Animated examples as practice content in a Java programming course

Code examples are commonly used learning resources that help students grasp various programming structures and concepts. However, example code usually requires explanations about what each line or part of the code does. Otherwise, students may find it difficult to follow an example. In this paper, we compare two types of code examples that use different techniques to describe important concepts in the code: annotated and animated examples. The former displays an explanation for a subset of lines in plain text, whereas the latter visualizes code execution. We studied the use and impact of these enhanced examples, provided as non-mandatory practice content, in three introductory Java courses. Our results suggest that animated examples are more engaging and have a positive impact on students' learning. As compared to annotated examples, students spent more time with animated examples and more likely completed them. Also, a positive relationship was found between the number of explored animated examples and the overall course grade

Program Construction Examples in Computer Science Education: From Static Text to Adaptive and Engaging Learning Technology

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