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EXP: Readily Available Learning Experiences: Turning the Entire Web into Progressive Examples to Bridge Conceptual Knowledge Gaps for Novice Web Developers: 1735977

Principal Investigator: Haoqi Zhang
CoPrincipal Investigator(s): Eleanor O’Rourke
Organization: Northwestern University

Abstract:
Computing is changing our world, and there is an urgent need to train large numbers of professional developers to meet the demands of our nation’s economy. While it is relatively easy to learn basic computing concepts, acquiring the conceptual knowledge and problem-solving skills of professional developers is challenging. Learners currently lack the materials needed to support their progression from writing functional code to writing production-quality software. To address this gap, this project will develop new technologies that transform the entire web of professional examples into opportunities for authentic learning. Professional web applications provide rich details missing from training examples, and offer opportunities for learners to think in the modes of the discipline. By making professional websites available as a learning resource, this project aims to improve learners’ conceptual understanding and better prepare them for programming challenges in professional work.

Novice developers lack the expert knowledge and self-directed skills required to (1) build conceptual models of professional examples; (2) implement professional features; and (3) apply professional concepts to solve diverse problems. This project aims to help learners overcome these challenges with Readily Available Learning Experiences (RALE), a theoretical model and platform that supports self-directed learning by: (1) scaffolding sensemaking to help learners build conceptual models of professional website features; (2) scaffolding process management to help learners implement concepts from professional examples; and (3) scaffolding reflection and articulation to support learning to apply concepts across diverse problems. To realize RALE in software, the researchers will develop computational techniques for implementing mixed-initiative scaffolds that leverage learner-created artifacts in conjunction with automated methods to support learning. This project will study the effectiveness of the RALE model using a design-based research process and generate empirically-validated principles to advance our understanding of authentic learning environments that provide a window into professional work.

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