Abstract
Abstract A network modelling approach to educational mapping leads to a scalable computational model that supports adaptive learning, intelligent tutors, intelligent teaching assistants, and data-driven continuous improvement. Current educational mapping processes are generally applied at a level of resolution that is too coarse to support adaptive learning and learning analytics systems at scale. This paper proposes a network modelling approach to structure extremely fine-grained statements of learning ability called Micro-outcomes, and a method to design sensors for inferring a learner’s knowledge state. These sensors take the form of high-resolution assessments and trackers that collect digital analytics. The sensors are linked to Micro-outcomes as part of the network model, enabling inference and pathway analysis. One example demonstrates the modelling approach applied to two community college subjects in College Algebra and Introductory Accounting. Application examples showcase how this modelling approach provides the design foundation for an intelligent tutoring system and intelligent teaching assistant system deployed at Arapahoe Community College and Quinsigamond Community College. A second example demonstrates the modelling approach deployed in an undergraduate aerospace engineering subject at the Massachusetts Institute of Technology to support course planning and teaching improvement.
Highlights
Maps for education are numerous and diverse at many levels of scale
We introduce a method to architect and design a network model using our high-granularity Micro-outcomes together with a sensor layer for inferring a learner’s state using high-granularity assessments and digital analytics
We introduce the approach of a high-granularity assessment and/or digital tracking analytics acting as a sensor, and show how these measurements link to the network model
Summary
Maps for education are numerous and diverse at many levels of scale. To give examples: there are degree maps that showcase paths through different majors (Aleven, McLaren, & Koedinger 2010), curriculum maps that trace subject sequences through a programme’s offerings (Arafeh 2016), concept maps that show related topics for learners (Fiorella & Mayer 2018), and outcomes maps that support accreditation (Willcox & Huang 2017) and learning path generation (Seering, Willcox, & Huang 2015; Miller, Willcox, & Huang 2016; Yang, Li, & Lau 2017). Use cases of the technology map include guiding technological change, exploration of design directions for inventors (Alstott et al 2017), identifying design innovation directions in the technology space (Luo, Yan, & Wood 2017) and visualising and analysing the expansion trajectories of the design knowledge base of a given technology domain (Song et al 2019). We introduce a method to architect and design a network model using our high-granularity Micro-outcomes together with a sensor layer for inferring a learner’s state using high-granularity assessments and digital analytics.
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