cognition model (i.e., bottom-up or top-down) applied during program comprehension tasks. The cognition models examine how programmers understand source code by describing the temporary information structures in the programmer’s short term memory. The two types of models that we are interested in are top-down and bottom-up. The top-down model is normally applied as-needed (i.e., the domain of the system is familiar). The bottom-up model is typically applied when a developer is not familiar with the domain or the source code. An eye-tracking study of 18 developers reading and summarizing Java methods is used as our dataset for analyzing the mental cognition model. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. The results indicate that on average, experts and novices read the methods more closely (using the bottom-up mental model) than bouncing around (using top-down). However, on average novices spend longer gaze time performing bottom-up (66s.) compared to experts (43s.)
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The Innovation Ecology: Collaborative Information, Community Support, and Policy in a Creative Technology Community
In this paper, we explore a network of distributed individuals’ collective efforts to establish an innovation ecology allowing them to engage in bottom up creative technological practices in today’s information society. Specifically, we present an empirical study of the technological practices in an emerging creative technology community – independent [indie] game developers in the United States. Based on indie game developers’ own accounts, we identified four themes that constitute an innovation ecology from the bottom up, including problem solving; collaborative information seeking, sharing, and reproducing; community support; and policy and politics. We argue that these findings inform our understanding of bottom up technological innovation and shed light on the design of sociotechnical systems to mediate and support such innovation beyond the gaming context.
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- Award ID(s):
- 1849718
- PAR ID:
- 10154595
- Date Published:
- Journal Name:
- Information in Contemporary Society. iConference 2019. Lecture Notes in Computer Science
- Volume:
- 11420
- Page Range / eLocation ID:
- 614-624
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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