Scientific expertise is manifested through extensive cycles of making and acting on decisions. To learn the processes and practices of science, therefore, students must have practice with scientific decision making. We argue that this can only happen if students are afforded agency: the opportunity to make decisions to pursue a goal. In this study, we compared two different introductory physics labs through the lens of structuring and supporting student agency. We explore both the role of decision making agency (students are afforded opportunities to make decisions about their lab investigations) and epistemic agency (students are afforded opportunities to generate knowledge). We found that the two labs differed in both the amount and type of structure provided to students: one lab (the intervention condition), with more overall structure, cued student decision making; the other lab (control condition), with less overall structure, made decisions for students. Students in the intervention condition documented, on average, ten times more decisions during their investigations, including more unprompted decisions. In contrast, in labs with less available epistemic agency (i.e., labs where they had to verify or demonstrate canonical models or constants), students in the intervention condition documented fewer decisions than in labs with more available epistemic agency. We associate the improved decision making with students taking up personal agency, based on significant shifts in students' tone as they described their experimental methods (from passive, third-person narratives to active, first-person narratives). Students in the intervention condition also made higher-quality decisions in line with scientific practice. The study speaks to the important nuances between structure and agency in instructional labs and tests the generalizability of previous research on lab instruction to a two-year college population.

Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we demonstrate that Eterna participants’ moves and strategies can be leveraged to improve automated computational RNA design. We present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, we present a multilayer convolutional neural network (CNN) EternaBrain that achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players’ moves are partially stereotyped. Pipelining this CNN’s move predictions with single-action-playout (SAP) of six strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. EternaBrain-SAP outperforms previously published RNA design algorithms and achieves similar or better performance than a newer generation of deep learning methods, while being largely orthogonal to these other methods. Our study provides useful lessons for future efforts to achieve human-competitive performance with automated RNA design algorithms.

Over the past decade, scientific discovery games (SDGs) have emerged as a viable approach for biomedical research, engaging hundreds of thousands of volunteer players and resulting in numerous scientific publications. After describing the origins of this novel research approach, we review the scientific output of SDGs across molecular modeling, sequence alignment, neuroscience, pathology, cellular biology, genomics, and human cognition. We find compelling results and technical innovations arising in problem-oriented games such as Foldit and Eterna and in data-oriented games such as EyeWire and Project Discovery. We discuss emergent properties of player communities shared across different projects, including the diversity of communities and the extraordinary contributions of some volunteers, such as paper writing. Finally, we highlight connections to artificial intelligence, biological cloud laboratories, new game genres, science education, and open science that may drive the next generation of SDGs.

Folded RNA molecules underlie emerging approaches to disease detection and gene therapy. These applications require RNA sequences that fold into target base-pairing patterns, but computational algorithms generally remain inadequate for these RNA secondary structure design tasks. The Eterna project has collected over 1 million player moves by crowdsourcing RNA designs in the form of puzzles that reach extraordinary difficulty. Here, we present these data in the eternamoves repository and test their utility in training a multilayer convolutional neural network to predict moves. When pipelined with hand-coded move combinations developed by the Eterna community, the resulting EternaBrain method solves 61 out of 100 independent RNA design puzzles in the Eterna100 benchmark. EternaBrain surpasses all six other prior algorithms that were not informed by Eterna strategies and suggests a path for automated RNA design to achieve human-competitive performance.

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