Digital Transformation of Education - 2016 Eagle, M., Mostafavi, B., & Barnes, T. (2016). Data-driven Domain Models for Problem Solving. In R. Sottilare, A. Graesser, X. Hu, A. Olney, B. Nye, & A. Sinatra (Eds.), Design Recommendations for Intelligent Tutoring Systems: Domain Modeling (Vol. 4). Orlando, Florida: Army Research Laboratory. Rowe, E., Asbell-Clarke, J., Hicks, M. E. A., Barnes, T., Brown, R., & Edwards, T. (2016). Validating Game-based Measures of Implicit Science Learning. Educational Data Mining, 490–495. Mostafavi, B., & Barnes, T. (2016). Exploring the Impact of Data-driven Tutoring Methods on Students' Demonstrative Knowledge in Logic Problem Solving Educational Data Mining (EDM2016). Educational Data Mining, 460–465. Zhou, G., Lynch, C., Price, T., Barnes, T., & Chi, M. (2016). The Impact of Granularity on the Effectiveness of Students' Pedagogical Decision. Presented at the Annual Meeting of the Cognitive Science Society (CogSci). Price, T., Dong, Y., & Barnes, T. (2016). Generating Data-driven Hints for Open-ended Programming. Educational Data Mining, 191–198. Liu, Z., Brown, R., Lynch, C., Barnes, T., Baker, R., Bergner, Y., & Mcnamara, D. (2016). MOOC Learning by Country and Culture; an Exploratory Analysis. Educational Data Mining, EDM2016, 127–134. Hicks, D., Liu, Z., Eagle, M., & Barnes, T. (2016). Measuring Gameplay Affordances of User-Generated Content in and Educational Game. Presented at the International Conference on Educational Data Mining (EDM), Raleigh, North Carolina. Lin, C., & Chi, M. (2016). Intervention-BKT: Incorporating Instructional Interventions into Bayesian Knowledge Tracing. In Intelligent Tutoring Systems (pp. 208–218). https://doi.org/10.1007/978-3-319-39583-8_20 Huang, K., Ge, X., & Eseryel, D. (2016). Metaconceptually-enhanced simulation-based inquiry: effects on eighth grade students’ conceptual change and science epistemic beliefs. Educational Technology Research and Development, 65(1), 75–100. https://doi.org/10.1007/S11423-016-9462-5 Price, T. W., Cateté, V., Albert, J., Barnes, T., & Garcia, D. D. (2016). Lessons Learned from "BJC" CS Principles Professional Development. Proceedings of the 47th ACM Technical Symposium on Computing Science Education - SIGCSE '16. Presented at the the 47th ACM Technical Symposium. https://doi.org/10.1145/2839509.2844625 Cateté, V., Snider, E., & Barnes, T. (2016). Developing a Rubric for a Creative CS Principles Lab. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education - ITiCSE '16. Presented at the the 2016 ACM Conference. https://doi.org/10.1145/2899415.2899449 Payton, J., & Barnes, T. (2016). Learn about broadening participation. ACM SIGCSE Bulletin, 48(3), 5–5. https://doi.org/10.1145/2993223.2993226 Payton, J., Barnes, T., Buch, K., Rorrer, A., Zuo, H. F., & Naolu, B. (2016). Promoting computing faculty success through interinstitutional faculty learning communities. 2016 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT 2016). https://doi.org/10.1109/respect.2016.7836163 Hicks, D., Eagle, M., Rowe, E., Asbell-Clarke, J., Edwards, T., & Barnes, T. (2016). Using Game Analytics to Evaluate Puzzle Design and Level Progression in a Serious Game. LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE, pp. 440–448. https://doi.org/10.1145/2883851.2883953 Price, T. W., Brown, N. C. C., Lipovac, D., Barnes, T., & Kolling, M. (2016). Evaluation of a Frame-based Programming Editor. PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH (ICER'16), pp. 33–42. https://doi.org/10.1145/2960310.2960319 Mostafavi, B., & Barnes, T. (2016). Data-driven Proficiency Profiling - Proof of Concept. LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE, pp. 324–328. https://doi.org/10.1145/2883851.2883935 Azevedo, R., Martin, S. A., Taub, M., Mudrick, N. V., Millar, G. C., & Grafsgaard, J. F. (2016). Are Pedagogical Agents' External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems? INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 197–207. https://doi.org/10.1007/978-3-319-39583-8_19 Taub, M., & Azevedo, R. (2016). Using multi-channel data to assess, understand, and support affect and metacognition with intelligent tutoring systems. Intelligent tutoring systems, its 2016, 0684, 543–544. Taub, M., & Azevedo, R. (2016). Using eye-tracking to determine the impact of prior knowledge on self-regulated learning with an adaptive hypermedia-learning environment. Intelligent tutoring systems, its 2016, 0684, 34–47. Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2016). Using Multi-level Modeling with Eye-Tracking Data to Predict Metacognitive Monitoring and Self-regulated Learning with CRYSTAL ISLAND. INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 240–246. https://doi.org/10.1007/978-3-319-39583-8_24 Lin, C., & Chi, M. (2016). Intervention-BKT: Incorporating instructional interventions into Bayesian knowledge tracing. Intelligent tutoring systems, its 2016, 0684, 208–218. Liu, Z., Mostafavi, B., & Barnes, T. (2016). Combining Worked Examples and Problem Solving in a Data-Driven Logic Tutor. INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 347–353. https://doi.org/10.1007/978-3-319-39583-8_40 Bouchet, F., Harley, J. M., & Azevedo, R. (2016). Can adaptive pedagogical agents' prompting strategies improve students' learning and self-regulation? Intelligent tutoring systems, its 2016, 0684, 368–374. Shen, S. T., Lin, C., Mostafavi, B., Barnes, T., & Chi, M. (2016). An analysis of feature selection and reward function for model-based reinforcement learning. Intelligent tutoring systems, its 2016, 0684, 504–505. Isukapati, I. K., List, G. F., & Kamlet, M. S. (2016). Bid-based signal control with all passive players. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 602–607. https://doi.org/10.1109/itsc.2016.7795615 Lynch, C. F., Xue, L. T., & Chi, M. (2016). Evolving augmented graph grammars for argument analysis. Proceedings of the 2016 Genetic and Evolutionary Computation Conference (GECCO'16 Companion), 65–66. https://doi.org/10.1145/2908961.2908994 Evans, M. A., Schnittka, C., Jones, B. D., & Brandt, C. B. (2016). Studio STEM: A Model to Enhance Integrative STEM Literacy Through Engineering Design. CONNECTING SCIENCE AND ENGINEERING EDUCATION PRACTICES IN MEANINGFUL WAYS: BUILDING BRIDGES, Vol. 44, pp. 107–137. https://doi.org/10.1007/978-3-319-16399-4_5 Law, V., Ge, X., & Eseryel, D. (2016). The Development of a Self-regulation in a Collaborative Context Scale. TECHNOLOGY KNOWLEDGE AND LEARNING, 21(2), 243–253. https://doi.org/10.1007/s10758-016-9274-z Schnittka, C. G., Evans, M. A., Won, S. G. L., & Drape, T. A. (2016). After-School Spaces: Looking for Learning in All the Right Places. RESEARCH IN SCIENCE EDUCATION, 46(3), 389–412. https://doi.org/10.1007/s11165-015-9463-0 Payton, J., Barnes, T., Buch, K., Rorrer, A., Zuo, H., Gosha, K., … Dennis, L. (2016). STARS Computing Corps: Enhancing Engagement of Underrepresented Students and Building Community in Computing. COMPUTING IN SCIENCE & ENGINEERING, 18(3), 44–57. https://doi.org/10.1109/mcse.2016.42 Barnes, T., Payton, J., Thiruvathukal, G. K., Boyer, K. E., & Forbes, J. (2016). Best of RESPECT, Part 2. COMPUTING IN SCIENCE & ENGINEERING, Vol. 18, pp. 11–13. https://doi.org/10.1109/mcse.2016.51 Harley, J. M., Carter, C. K., Papaionnou, N., Bouchet, F., Landis, R. S., Azevedo, R., & Karabachian, L. (2016). Examining the predictive relationship between personality and emotion traits and students' agent-directed emotions: Towards emotionally-adaptive agent-based learning environments. User Modeling and User-Adapted Interaction, 26(2-3), 177–219. Barnes, T., Payton, J., Thiruvathukal, G. K., Boyer, K. E., & Forbes, J. (2016). Best of RESPECT, Part 1. Computing in Science & Engineering, 18(2), 6–8. Chin, D. B., Chi, M., & Schwartz, D. L. (2016). A comparison of two methods of active learning in physics: inventing a general solution versus compare and contrast. INSTRUCTIONAL SCIENCE, 44(2), 177–195. https://doi.org/10.1007/s11251-016-9374-0 Barnes, T., & Thiruvathukal, G. K. (2016, March). The Need for Research in Broadening Participation. COMMUNICATIONS OF THE ACM, Vol. 59, pp. 33–34. https://doi.org/10.1145/2880177 Trevors, G., Feyzi-Behnagh, R., Azevedo, R., & Bouchet, F. (2016). Self-regulated learning processes vary as a function of epistemic beliefs and contexts: Mixed method evidence from eye tracking and concurrent and retrospective reports. Learning and Instruction, 42, 31–46.