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People often learn programming in face-to-face courses or online tutorials. Interactive computer tutors---systems that provide learning content interactively---are becoming more common in online tools such as those teaching computer programming. Studies have shown that teachers, interactive computer tutors, and the combination of both are efficient and effective in teaching programming. However, there is limited understanding of the comparative perspectives of learners learning from these two different sources. We conducted an exploratory study using semi-structured interviews and recruited 20 participants with programming experience from both teachers and interactive computer tutors. Speaking with our participants, we surfaced factors that learners like and dislike from the two learning resources and discussed the strengths and weaknesses between the two. Based on our findings, we discuss implications for designs that programming educators and interactive computer tutor developers can use to improve their teaching effectiveness. 

Many people are learning programming on their own using various online resources. Unfortunately, learners using these resources often be- come disengaged or even quit when encountering an obstacle they cannot overcome without additional help. Teachers in a classroom can provide this type of help, but this may be impractical or impossible to implement in online educational settings. To address this issue, we added a visually- oriented hint system into an existing online educational game designed to teach novices introductory programming concepts. We implemented three versions of the hint system, providing equivalent information for each level of the game, adjusting the amount of interactivity between versions. The first version consisted of a static image with text showing how to solve a level in a single panel. The second version included a series of images that allowing users to scroll through hints step-by-step. The final version showed a short video allowing users to play, pause, and seek through animated hint(s). In total, we had 150 people play the game, randomly assigned to one of these three versions of the hint system. We found that users had a strong preference for the video version of the hint system, completing more levels. Based on these findings, we propose suggestions for designers of online educational tools to better support their users. 

Many people are learning programming on their own using various online resources such as educational games. Unfortunately, little is known about how to keep online educational game learners motivated throughout their game play, especially if they become disengaged or frustrated with their task. Keeping online learners engaged is essential for learning programming, as it may have lasting effects on their views and self-efficacy towards computer science. To address this issue, we created a coarse-grained frustration detector that provided users with customized, adaptive feedback to help (re)engage them with the game content. We ran a controlled experiment with 400 participants over the course of 1.5 months, with half of the players playing the original game, and the other half playing the game with the frustration detection and adaptive feed- back. We found that the users who received the adaptive feedback when frustrated completed more levels than their counterparts who did not receive this customized feedback. Based on these findings, we believe that adaptive feedback is essential in keeping educational game learners engaged, and propose future work for researchers and designers of online educational games to better support their users. 

It is critical to focus on diversity and increasing participation of underrepresented groups in computing. To address this need, we must better understand minorities' access to role models and mentors, especially at a young age, as research and practice shows that these relationships can affect students' self-efficacy and motivation in the educational fields and careers they choose to pursue. We provided a 9-Saturday programming camp to middle school students in Newark, New Jersey with near-peer mentors (first year, college student instructors) to learn more about the younger students' initial access to role-models and mentors, and how an intervention might change this. Our camp served a total of 28 minority students (17 males and 11 females; grades 5-7) from a low-income, urban area. We found that when asked at the beginning of the camp, our middle students largely reported that they did not have any role-models or mentors in computing. However, at the conclusion of the camp, these same students indicated that they developed strong connections with their near-peer mentors and even saw them as role-models. These findings highlight the need for more mentorship opportunities for students of all ages, and the importance of providing resources and support to help develop and nurture these connections. 

As programming continues to be an essential 21st century skill, it is critical to focus on diversity and increasing participation of underrepresented groups in computing. To address this need, we must better understand minorities' views and attitudes towards programming, especially in their youth, as literature shows that children form ideas about their interests and careers in middle school or earlier. To explore this, we provided middle school students in the U.S. with a full day (7 hours) of programming activities to learn about their initial attitudes towards computing and how a short intervention might change these attitudes. We ran two separate one-day events, serving a total of 34 minority students (21 males and 13 females; grades 6 and 7) from a low-income, urban area. We found that students' initial attitudes towards computing were high, and that one day of learning programming increased their reported attitudes in computing even more. We also found differences in attitudes by gender and ethnicity. These findings highlight the positive attitudes minority students have towards computing, and the importance of providing resources and support to help maintain their interests in computing while recognizing demographic differences. 

People typically learn programming from teachers in in-person courses or online tutorials. Interactive computer tutors---systems that deliver learning content interactively---have become more prevalent in online settings for teaching skills such as computer programming. Research has shown the efficiency and effectiveness of learning programming from teachers, interactive computer tutors, and a combination of both. However, there is limited understanding of learners' comparative perspectives about their experience learning from these different resources. We conducted an exploratory study using semi-structured interviews, recruiting 20 participants that had experience learning programming from both teachers and interactive computer tutors. We identified factors that learners like and dislike from both learning methods and discussed the strengths and weaknesses of them. Based on our findings, we propose suggestions for designers of interactive computer tutors, and for programming educators. 

Learners regularly abandon online coding tutorials when they get bored or frustrated, but there are few techniques for anticipating this abandonment to intervene. In this paper, we examine the feasibility of predicting abandonment with machine-learned classifiers. Using interaction logs from an online programming game, we extracted a collection of features that are potentially related to learner abandonment and engagement, then developed classifiers for each level. Across the first five levels of the game, our classifiers successfully predicted 61% to 76% of learners who did not complete the next level, achieving an average AUC of 0.68. In these classifiers, features negatively associated with abandonment included account activation and help-seeking behaviors, whereas features positively associated with abandonment included features indicating difficulty and disengagement. These findings highlight the feasibility of providing timely intervention to learners likely to quit. 

As more people turn to online resources to learn, there will be an increasing need for systems to understand and adapt to the needs of their users. Engagement is an important aspect to keep users committed to learning. Learning approaches for online systems can benefit from personalization to engage their users. However, many approaches for personalization currently rely on methods (e.g., historical behavioral data, questionnaires, quizzes) that are unable to provide a personalized experience from the start-of-use of a system. As users in a learning environment are exposed to new content, the first impression that they receive from the system influences their commitment with the program. In this position paper we propose a quantitative approach for personalization in online learning environments to overcome current problems for personalization in such environments.