Learning engineering: A view on where the field is at, where it’s going, and the research needed.

Abstract

Although the idea of learning engineering dates back to the 1960s, there has been an explosion of interest in the area in the last decade. This interest has been driven by an expansion in the computational methods available both for scaled data analysis and for much faster experimentation and iteration on student learning experiences. This article describes the findings of a virtual convening brought together to discuss the potential of learning engineering and the key opportunities available for learning engineering over the next decades. We focus the many possibilities into ten key opportunities for the field, which in turn group into three broad areas of opportunity. We discuss the state of the current art in these ten opportunities and key points of leverage. In these cases, a relatively modest shift in the field's priorities and work may have an outsized impact.

Terracotta: A Tool for Conducting Experimental Research on Student Learning

“…As digital tools for education research emerge, a known risk is that they will marginalize the teacher and neglect important details of the educational implementation, instead focusing myopically on the inner workings of the tool itself. Proponents of research within digital learning platforms advocate for greater involvement of teachers in the research process (Baker et al, 2022;McCarthy, et al, 2022), as others have advocated more broadly (Joyce & Cartwright, 2019). This is also our goal with Terracotta.…”

“…Fortunately, things are starting to change. Enabled by the digital transformation of education, research tools for experimentally manipulating features of the student learning environment have begun to emerge (Baker et al, 2022;McCarthy, et al, 2022). As student learning activities and student data are increasingly online, the technical infrastructure for supporting education can be leveraged for research purposes -digital learning platforms can support student learning, and also can be equipped with tools to support experimental research on education.…”

Challenges of Knowledge Component Modeling: A Software Engineering Case Study

“…Such a feat seems practically impossible, as most classrooms have dozens or hundreds of students with different prior knowledge, interests, and affective states when the instruction is being delivered (Aleven et al, 2016). This problem is one of visibility and scale (Baker et al, 2022). It is difficult for an educator delivering the lecture to understand the learning gains obtained by an individual student through passive observation.…”

“…Contemporary learning theories coupled with digital technologies provide a scalable way to achieve visibility into individual students' learning gaps and gains (Koedinger et al, 2012;Ritter et al, 2019;Baker et al, 2022). Adaptive e-learning tools, such as intelligent tutoring systems (ITSs), interleave instruction with assessments to ensure a student achieves mastery of a unit (i.e., the expected learning gains) before moving on to the next one.…”