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Developing resources and curriculum to introduce fundamental computer science skills to students in kindergarten through second grade.
Developing resources and curriculum to introduce fundamental computer science skills to students in kindergarten through second grade.
RCT: with the Treatment condition receiving the CAL Curriculum
USA (2 States in New England)
School Year: 2021-2023
Highlighted Technology: ScratchJr
Overview
Computer Science in K-2 is a recent project that focuses on integrating computer science concepts like coding and computational thinking into early childhood education. This project presents the results of a four-year project conducted in public schools in 2 states in the US during SY2021-2023. Initially, 40 schools were recruited, with 33 remaining through the study. Schools were split evenly between control and treatment groups through two random assignment sessions. The treatment group implemented the CAL curriculum in the first year, while the control group followed in the second year (delayed treatment).
Professional development (PD) for teachers was delivered by the DevTech research group in the first year, and by trained teachers in the second year. Baseline assessments using the Coding Stages Assessment (CSA) and TechCheck for computational thinking were conducted at the beginning of each school year. Curriculum implementation began in Winter/Spring 2022, followed by spring assessments using CSA and TechCheck.
PROJECT OBJECTIVES
1- Create a comprehensive, field-tested, high-quality integrated K-2 computer science (CS) curriculum along with a suite of teaching materials and implementation supports, all of which are free and publicly available
2- Achieve high-fidelity implementation in schools, resulting in statistically significant student learning outcomes
3- Enhance teacher pedagogical and content knowledge for curriculum implementation. Also, build the capacity of leaders, technology coordinators, and specialized coaches to replicate and sustain the work beyond the grant period.
Data COLLECTION
The validated assessments Coding Stages Assessment (CSA) & TechCheck were used to measure both teacher and student knowledge of coding in ScratchJr and students’ CT. These assessments were administered before and after the intervention for both the treatment and control groups. Independent research assistants conducted the one-on-one assessments via Zoom.
Creative coding projects (artifacts) were collected three times, and exercises to show what students know (SWYK) were administered at the end of the curriculum.
Teacher self-efficacy (TSE) in teaching CS was gauged using a pre- and post-training survey (13 items, 5-point Likert scale). To gain deeper insights, semi-structured focus groups (45 minutes) were conducted after training and implementation to explore teachers’ perceptions of the training and curriculum. Finally, lesson logs were used to track fidelity of implementation.
Highlights
Highlights
Student Performance and Growth
At baseline, there were no significant differences in mean coding scores between Treatment and Control conditions. However, significant improvement was observed in the Treatment condition at endline.
Within the Treatment condition, artifact-based assessments generally showed better student performance compared to Task-based assessments.
Girls and students with an Individualized Education Program (IEP) tended to score lower on Task-based assessments at endline but not on Artifact-based assessments.
Significant differences were observed in mean coding and computational thinking (CT) scores between Treatment and Control conditions at both baseline and endline, with improvements favoring the Treatment condition over time.
Coding scores growth showed significant differences between districts and student demographics (IEP, LEP, SES), impacting performance differently across groups.
Teacher Experience and Growth
Teachers in their second year (train the trainer models) rated their overall experience higher.
Significant improvements in coding skills were observed among teachers in the Treatment condition from baseline to endline.
PD training significantly impacted teachers’ coding skills development, with varying effect sizes across different PD models (Expert-led Model: d = 0.69, Peer-Led Model: d = 0.83, Peer-Led Model with extra time: d = 0.74).
Predictors of teachers’ coding skill improvement included years of experience, gender, race/ethnicity, intervention condition, and PD models.
Teacher Self-Efficacy (TSE) showed significant improvements across both Treatment and Control conditions from baseline to endline.
Significant predictors of TSE included years of experience, gender, race/ethnicity, intervention condition, grade level, and PD models.
What Teachers Say
What Teachers Say
“I was a little nervous this year coming into it, but so far I’ve been really, really impressed. They’ve given us the handheld directions of how to do everything. If you’re really not comfortable or confident they can walk you through it even more or go to all the office hours.”
T.1
“I’m nervous about the scheduling of the curriculum. My school just has a jam-packed schedule.”
T.2
“It was a really fun training and I love the hands-on piece of it. I got really into it and the training allowed me to figure out where I was strong and where I might need to focus a little bit more on.”
T.3
“All my kids, learn so differently. […] They were all able to learn. No one ever really felt frustrated or like a failure. Because we learned through this program like you do fail. You need to debug, you need to rethink, and you should go back. So, I really thought that all of my learners were able to complete this with success.”
“All my kids, learn so differently. […] They were all able to learn. No one ever really felt frustrated or like a failure. Because we learned through this program like you do fail. You need to debug, you need to rethink, and you should go back. So, I really thought that all of my learners were able to complete this with success.”
T.4
Want to learn more?
Want to learn more?
Interested in learning more about this project? I’d love to hear from you!
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