While there is no set definition, in general, adaptivity in learning environments refers to changing or adapting a learning context over time in response to learner actions and outcomes. The CISL project leverages the power of adaptivity with a UDL environment. What difference does this combination make? Adaptivity and UDL together work to make clear, data-based recommendations transparent for learners, putting ultimate control in the hands of learners themselves, in order to promote student learning, motivation, and self-agency.
The adaptive features in CISL will provide recommendations for supports and content that individualize learning with the ultimate goal of creating expert learners.
Adaptive tools build expert learners
Adaptivity is intelligence. Intelligence about a learner’s strengths and needs, informing recommendations that facilitate learning. In a UDL context, this intelligence extends further to inform and empower learners: strengths, needs, and recommendations are clearly transparent and in the learner's control in order to build expert learners.
So, how will CISL use adaptive learning solutions to help build expert learners? CISL tools will:
- Prompt and support learners in exploring their preferences
- Use information from learners’ prior actions and work products to suggest and deliver helpful supports and content
- Give learners agency over how and when adaptive supports and content appear
- Build self-awareness by explaining system recommendations and changes
A game-like word challenge gathers information about users and informs Clusive recommendations.
What adaptivity features are we working on now?
Adaptive dictionary and glossary. When Clusive users open a learning object, they can opt to do a game-like word challenge that helps assess current word knowledge and set goals. Inside a text, learners can look up words, view their previous word activity, and add any word or term to their personal Clusive Word Bank, a full list of words collected by a learner.
Text adaptivity. Learner goals, preferences, and activity help Clusive recommend a just-right challenge for each learner. Text versions currently include complexity-leveled texts and modernized views. What's next? Text versions may also include outline, main idea, summary, or annotated views, simplified texts, translations, and more. And as always, learners will know why a recommendation is made and can select other text versions.
Preference setting adaptivity. At periodic intervals, Clusive will suggest users try different accessibility preference settings based on a learner's individual strengths, needs, and goals. Clusive will ask learners for their reaction to the settings, too, to inform future recommendations.
Learning tools adaptivity. Clusive will occasionally prompt users to try learning features. A friendly message such as, “Try out this feature. We’d love your feedback!" will encourage learners to explore features that support learning progress and promote learner agency to make choices that work best for each individual. User feedback will also help inform future learning tool recommendations for each learner.