Action Research Topic: The Efficacy of Teaching Computer Science Fundamentals using Robotics and Problem-based Learning

 

Purpose of Study: During its formative years computer-science, specifically computer programming, was taught in isolation, with students learning code sans real-world or interdisciplinary concepts.  Most instruction comprised of students learning code rotely in a lab whilst sitting in front of a monitor. 

 

In more modern times, computer science instruction has begun broadening its instructional scope, with problem-based learning and supplemental design, such as robotics, being implemented to give the curriculum more context.  Collaborative learning has also been added to the fold to simulate more real-world scenarios. 

 

In 2020, the COVID pandemic disrupted this learning environment and many students were forced into learning the discipline through distance-learning.  Thus, the instruction of computer science reverted to the former of the two scenarios presented, with students learning computer programming in isolation (using online-based curricula), while some learn in the latter, in a collaborative, problem-based learning environment.

 

Research Question: The purpose of this study is to attain data that will shed some light on which of the two programming instructional methods listed above better prepares students to succeed on the district-issued STUDENT LEARNING OBJECTIVE examination.

 

Research Design and Methods:  In the Spring of 2021, middle school students in 6th grade will attend a INVESTIGATING CAREERS IN ROBOTICS AND COMPUTER SCIENCE class at Burnet Middle School in Austin, Texas.  Due to the COVID-19 pandemic, a portion of these students will learn remotely while the other learns in the classroom environment. 

 

Students who will be learning at home will be using district-issued Chromebooks to complete online curriculum using websites such as Code.Org, CodeHS, Code Combat and Tynker.  Students learning in the classroom will use coding in conjunction with problem-based learning, robotics, and collaborative learning. 

 

At the start of the semester, each of these students will be given the SLO (student-learning objective) EXAMINATION PRE-TEST to determine aptitude in computer science.  At the end of the course, each student will take the same Austin ISD administered SLO post-test.  Both group’s scores will be compared, along with any variables taken into account, to determine the efficacy of each of the two instructional methods.

 

Timeline:

January 2021: Pre-test administration of the district SLO examination to both groups of learners.

 

Late-January 2021: Students in both groups will begin learning computer science fundamentals, one through online-based curricula, the other through in-person learning.

 

Early-May 2021: Post-test administration of the district SLO examination to both groups of learners.

 

Mid-May 2021: Compile and analyze testing data.

 

Late May 2021: Dissemination of data results.