Many computer science students complete their undergraduate degrees with insufficient testing skills and knowledge. To understand the gaps in students' testing skills and knowledge, we analyzed 1014 software tests written by 12 groups in an undergraduate Software Quality Assurance (SQA) course project. In the project the student groups were provided a requirements document and were instructed to follow Test Driven Development (TDD) practices using black-box tests. To understand how the groups applied black-box testing in their project, we created an automatic tool to sort the tests into categories or "test buckets." By analyzing the test bucket data, we were able to assess the effectiveness and efficiency of student-written tests. We observed that the student groups were significantly more likely to test for explicit requirements than implicit requirements and significantly more likely to test happy paths than invalid inputs. Furthermore, students inefficiently tested happy paths, invalid inputs and explicit requirements resulting in a higher proportion of software tests with duplicate intent. Based on these results we provide insights into how black-box test education can be improved.

Computational thinking is now included in K-5 classrooms and this has led to a demand for new interactive and collaborative learning tools that engage a younger audience. Block-based programming and educational games have both been shown to be effective at engaging children, however they have limitations with respect to supporting collaborative learning and equitable access. Our goal in designing Run, Llama, Run was to build on the positive aspects of block-based programming and educational games while also addressing these limitations. Furthermore, we are using Run, Llama, Run as a platform to explore the trade-offs between digital and tangible interfaces to understand how best to support equitable access while maintaining learning, engagement, and collaboration.

The use of games for education has shown effectiveness in engaging students to learn. Computer Science is one of the most heavily targeted fields of study for educational games. A major challenge for serious games is the need for them to be suitable for learners with different abilities and needs. To address this challenge, this thesis considers the use of automatically adaptive serious games (in the context of Computer Science). The goal of this research is to develop a methodology for modifying existing non-adaptive programming games and assess the benefits of applying an adaptive approach with respect to learner engagement and achievement of learning outcomes.

Debugging is an essential but challenging task that can present a great deal of confusion and frustration to novice programmers. It can be argued that Computer Science education does not sufficiently address the challenges that students face when identifying bugs in their programs. To help students learn effective debugging techniques and to provide students a more enjoyable and motivating experience, we have designed the RoboBUG game. RoboBUG is a serious game that can be customized with respect to different programming languages and game levels.