Enhancing learning by Open Learner Model (OLM) driven data design

Abstract

There is a huge and growing amount of data that is already captured in the many, diverse digital tools that support learning. Additionally, learning data is often inaccessible to teachers or served in a manner that fails to support or inform their teaching and design practice. We need systematic, learner-centred ways for teachers to design learning data that supports them. Drawing on decades of Artificial Intelligence in Education (AIED) research, we show how to make use of important AIED concepts: (1) learner models ; (2) Open Learner Models (OLMs) ; (3) scrutability and (4) Ontologies . We show how these concepts can be used in the design of OLMs, interfaces that enable a learner to see and interact with an externalised representation of their learning progress. We extend this important work by demonstrating how OLMs can also drive a learner-centred design process of learning data. We draw on the work of Biggs on constructive alignment (Biggs, 1996, 1999, 2011), which has been so influential in education. Like Biggs, we propose a way for teachers to design the learning data in their subjects and we illustrate the approach with case studies. We show how teachers can use this approach today, essentially integrating the design of learning data along with the learning design for their subjects. We outline a research agenda for designing the collection of richer learning data. There are three core contributions of this paper. First, we present the terms OLM, learner model, scrutability and ontologies, as thinking tools for systematic design of learning data. Second, we show how to integrate this into the design and refinement of a subject. Finally, we present a research agenda for making this process both easier and more powerful. • We introduce AIED concepts, Open Learner Models (OLMs), Learner Models, Ontologies and scrutability . • These are valuable tools for teachers to think about and discuss the design of learning data. • Students can use them to monitor, reflect and plan their learning, supporting self-regulated learning. • We illustrate this OLM-driven learning data design approach in two case studies. • We describe the barriers to wide scale, easy adoption of OLM-driven data design and a research agenda to overcome them.