Science Practices Innovation Notebook (SPIN)

Author:

Peters-Burton Erin E.1ORCID,Cleary Timothy2,Rich Peter J.3ORCID,Kitsantas Anastasia1

Affiliation:

1. George Mason University, USA

2. Rutgers University, USA

3. Brigham Young University, USA

Abstract

This chapter describes the development, characteristics, and applicability of a web-based interactive notebook, the Science Practices Innovation Notebook (SPIN), for use by high school students and teachers during science investigations. SPIN integrates data practices, computational thinking (CT), and self-regulated learning (SRL) principles to support high school students as they engage in science investigations. SPIN uses CT tactics and underlying SRL principles to optimize student progression through five core data practices. SPIN also incorporates loop mechanisms that foster communication, prompting, and feedback among individual students, their teachers, and peers. SPIN gives teachers data about student progress, communication, and learning analytics that assists in providing high quality feedback to students. This chapter will describe how the theoretical underpinnings of SPIN reinforce each other, how collaborative interactions between teachers and researchers led to SPIN development and design, and how SPIN implements support for student data practices in an online setting.

Publisher

IGI Global

Reference35 articles.

1. Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018)

2. Azevedo, R., Witherspoon, A., Graesser, A., McNamara, D., Chauncey, A., Siler, E., . . . Lintean, M. (2009). MetaTutor: Analyzing self-regulated learning in a tutoring system for biology. In Artificial intelligence in education (pp. 635-637). IOS Press.

3. BanduraA. (1997). Self-efficacy: The exercise of control. Macmillan.

4. A Technology-Enhanced Intervention for Self-Regulated Learning in Science

5. Effects of mobile-app learning diaries vs online training on specific self-regulated learning components

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