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Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in many disciplines, including computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, 1712231, 1927906, 2110156, 2111578), subgoal labels have been previously identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes) and an e-book has been created on the Runestone platform to enable students to complete practice problems using the subgoals. The initial implementation focused on Java, but within the past year, the development of subgoals for CS1 courses in Python have been created. Subsequently, course materials have been created as well. This workshop will introduce participants to the new materials (in Python) and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include worked examples and practice problems that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels. 

In our previous research we found that teaching novice programmers introductory programming in Java using subgoal labels led to deeper knowledge [2] and increased persistence for students potentially at risk of dropping out or failing their first undergraduate course in CS [3]. Given the increasing number of universities using Python in introductory programming classes, we have begun the process of defining the subgoals to use in Python-based introductory courses. We have repeated the Task Analysis by Problem Solving (TAPS) development process [1] that we used to create Java-based instructional materials. As with the Java-based instructional materials, we have created subgoals for both evaluating programs written in Python and writing programs in Python. The poster will present an overview of the TAPS development process for identifying the Python subgoals and the proposed experimental design for assessing the effectiveness of the instructional materials. 

Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in disciplines both in and out of computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, #1712231), subgoal labels have been identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes). This workshop will introduce participants to the materials and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include over 100 worked examples and practice problem pairs that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels.