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Background and context. “Explain in Plain English” (EiPE) questions ask students to explain the high-level purpose of code, requiring them to understand the macrostructure of the program’s intent. A lot is known about techniques that experts use to comprehend code, but less is known about how we should teach novices to develop this capability. Objective. Identify techniques that can be taught to students to assist them in developing their ability to comprehend code and contribute to the body of knowledge of how novices develop their code comprehension skills. Method. We developed interventions that could be taught to novices motivated by previous research about how experts comprehend code: prompting students to identify beacons, identify the role of variables, tracing, and abstract tracing. We conducted think-aloud interviews of introductory programming students solving EiPE questions, varying which interventions each student was taught. Some participants were interviewed multiple times throughout the semester to observe any changes in behavior over time. Findings. Identifying beacons and the name of variable roles were rarely helpful, as they did not encourage students to integrate their understanding of that piece in relation to other lines of code. However, prompting students to explain each variable’s purpose helped them focus on useful subsets of the code, which helped manage cognitive load. Tracing was helpful when students incorrectly recognized common programming patterns or made mistakes comprehending syntax (text-surface). Prompting students to pick inputs that potentially contradicted their current understanding of the code was found to be a simple approach to them effectively selecting inputs to trace. Abstract tracing helped students see high-level, functional relationships between variables. In addition, we observed student spontaneously sketching algorithmic visualizations that similarly helped them see relationships between variables. Implications. Because students can get stuck at many points in the process of code comprehension, there seems to be no silver bullet technique that helps in every circumstance. Instead, effective instruction for code comprehension will likely involve teaching a collection of techniques. In addition to these techniques, meta-knowledge about when to apply each technique will need to be learned, but that is left for future research. At present, we recommend teaching a bottom-up, concrete-to-abstract approach. 

Programming can be an emotional experience, particularly for undergraduate students who are new to computer science. While researchers have interviewed novice programmers about their emotional experiences, it can be difficult to pinpoint the specific emotions that occur during a programming session. In this paper, we argue that electrodermal activity (EDA) sensors, which measure the physiological changes that are indicative of an emotional reaction, can provide a valuable new data source to help study student experiences. We conducted a study with 14 undergraduate students in which we collected EDA data while they worked on a programming problem. This data was then used to cue the participants’ recollections of their emotions during a retrospective interview about the programming experience. Using this methodology, we identified 21 distinct events that triggered student emotions, such as feeling anxiety due to a lack of perceived progress on the problem. We also identified common patterns in EDA data across multiple participants, such as a drop in their physiological reaction after developing a plan, corresponding with a calmer emotional state. These findings provide new information about how students experience programming that can inform research and practice, and also contribute initial evidence of the value of EDA data in supporting studies of emotions while programming. 

Physicians are challenged in treating pain patients due to the lack of quantifiable, objective methods of measuring pain in the clinic; pain sensation is multifaceted and subjective to each individual. There is a critical need for point-of-care quantification of accessible biomarkers to provide objective analyses beyond the subjective pain scales currently employed in clinical care settings. In the present study, we employed an animal model to test the hypothesis that circulating regulators of the inflammatory response directly associate with an objective behavioral response to inflammatory pain. Upon induction of localized paw inflammation, we measured the systemic protein expression of cytokines, and activity levels of matrix metalloproteinases (MMPs) that are known to participate in the inflammatory response at the site of injury and investigated their relationship to the behavioral response across a 24 h period. Intraplantar injection with 1% λ-carrageenan induced a significant increase in paw thickness across this timespan with maximal effects observed at the 8 h timepoint when locomotor activity was also impaired. Expression of the chemokines C-X-C motif chemokine ligand 1 (CXCL1) and C-C motif chemokine ligand 2 (CCL2) positively correlated with paw inflammation and negatively correlated with locomotor activity at 8 h. The ratio of MMP9 to MMP2 activity negatively correlated with paw inflammation at the 8 h timepoint. We postulate that the CXCL1 and CCL2 as well as the ratio of MMP9 to MMP2 activity may serve as predictive biomarkers for the timecourse of inflammation-associated locomotor impairment. These data define opportunities for the future development of a point-of-care device to objectively quantify biomarkers for inflammatory pain states. 

For many decades, educational communities, including computing education, have debated the value of telling students what they need to know (i.e., direct instruction) compared to guiding them to construct knowledge themselves (i.e., constructivism). Comparisons of these two instructional approaches have inconsistent results. Direct instruction can be more efficient for short-term performance but worse for retention and transfer. Constructivism can produce better retention and transfer, but this outcome is unreliable. To contribute to this debate, we propose a new theory to better explain these research results. Our theory, multiple conceptions theory, states that learners develop better conceptual knowledge when they are guided to compare multiple conceptions of a concept during instruction. To examine the validity of this theory, we used this lens to evaluate the literature for eight instructional techniques that guide learners to compare multiple conceptions, four from direct instruction (i.e., test-enhanced learning, erroneous examples, analogical reasoning, and refutation texts) and four from constructivism (i.e., productive failure, ambitious pedagogy, problem-based learning, and inquiry learning). We specifically searched for variations in the techniques that made them more or less successful, the mechanisms responsible, and how those mechanisms promote conceptual knowledge, which is critical for retention and transfer. To make the paper directly applicable to education, we propose instructional design principles based on the mechanisms that we identified. Moreover, we illustrate the theory by examining instructional techniques commonly used in computing education that compare multiple conceptions. Finally, we propose ways in which this theory can advance our instruction in computing and how computing education researchers can advance this general education theory.