Digital Transformation of Education - 2018

Paaßen, B., Hammer, B., Price, T. W., Barnes, T., Gross, S., & Pinkwart, N. (2018). The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), 1–35. Retrieved from https://doi.org/10.5281/zenodo.3554697

Zhi, R., Price, T. W., Lytle, N., Dong, Y., & Barnes, T. (2018). Reducing the State Space of Programming Problems through Data-Driven Feature Detection. Education Data Mining Workshop.

Sirbu, M.-D., Dascalu, M., Crossley, S. A., McNamara, D. S., Barnes, T., Lynch, C. F., & Trausan-Matu, S. (2018). Exploring Online Course Sociograms Using Cohesion Network Analysis. In Lecture Notes in Computer Science (pp. 337–342). https://doi.org/10.1007/978-3-319-93846-2_63

Shen, S., Mostafavi, B., Lynch, C., Barnes, T., & Chi, M. (2018). Empirically Evaluating the Effectiveness of POMDP vs. MDP Towards the Pedagogical Strategies Induction. In Lecture Notes in Computer Science (pp. 327–331). https://doi.org/10.1007/978-3-319-93846-2_61

Crossley, S. A., Sirbu, M.-D., Dascalu, M., Barnes, T., Lynch, C. F., & McNamara, D. S. (2018). Modeling Math Success Using Cohesion Network Analysis. In Lecture Notes in Computer Science (pp. 63–67). https://doi.org/10.1007/978-3-319-93846-2_12

Cody, C., Mostafavi, B., & Barnes, T. (2018). Investigation of the Influence of Hint Type on Problem Solving Behavior in a Logic Proof Tutor. In Lecture Notes in Computer Science (pp. 58–62). https://doi.org/10.1007/978-3-319-93846-2_11

Cateté, V., Lytle, N., & Barnes, T. (2018). Creation and validation of low-stakes rubrics for K-12 computer science. Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education  - ITiCSE 2018. Presented at the the 23rd Annual ACM Conference. https://doi.org/10.1145/3197091.3197134

Price, T. W., Zhi, R., Dong, Y., Lytle, N., & Barnes, T. (2018). The Impact of Data Quantity and Source on the Quality of Data-Driven Hints for Programming. In Lecture Notes in Computer Science (pp. 476–490). https://doi.org/10.1007/978-3-319-93843-1_35

Peddycord-Liu, Z., Harred, R., Karamarkovich, S., Barnes, T., Lynch, C., & Rutherford, T. (2018). Learning Curve Analysis in a Large-Scale, Drill-and-Practice Serious Math Game: Where Is Learning Support Needed? In Lecture Notes in Computer Science (pp. 436–449). https://doi.org/10.1007/978-3-319-93843-1_32

Eseryel, U. Y., Booji, R., & Eseryel, D. (2018). Recruitment through LinkedIn: Lessons learned from the Fortune 100 companies. International Conference on Information Systems (ICIS). San Francisco, CA.

Wolters, J., Eseryel, U. Y. E., & D. (2018). Identifying the critical success factors for low customized ERP system implementations in SMEs. . Hawaii International Conference on System Sciences (HICCS), 3–6, Waikoloa Village, HI: January.

Peng, X., Zhang, D., Jackson, M., Yalvac, B., Ketsetzi, A., & Eseryel, D. (2018). Examining the Learning by Teaching Method in Computer-Aided Design Instruction. Computer-Aided Design and Applications, 8. https://doi.org/10.14733/cadaps.2019.129-139

Peng, X., Zhang, D., Jackson, M., Yalvac, B., Ketsetzi, A., & Eseryel, D. (2018). Computer-Aided Design & Applications. Examining the Learning by Teaching Method in Computer-Aided Design Instruction., 16(1), 129–139. https://doi.org/10.14733/cadaps.2019.129.139

Shen, S., Ausin, M. S., Mostafavi, B., & Chi, M. (2018). Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach. PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18), pp. 43–51. https://doi.org/10.1145/3209219.3209232

Emerson, A., Sawyer, R., Azevedo, R., & Lester, J. (2018). Gaze-Enhanced Student Modeling for Game-based Learning. PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18), pp. 63–72. https://doi.org/10.1145/3209219.3209238

Catete, V., Lytle, N., Dong, Y., Boulden, D., Akram, B., Houchins, J., … Boyer, K. (2018). Infusing Computational Thinking into Middle Grade Science Classrooms: Lessons Learned. WIPSCE'18: PROCEEDINGS OF THE 13TH WORKSHOP IN PRIMARY AND SECONDARY COMPUTING EDUCATION, pp. 109–114. https://doi.org/10.1145/3265757.3265778

Bouchet, F., Harley, J. M., & Azevedo, R. (2018). Evaluating Adaptive Pedagogical Agents' Prompting Strategies Effect on Students' Emotions. INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 33–43. https://doi.org/10.1007/978-3-319-91464-0_4

Sinclair, J., Jang, E. E., Azevedo, R., Lau, C., Taub, M., & Mudrick, N. V. (2018). Changes in Emotion and Their Relationship with Learning Gains in the Context of MetaTutor. INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 202–211. https://doi.org/10.1007/978-3-319-91464-0_20

Taub, M., Mudrick, N. V., Rajendran, R., Dong, Y., Biswas, G., & Azevedo, R. (2018). How Are Students' Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs? INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 233–242. https://doi.org/10.1007/978-3-319-91464-0_23

Mudrick, N. V., Sawyer, R., Price, M. J., Lester, J., Roberts, C., & Azevedo, R. (2018). Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH. INTELLIGENT TUTORING SYSTEMS, ITS 2018, 10858, 140–149. https://doi.org/10.1007/978-3-319-91464-0_14

Cloude, E. B., Taub, M., & Azevedo, R. (2018). Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems. INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 44–53. https://doi.org/10.1007/978-3-319-91464-0_5

Price, M. J., Mudrick, N. V., Taub, M., & Azevedo, R. (2018). The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with MetaTutor. INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 170–179. https://doi.org/10.1007/978-3-319-91464-0_17

Taub, M., Azevedo, R., & Mudrick, N. V. (2018). How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems? INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 223–232. https://doi.org/10.1007/978-3-319-91464-0_22

Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2018). "Let's set up some subgoals": Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies, 11(1), 54–66.

Taub, M., Azevedo, R., Bradbury, A. E., Millar, G. C., & Lester, J. (2018). Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. LEARNING AND INSTRUCTION, 54, 93–103. https://doi.org/10.1016/j.learninstruc.2017.08.005

Duan, C., Peng, X. B., Yuan, T. Y., Yalvac, B., Ketsetzi, A., Hing, E. L., … Eyupoglu, T. F. (2018). Promoting life-long learning skills in CAD using the peer-generated screencast tutorials. Proceedings of the ASME International Mechanical Engineering Congress and Exposition, 2017, vol 5. https://doi.org/10.1115/imece2017-71891