Award Abstract # 1343227
EAGER: Foundations for Advancing Computational Thinking (FACT): Learning and Assessment through an Online Middle School Curriculum

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date: September 11, 2013
Latest Amendment Date: September 23, 2013
Award Number: 1343227
Award Instrument: Standard Grant
Program Manager: kamau bobb
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2013
End Date: September 30, 2015 (Estimated)
Total Intended Award Amount: $252,656.00
Total Awarded Amount to Date: $252,656.00
Funds Obligated to Date: FY 2013 = $252,656.00
History of Investigator:
  • Roy Pea (Principal Investigator)
    roypea@stanford.edu
  • Stephen Cooper (Co-Principal Investigator)
Recipient Sponsored Research Office: Stanford University
450 JANE STANFORD WAY
STANFORD
CA  US  94305-2004
(650)723-2300
Sponsor Congressional District: 16
Primary Place of Performance: Stanford University
CA  US  94305-2023
Primary Place of Performance
Congressional District:
16
Unique Entity Identifier (UEI): HJD6G4D6TJY5
Parent UEI:
NSF Program(s): Special Projects - CNS,
Discovery Research K-12,
Cyberlearn & Future Learn Tech
Primary Program Source: 01001314DB NSF RESEARCH & RELATED ACTIVIT
04001314DB NSF Education & Human Resource
Program Reference Code(s): 7439, 7578, 7916, 8045
Program Element Code(s): 171400, 764500, 802000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Stanford University proposes to develop and evaluate a proof-of-concept online middle school course (with a teacher version as well) that adapts concepts from the Exploring Computer Science (ECS) curriculum, specifically algorithmic thinking and introductory programming. The project will:
(1) Design and deploy an online six-week "Foundations for Advancing Computational Thinking" (FACT) curriculum on Stanford University's instance of the open edX online platform. Aimed at 12 to 15 year-old learners, the curriculum borrows from the ECS Programming and Problem Solving units. It will be driven by short video lessons with in-video and stand-alone quizzes for formative and summative assessments, and programming activities.
(2) Pilot FACT in a Bay Area public middle school class.
(3) Empirically examine the efficacy of: (a) the curriculum for the development of computational competencies, preparation for future learning of computing, and changes in student perceptions of CS via assessments designed for these purposes, and (b) student attitudes towards and experiences with online learning, including online course features such as in-video quizzes and discussion forums.
(4) Create and pilot an appropriately enhanced version of the curriculum for teachers to effectively prepare them to facilitate FACT/ECS use in their classrooms.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Cooper, S., Grover, S., and Simon, B. "Building a virtual community of practice for K-12 CS teachers" Communications of the ACM , v.57 , 2014 , p.39
Grover, S., Pea, R., Cooper, S. "Designing for Deeper Learning in a Blended Computer Science Course for Middle School Students." Computer Science Education , v.25 , 2015 , p.199-237

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project involved designing, creating, and iteratively refining ‘Foundations for Advancing Computational Thinking’ (FACT) as an online course on Stanford University’s OpenEdX online platform for blended in-class learning of introductory computing concepts in middle school. The goal was to empirically test and refine a course that teachers with minimal experience in computing could use in their classrooms. FACT drew on the rich body of prior research in computing education and the learning sciences for teaching introductory programming concepts. Unique features of FACT included—

  • Blending of individual and collaborative activities.
  • Making explicit the foundational ideas of algorithmic thinking through modeling of the problem solving process in computer science (CS) with worked examples.
  • Consciously using academic language to explain concepts in terms of the vocabulary of the domain of CS.
  • Embedding formative assessments throughout FACT.
  • Novel use of summative assessments of algorithmic concepts including those that assess for transfer or “preparation for future learning” (PFL).
  • Remedying misperceptions of computing as a discipline.

 

Empirical investigations examined the efficacy of FACT in a public middle school classroom setting (N=28; Mean age=12.3 years; 20 males, 8 females) and answered these research questions—

  1. What is the variation across learners in learning of algorithmic flow of control  (serial execution, looping constructs, and conditional logic) through FACT, and what factors influence learning outcomes?
  2. Does the curriculum promote an understanding of algorithmic concepts that goes deeper than tool-related syntax details as measured by PFL assessments?
  3. What is the change in the perception of the discipline of CS as a result of FACT?

Key results and findings of the empirical investigations are—

  • The effect size (Cohen’s d) in the pre-to-post gains in computational learning was roughly 2.4. Results from the online study were statistically comparable to the earlier study that used FACT in a face-to-face classroom setting.
  • Students found serial execution to be the easiest algorithmic concept, followed by conditionals and loops. This was perhaps because most of the questions on loops also involved variable manipulation—a concept that students found difficult to learn.
  • A comparative analysis on questions from the Israeli nationwide exam administered to 7th graders in Israel in 2012 revealed better performances by our students on one question.
  • Qualitative analysis of the final projects and interviews revealed that students showed higher levels of engagement and more evidence of understanding of algorithmic constructs than was indicated by their posttest performances.
  • On the PFL transfer test, there was evidence that students were able to understand algorithmic flow of control in snippets of code written in a text-based programming language.
  • Mathematics achievement was found to be a (positively correlated) predictor for performance in both pretest and posttest (even when controlling for the pretest).
  • Prior programming experience as measured by the pretest was found to positively predict performance on both posttest and PFL posttest.
  • Students’ perceptions of computing registered a significant shift from naïve “computer-centric” notions of computer scientists, to a more sophisticated understanding of CS as an engaging problem-solving discipline that has applications in many diverse fields.

 

The projec...

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