emerged to make both retrieval practice and responsive teaching more possible is the wave of adaptive learning AI to assess, customise, and consolidate. There are suites of learning platforms that use artificial intelligence and machine learning techniques to ‘adapt’ the learning path and pacing offered to individual students. The gap in understanding is much easier to broach if students are provided with the most efficient systems to build knowledge schemas. The pre-assessment is there to assess, sequence, and customise the scaffolding. These platforms can be self-paced, give rapid feedback, save teacher time and embed low-stakes formative assessment. These platforms can collect and analyze data from various sources, in some cases data can create a detailed learner profile, which includes information about the student’s current knowledge, skills development, support for diverse learners and progress.
As an added bonus, this is data-driven instruction. There are things that adaptive software capture that we may miss—for instance, some can parse the difference between performance data (pathways taken to work through material and what they understand) and engagement data (time on task and spent on each question). We can respond, support and motivate while wielding tools to better inform our interventions. It is important to understand how any utilised platform is working—there are systems that use advanced algorithms, decision trees, or are rule-based. Teachers often have a time-consuming frictional workload (ie. collecting and chasing down exercise books), automation of student feedback can increase scale and immediacy, while also providing a teacher with an overview of results. Data captured by adaptive learning software can be analysed to review the needs of individual students or groups of students. Teachers can then adapt instruction to those needs, and revise the curriculum accordingly. Similarly, students can use data about their skills and performance to adapt their learning practices.
Technology can help make inequity visible, integrated data tracking software can parse demographic differences, as it can be used to identify the progress of targeted subpopulations. This is a powerful way to identify trends and confront barriers for equity seeking groups and under-resourced students. Everylearner Everywhere notes that ‘Having access to real-time data is also an opportunity to understand the needs of your students, especially racially minoritized, poverty-impacted, and first-generation students’.
All of this is not to say that we need software to be responsive. There are things that only a teacher can see and respond to. “Socio-emotional needs” seem like an odd clinical way to frame it–simply put, teaching is a human profession. ‘One of the misconceptions that comes with the adaptive system is that the technology will replace instructors in schools. Although adaptive technology facilitates the students’ learning process, the successful implementation of adaptive learning still requires human planning, interactions, monitoring, and interventions’ (Cavanagh, Chen & Lahcen, 2020; pp 187).
Adaptive teaching does not depend on technology- it depends on knowing your students.
References:
Allison, S., Tharby, A. (2015) Making Every Lesson Count: Six Principles to Support Great Teaching. Wales, UK:Crown House Publishing.
Cavanagh, T., Chen, B., Lahcen, R.A.M. (2020) ‘Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective’, International Review of Research in Open and Distributed Learning 21(1) pp. 174-197
Education Endowment Foundation (EEF). (2019) ‘Early career framework’, UK Department for Education, January. Retrieved from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/978358/Early-Career_Framework_April_2021.pdf. Accessed April 18, 2023.
Holiday, T., O’Sullivan, P., Adams, S. (2020) ‘Adaptive courseware implementation guide’, Every learner everywhere. Retrieved from: https://www.everylearnereverywhere.org/wp-content/uploads/Adaptive-Courseware-Implementation-Guide-updated-links-by-CF-3-updated-links-included.pdf. Accessed April 18, 2023.
Quigley, A. (2023) ‘Differentiation is dead, long live adaptive teaching’, tes magazine, 21 February. Retrieved from: https://www.tes.com/magazine/teaching-learning/general/adaptive-teaching-vs-differentiation-inclusive-teaching. Accessed April 18, 2023.
Silicon Valley Innovation Center (2023) ‘Revolutionizing Education: The Power of Adaptive Learning Platforms’ 23 May 2023. Retrieved from: https://siliconvalley.center/blog/revolutionizing-education-the-power-of-adaptive-learning-platforms Accessed 15 January 2024.
Talbot, A. (2023) ‘Adaptive Teaching’ Huntington Research School 23 January 2023. Retrieved from: https://researchschool.org.uk/huntington/news/adaptive-teaching. Accessed February 14, 2024.