We are a group of students who are performing a research study to investigate how students learn and improve their performance at different aspects of Python programming. For the study, we are looking for students who are currently enrolled in CMSC 201 or who took CMSC 201 in the last semester or two and have not yet completed CMSC 202.
In exchange for your participation in the study, we will provide pizza for the participants, and each student will also be entered into a drawing for an 8G iPod Nano, in the color of your choice. One participant, of the 24 to 32 students that we recruit, will win the Nano. We are currently signing students up for the following three time slots:
- Wed 4/6, 7pm-10pm: free pizza will be available for registered participants at 6:45
- Fri 4/8, 10am-2pm: Show up *any time* between 10am and 2pm — whatever works for you! Pizza will be ordered around noon.
- Mon 4/11, 10:30am-8pm: Show up any time between 10:30am and 8pm! Pizza will be provided around noon and again around 6:30 if there are participants there to eat it!
The study will take place in ITE 240. Please sign up in advance if at all possible, and let us know what time you expect to arrive during the open sessions, so that we know how many students to expect and how much pizza to order!
The study involves four stages: first, you will be given a brief tutorial in the Python-based RUR-PLE visual programming environment and be asked to answer some warm-up questions to help familiarize you with the RUR-PLE environment. You will then be given a pretest that asks you to answer some basic multiple-choice programming questions. Next, you will be given a series of problems to solve within RUR-PLE, either by writing Python programs to perform a specified task, or by predicting the output and behavior of a given program. Finally, you will take a posttest that is similar to the pretest. We will record your answers to help us understand how to predict student programming performance and learning, based on their starting knowledge. The length of time to complete these tasks will vary, depending on the student, from one to two hours. Your data will be completely anonymized, and no information about you personally will be stored with the results of the study.
If you have any questions or wish to volunteer for the study, please contact Amy Ciavolino at Prof. Marie desJardins () is the faculty advisor for this project, and you may also contact her with any questions or concerns.
Amy Ciavolino (), Robert Deloatch (), Eliana Feasley () and David Walser ()