@article{rowe_asbell-clarke_baker_eagle_hicks_barnes_brown_edwards_2017, title={Assessing implicit science learning in digital games}, volume={76}, ISSN={["1873-7692"]}, DOI={10.1016/j.chb.2017.03.043}, abstractNote={Building on the promise shown in game-based learning research, this paper explores methods for Game-Based Learning Assessments (GBLA) using a variety of educational data mining techniques (EDM). GBLA research examines patterns of behaviors evident in game data logs for the measurement of implicit learning—the development of unarticulated knowledge that is not yet expressible on a test or formal assessment. This paper reports on the study of two digital games showing how the combination of human coding with EDM has enabled researchers to measure implicit learning of Physics. In the game Impulse, researchers combined human coding of video with educational data mining to create a set of automated detectors of students' implicit understanding of Newtonian mechanics. For Quantum Spectre, an optics puzzle game, human coding of Interaction Networks was used to identify common student errors. Findings show that several of our measures of student implicit learning within these games were significantly correlated with improvements in external postassessments. Methods and detailed findings were different for each type of game. These results suggest GBLA shows promise for future work such as adaptive games and in-class, data-driven formative assessments, but design of the assessment mechanics must be carefully crafted for each game.}, journal={COMPUTERS IN HUMAN BEHAVIOR}, author={Rowe, Elizabeth and Asbell-Clarke, Jodi and Baker, Ryan S. and Eagle, Michael and Hicks, Andrew G. and Barnes, Tiffany M. and Brown, Rebecca A. and Edwards, Teon}, year={2017}, month={Nov}, pages={617–630} }
 @article{liu_zhi_hicks_barnes_2017, title={Understanding problem solving behavior of 6-8 graders in a debugging game}, volume={27}, ISSN={["1744-5175"]}, DOI={10.1080/08993408.2017.1308651}, abstractNote={Abstract Debugging is an over-looked component in K-12 computational thinking education. Few K-12 programming environments are designed to teach debugging, and most debugging research were conducted on college-aged students. In this paper, we presented debugging exercises to 6th–8th grade students and analyzed their problem solving behaviors in a programming game – BOTS. Apart from the perspective of prior literature, we identified student behaviors in relation to problem solving stages, and correlated these behaviors with student prior programming experience and performance. We found that in our programming game, debugging required deeper understanding than writing new codes. We also found that problem solving behaviors were significantly correlated with students’ self-explanation quality, number of code edits, and prior programming experience. This study increased our understanding of younger students’ problem solving behavior, and provided actionable suggestions to the future design of debugging exercises in BOTS and similar environments.}, number={1}, journal={COMPUTER SCIENCE EDUCATION}, author={Liu, Zhongxiu and Zhi, Rui and Hicks, Andrew and Barnes, Tiffany}, year={2017}, pages={1–29} }
 @inproceedings{hicks_peddycord_barnes_2014, title={Building games to learn from their players: Generating hints in a serious game}, volume={8474}, DOI={10.1007/978-3-319-07221-0_39}, abstractNote={This paper presents a method for generating hints based on observed world states in a serious game. BOTS is an educational puzzle game designed to teach programming fundamentals. To incorporate intelligent feedback in the form of personalized hints, we apply data-driven hint-generation methods. This is especially challenging for games like BOTS because of the open-ended nature of the problems. By using a modified representation of player data focused on outputs rather than actions, we are able to generate hints for players who are in similar (rather than identical) states, creating hints for multiple cases without requiring expert knowledge. Our contributions in this work are twofold. Firstly, we generalize techniques from the ITS community in hint generation to an educational game. Secondly, we introduce a novel approach to modeling student states for open-ended problems, like programming in BOTS. These techniques are potentially generalizable to programming tutors for mainstream languages.}, booktitle={Intelligent tutoring systems, its 2014}, author={Hicks, A. and Peddycord, B. and Barnes, T.}, year={2014}, pages={312–317} }