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1. Readable vs. Writable Code: A Survey of Intermediate Students' Structure Choices

   https://doi.org/10.1145/3478431.3499413  

   Wiese, Eliane; Rafferty, Anna N.; Pyper, Jordan (February 2022, SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education)

   Since intermediate CS students can use a variety of control structures, why do their choices often not match experts' Students may not realize what choices expert prefer, find non-expert choices easier to read, or simply forget to write with expert structure. To disentangle these explanations, we surveyed 328 2nd and 3rd semester undergraduates, with tasks including writing short functions, selecting which structure was most readable or best styled, and comprehension questions. Questions focused on seven control structure topics that were important to instructors (e.g., factoring out repeated code between an if-block and its else). Students frequently wrote with non-expert structure, and, for five topics, at least 1/3 of students (48% - 71%) thought a non-expert structure was more readable than the expert one. However, students often made one choice when writing code, but preferred a different choice when reading it. Additionally, for more complex topics, students often failed to notice (or understand) differences in execution caused by changes in structure. Together, these results suggest that instruction and practice for choosing control structures should be context-specific, and that assessment focused only on code writing may miss underlying misunderstandings. 

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2. Formalizing the fundamental Faustian bargain: Inefficacious decision-makers sacrifice their freedom of choice to coercive leaders for economic security

   https://doi.org/10.1371/journal.pone.0275265  

   DeCaro, Daniel A.; DeCaro, Marci S.; Hotaling, Jared M.; Appel, Rachel (September 2022, PLOS ONE)

   Fernandes, Thiago P. (Ed.)

   Individuals typically prefer the freedom to make their own decisions. Yet, people often trade their own decision control (procedural utility) to gain economic security (outcome utility). Decision science has not reconciled these observations. We examined how decision-makers’ efficacy and security perceptions influence when, why, and how individuals exchange procedural and outcome utility. Undergraduate adults ( N = 77; M age = 19.45 years; 73% female; 62% Caucasian, 13% African American) were recruited from the psychology participant pool at a midwestern U.S. metropolitan university. Participants made financial decisions in easy and hard versions of a paid card task resembling a standard gambling task, with a learning component. During half the trials, they made decisions with a No-Choice Manager who controlled their decisions, versus a Choice Manager who granted decision control. The hard task was designed to be too difficult for most participants, undermining their efficacy and security, and ensuring financial losses. The No-Choice Manager was designed to perform moderately well, ensuring financial gains. Participants felt greater outcome satisfaction (utility) for financial gains earned via Choice, but not losses. Participants (85%) preferred the Choice manager in the easy task but preferred the No-Choice Manager (56%) in the hard task. This change in preference for choice corresponded with self-efficacy and was mediated by perceived security. We used Decision Field Theory to develop potential cognitive models of these decisions. Preferences were best described by a model that assumed decision-makers initially prefer Choice, but update their preference based on loss-dependent attentional focus. When they earned losses (hard task), decision-makers focused more on economic payoffs (financial security), causing them to deemphasize procedural utility. Losses competed for attention, pulling attention toward economic survivability and away from the inherent value of choice. Decision-makers are more likely to sacrifice freedom of choice to leaders they perceive as efficacious to alleviate perceived threats to economic security. 

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3. Vernacular Visualization Practices in a Citizen Science Project

   https://doi.org/10.1145/2998181.2998239  

   Snyder, Jaime (January 2017, CSCW 2017)

   Vernacular visualizations are visual representations of information created by and for non-expert users, in contrast to those developed by experts for specialized audiences. Research looking at everyday design practices and the democratization of innovation indicates that deeper understanding of non-expert design practices has a positive impact on technology development. This qualitative study focuses on the creation, use and dissemination of vernacular visualizations in a citizen science project. Findings from this research (1) map visualization practices in an established citizen science project, (2) contribute to theoretical understanding of the ways in which vernacular visualization practices support data-rich collaborative and coordinated work, and (3) suggest ways in which visualizations and visual resources can be evaluated in terms of their abilities to enrich coordination and communication in these contexts. 

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4. Assuage: Assembly Synthesis Using A Guided Exploration

   https://doi.org/10.1145/3472749.3474740  

   Hu, Jingmei; Vaithilingam, Priyan; Chong, Stephen; Seltzer, Margo; Glassman, Elena L. (October 2021, UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology)

   Assembly programming is challenging, even for experts. Program synthesis, as an alternative to manual implementation, has the potential to enable both expert and non-expert users to generate programs in an automated fashion. However, current tools and techniques are unable to synthesize assembly programs larger than a few instructions. We present Assuage : ASsembly Synthesis Using A Guided Exploration, which is a parallel interactive assembly synthesizer that engages the user as an active collaborator, enabling synthesis to scale beyond current limits. Using Assuage, users can provide two types of semantically meaningful hints that expedite synthesis and allow for exploration of multiple possibilities simultaneously. Assuage exposes information about the underlying synthesis process using multiple representations to help users guide synthesis. We conducted a within-subjects study with twenty-one participants working on assembly programming tasks. With Assuage, participants with a wide range of expertise were able to achieve significantly higher success rates, perceived less subjective workload, and preferred the usefulness and usability of Assuage over a state of the art synthesis tool. 

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5. Combining Experts' Causal Judgments

   Alrajeh, D; Chockler, H.; Halpern, J.Y. (February 2018, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18))

   Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being \emph{compatible}, and show how compatible causal models can be combined. We then use it as the basis for combining experts causal judgments. We illustrate our approach on a number of real-life examples. 

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