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A subgoal label is a brief bit of text (a label) that describes a step in some problem solving process. When these labels are provided to the novice it helps clarify the steps needed to achieve some outcome.
The example on the right shows subgoal labels applied to a small piece of code (in a different language than we use in CSP). It can seem simplistic to the coder with experience, but for the novice these labels can really help get them over initial feelings of not even knowing where to begin.
Besides helping inform best practices in computer science education, your participation may improve your students’ performance. In preliminary research in computing classes, students who learn using subgoal labels have been shown to:
Perform 10-15% better on problem solving tasks (this is the equivalent of a letter grade on a test)
Retain more knowledge over time
Because of more efficient learning, complete problem solving tasks in less time
Prior research across a variety of disciplines such as statistics, algebra and chemistry suggests that using subgoal labels improves students’ problem solving abilities by helping to break down problems into manageable parts.
By participating you can help to verify these initial results with a wide range of students.
There is NO different or additional teacher prep required - if you prepped for Unit 3 already, fantastic.
There is NO difference in the Unit 3 structure, lesson sequence, learning objectives, or projects
The only differences students will see are (1) how some material is presented to them through the App Lab programming environment (2) some assessment questions
You may be asked to provide feedback on how you perceive your students are doing with unit 3, and we may ask you additional demographic data about your class.
Code.org, in collaboration with the researchers, are conducting a scientific study about how effective subgoal labels are for students learning how to program.
For teachers who opt in to participate in the study, all will be assigned to a redesigned version of Unit 3 that includes improvements to instructions and sequencing.
A number of those teachers will be randomly selected to receive the subgoal "intervention". Others will still receive a redesigned version of the unit but without the intervention. This type of blind experiment is important for scientific rigor.
all students in all your classes will see the same thing, all your students see the same version of the study – your students won’t be split between different versions of the study.
Your students will be automatically routed to a "research" version of CSP Unit 3 which will have a different URL that might look something like https://studio.code.org/s/csp3-research-MXGHYT
Simply follow along with that version of unit 3, and that's it!
Data collection and protection
Code.org does not store student contact information on its servers. All student activity data collected by Code.org is anonymized with randomly generated student and teacher IDs and further stripped of any identifying information before sharing it for research purposes.
Results will never be tied back to any individual student, teacher or school.
Any data collected directly by the researchers will have no identifying information
The researchers will never have direct access to any student generated data – only aggregated data prepared and cleaned by Code.org. An example of this might be how many students per class get a specific question correct on the first try. While data might be aggregated and generalized based on class groupings, there will be no way to know the identity of a student who got the question right or wrong, nor the identity of the class they were part of, nor the identity of their teacher.
If you have any questions about the research study, subgoal labels, or your participation in the study, please contact Dr. Morrison or Dr. Margulieux:
Dr. Briana B. Morrison, Ph.D.
Assistant Professor
Computer Science Department
College of IS&T PKI 285B
University of Nebraska at Omaha bbmorrison@unomaha.edu
Dr. Lauren Margulieux, Ph.D.
Assistant Professor
Learning Technologies Division
Georgia State University lmargulieux@gsu.edu