DISSECT: integrating computational thinking in the traditional K-12 curricula through collaborative teaching (abstract only)

  • Authors:

  • Sarah Hug;Josh Sandry;Ryan Vordermann;Enrico Pontelli;Ben Wright

  • Affiliations:

  • University of Colorado, Boulder, Boulder, CO, USA;New Mexico State University, Las Cruces, NM, USA;New Mexico State University, Las Cruces, NM, USA;New Mexico State University, Las Cruces, NM, USA;New Mexico State University, Las Cruces, NM, USA

  • Venue:
  • Proceeding of the 44th ACM technical symposium on Computer science education
  • Year:
  • 2013

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Abstract

The goal of the DISSECT program is to integrate computational thinking lessons into general education K-12 classrooms via graduate student/teacher partnerships. The idea of combining the teaching of CT with other disciplines is not new and it has taken shape in a variety of recent efforts in the formal education of youth. What is promising and innovative is the approach as it is implemented in K12 DISSECT classrooms. Through a collaborative teaching partnership, teachers gain a new perspective regarding computer science, and in cooperation with graduate students well-versed in computer science concepts, develop lessons and course modules that serve two purposes: 1.) Address K-12 content standards in core disciplines (e.g., language arts, life science) and 2.) Introduce CT concepts, such as abstraction, algorithms, data analysis and modeling. This poster describes ways computational thinking (CT) is taught in general K-12 classrooms in New Mexico through cooperative teaching. Along with their potential to stimulate interest in computing, these pilot modules were viewed by K12 teachers as enhancing disciplinary course content that teachers are charged with teaching (e.g., middle school science, language arts), deemed vital for sustainability in K12 schools by participating teachers. Preliminary data indicate graduate student and teacher satisfaction with cooperative teaching of CT. Next steps for research will involve student level data collection and analysis.