Peter Brusilovsky (Chair) (ISP & SCI, PITT)
Christian D. Schunn (ISP & Psychology, PITT)
Diane Litman (ISP & SCI, PITT)
Vincent Aleven (HCII, CMU)
My dissertation is situated in the field of computer science education research, specifically, the learning and teaching of programming. This is a critical area to be studied, since, primarily, learning to program is difficult, but also, the need for programming knowledge and skills is growing, now more than ever. This research is particularly focused on how to support a student's acquisition of program construction skills through worked examples, one of the best practices for acquiring cognitive skills in STEM areas.
While learning from examples is superior to problem-solving for novices, it is not recommended for intermediate learners with sufficient knowledge, who require more attention to problem-solving. Thus, it is critical for example-based learning environments to adapt the amount and type of assistance given to the student's needs. This important matter has only recently received attention in a few select STEM areas and is still unexplored in the programming domain. The learning technologies used in programming courses mostly focus on supporting student problem-solving activities and, with few exceptions, examples are mostly absent or presented in a static, non-engaging form.
To fill existing gaps in the area of learning from programming examples, my dissertation explores a new genre of worked examples that are both adaptive and engaging, to support students in the acquisition of program construction skills. My research examines how to personalize the generation of examples and how to determine the best sequence of examples and problems, based on the student's evolving level of knowledge. It also includes a series of studies created to assess the effectiveness of the proposed technologies and, more broadly, to investigate the role of worked examples in the process of acquiring programming skills.
Results of our studies show the positive impact that examples have on student engagement, problem-solving, and learning. Adaptive technologies were also found to be beneficial: The adaptive generation of examples had a positive impact on learning and problem-solving performance. The adaptive sequencing of examples and problems engaged students more persistently in activities, resulting in some positive effects on learning.