No abstract

Expertise in programming traditionally assumes a binary novice-expert divide. Learning resources typically target programmers who are learning programming for the first time, or expert programmers for that language. An underrepresented, yet important group of programmers are those that are experienced in one programming language, but desire to author code in a different language. For this scenario, we postulate that an effective form of feedback is presented as a transfer from concepts in the first language to the second. Current programming environments do not support this form of feedback.In this study, we apply the theory of learning transfer to teach a language that programmers are less familiar with--such as R--in terms of a programming language they already know--such as Python. We investigate learning transfer using a new tool called Transfer Tutor that presents explanations for R code in terms of the equivalent Python code. Our study found that participants leveraged learning transfer as a cognitive strategy, even when unprompted. Participants found Transfer Tutor to be useful across a number of affordances like stepping through and highlighting facts that may have been missed or misunderstood. However, participants were reluctant to accept facts without code execution or sometimes had difficulty reading explanations that are verbose or complex. These results provide guidance for future designs and research directions that can support learning transfer when learning new programming languages.

Crop raiding and livestock predation are major conservation problems throughout most protected areas in Nepal, including the Khaptad National Park (KNP). However, no information exists on the extent of crop raiding, livestock predation, and animal attacks among villages surrounding KNP. We conducted a survey of 304 households in 30 villages in four districts (Bajhang, Bajura, Doti, and Achham) in the buffer zone of KNP between 24 May and 20 June 2019, using the snowball sampling technique. All households experienced numerous major incidents of crop raiding between April 2017 and May 2019. Major wildlife involved were Wild Boar Sus scrofa, Himalayan Black Bear Ursus thibetanus, Rhesus Macaque Macaca mulatta, Barking Deer Muntiacus vaginalis, Common Leopard Panthera pardus, Golden Jackal Canis aureus, and Porcupine Hystrix spp. Of the 304 households, all had their crops raided over the past two years, 55.5% (n = 169) faced livestock predation, and 2% (n = 6) attacks resulting in death or injury. Over 40% of households reported taking mitigation measures to minimize crop raiding. Common measures such as night guarding, noise making, use of scarecrows, watch dogs, and fencing were practiced. More than half of respondents had negative opinions towards wildlife but they still believed that wildlife should be conserved. There was no or negligible correlation between general opinion of respondents towards wildlife and wildlife conservation with their education, sex, or involvement in natural resources management group. We established baseline information on crop raiding and livestock predation in villages surrounding KNP. Gathered information will be transmitted to relevant authorities to design and implement measures to mitigate such conflicts.

Data science practitioners face the challenge of continually honing their skills such as data wrangling and visualization. As data scientists seek online spaces to network, learn and share resources with one another, each individual has to employ their own ad-hoc strategy to practice their data science skills. Given these disjointed efforts, it is crucial to ask: how can we build an inclusive, welcoming online community of practice that unites data scientists in their collective efforts to become experts? Daily hashtags on Twitter are used on specific days and have shown promise in forming a community of practice (CoP) in social networking sites like Twitter, but how do they benefit the community and its members? To understand how daily hashtags benefit data scientists and form an online CoP, we conducted a qualitative study on #TidyTuesday---a daily hashtag project for data scientists using R---using the framework of CoP as a lens for analysis. We conducted semi-structured interviews with 26 participants and uncovered motivations behind their participation in #TidyTuesday, how the project benefited them, and how it cultivated an online CoP. Our findings contribute to the CSCW research on community of practices by providing design trade-offs of using daily hashtags on Twitter, and guidelines on growing and sustaining an online community of practice for data scientists.