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It has been well documented that a large portion of the cost of any software lies in the time spent by developers in understanding a program's source code before any changes can be undertaken. One of the main contributors to software comprehension, by subsequent developers or by the authors themselves, has to do with the quality of the lexicon, (i.e., the identifiers and comments) that is used by developers to embed domain concepts and to communicate with their teammates. In fact, previous research shows that there is a positive correlation between the quality of identifiers and the quality of a software project. Results suggest that poor quality lexicon impairs program comprehension and consequently increases the effort that developers must spend to maintain the software. However, we do not yet know or have any empirical evidence, of the relationship between the quality of the lexicon and the cognitive load that developers experience when trying to understand a piece of software. Given the associated costs, there is a critical need to empirically characterize the impact of the quality of the lexicon on developers' ability to comprehend a program. In this study, we explore the effect of poor source code lexicon and readability on developers' cognitive load as measured by a cutting-edge and minimally invasive functional brain imaging technique called functional Near Infrared Spectroscopy (fNIRS). Additionally, while developers perform software comprehension tasks, we map cognitive load data to source code identifiers using an eye tracking device. Our results show that the presence of linguistic antipatterns in source code significantly increases the developers' cognitive load.

Educators stand to benefit from advance predictions of their students' course performance based on learning process data collected in their courses. Indeed, such predictions can help educators not only to identify at-risk students, but also to better tailor their instructional methods. In computing education, at least two different measures, the Error Quotient and Watwin Score, have achieved modest success at predicting student course performance based solely on students' compilation attempts. We hypothesize that one can achieve even greater predictive power by considering students' programming activities more holistically. To that end, we derive the Normalized Programming State Model (NPSM), which characterizes students' programming activity in terms of the dynamically-changing syntactic and semantic correctness of their programs. In an empirical study, the NPSM accounted for 41% of the variance in students' programming assignment grades, and 36% of the variance in students' final course grades. We identify the components of the NPSM that contribute to its explanatory power, and derive a formula capable of predicting students' course programming performance with between 36 and 67 percent accuracy, depending on the quantity of programming process data.

Social learning theory emphasizes the importance of providing learners with opportunities to observe their peers, and to participate actively in a community. Unfortunately, early computing courses tend to emphasize individual programming assignments, which discourage learners from observing and working with their peers. In order to explore the possibility that increased opportunities for social awareness and interaction while working on programming assignments might influence learning outcomes in early computing courses, we are studying the design and use of social networking-style activity streams in such courses. In an empirical study of the use of two types of activity streams in a CS 2 course - one that was part of a learning management system, and one integrated directly into students' programming environment - we found that students who used the integrated stream were twice as socially active; however, social participation in both environments was positively correlated with students' grades. Our results suggest that the use of activity streams as an adjunct to individual programming assignments can positively influence learning; computing instructors would do well to find ways to get their students to participate actively in activity streams during the programming process.