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Joseph Krajcik

Researcher at Michigan State University

Publications -  291
Citations -  26871

Joseph Krajcik is an academic researcher from Michigan State University. The author has contributed to research in topics: Science education & Curriculum. The author has an hindex of 74, co-authored 284 publications receiving 24808 citations. Previous affiliations of Joseph Krajcik include University of Maryland, College Park & University of Iowa.

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Journal ArticleDOI

Motivating Project-Based Learning: Sustaining the Doing, Supporting the Learning

TL;DR: Project-based learning as discussed by the authors is a comprehensive approach to classroom teaching and learning that is designed to engage students in investigation of authentic problems, and it has the potential to help people learn.
Book ChapterDOI

Nature, Sources, and Development of Pedagogical Content Knowledge for Science Teaching

TL;DR: The concept of pedagogical content knowledge as mentioned in this paper is used to describe the transformation of several types of knowledge for teaching (including subject matter knowledge), and as such it represents a unique domain of teacher knowledge.
Journal ArticleDOI

Designing Educative Curriculum Materials to Promote Teacher Learning

TL;DR: In this paper, the authors present a set of design heuristics for K-12 curriculum materials to promote teacher learning in addition to student learning and explore challenges in the design of these materials, such as the tension between providing guidance and choice.
Journal ArticleDOI

A Scaffolding Design Framework for Software to Support Science Inquiry

TL;DR: A scaffolding design framework addressing scaffolded software tools for science inquiry that synthesizes the work of prior design efforts, theoretical arguments, and empirical work in a set of guidelines that are organized around science inquiry practices and the challenges learners face in those practices.
Journal ArticleDOI

Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners

TL;DR: In this paper, the authors present theoretical and empirical motivation for a learning progression for scientific modeling that aims to make the practice accessible and meaningful for learners, including the elements of the practice (constructing, using, evaluating, and revising scientific models) and the metaknowledge that guides and motivates the practice.