Vượt ra ngoài độ chính xác nhị phân: Phân loại các câu trả lời của sinh viên trong hệ thống học tập

Aleven, V., Koedinger, K.R.: Limitations of student control: do students know when they need help?. In: Proceedings of Intelligent Tutoring Systems, pp. 292–303. Springer, Berlin (2000) Aleven, V., Stahl, E., Schworm, S., Fischer, F., Wallace, R.: Help seeking and help design in interactive learning environments. Rev. Educ. Res. 73(3), 277–320 (2003) Aleven, V., McLaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. In: Mayer, R.E., Alexander, P.A. (eds.) Handbook of Research on Learning and Instruction, pp. 522–559. Routledge, Abingdon (2016) Arroyo, I., Woolf, B.P., Burelson, W., Muldner, K., Rai, D., Tai, M.: A multimedia adaptive tutoring system for mathematics that addresses cognition, metacognition and affect. Int. J. Artif. Intell. Educ. 24(4), 387–426 (2014) Baker, R.S.: Modeling and understanding students’ off-task behavior in intelligent tutoring systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1059–1068. ACM (2007) Baker, R.S.: Stupid tutoring systems, intelligent humans. Int. J. Artif. Intell. Educ. 26(2), 600–614 (2016) Baker, R.S., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. In: Proceedings of Intelligent Tutoring Systems, pp. 406–415. Springer (2008) Baker, R.S., Gowda, S.M., Wixon, M., Kalka, J., Wagner, A.Z., Salvi, A., Aleven, V., Kusbit, G.W., Ocumpaugh, J., Rossi, L.: Towards sensor-free affect detection in cognitive tutor algebra. In: Proceedings of Educational Data Mining (2012) Barnes, T.: The q-matrix method: Mining student response data for knowledge. In: American Association for Artificial Intelligence 2005 Educational Data Mining Workshop, pp. 1–8 (2005) Beck, J.E.: Engagement tracing: using response times to model student disengagement. In: Proceedings of Artificial Intelligence in Education, vol. 125 (2005) Beck, J.E., Gong, Y.: Wheel-spinning: students who fail to master a skill. In: Proceedings of Artificial Intelligence in Education, pp. 431–440. Springer (2013) Beck, J.E., Chang, K.-m., Mostow, J., Corbett, A.: Does help help? Introducing the Bayesian evaluation and assessment methodology. In: Proceedings of Intelligent Tutoring Systems, pp. 383–394. Springer (2008) Bull, S., Kay, J.: Student models that invite the learner in: the smili open learner modelling framework. Int. J. Artif. Intell. Educ. 17(2), 89–120 (2007) Burrows, S., Gurevych, I., Stein, B.: The eras and trends of automatic short answer grading. Int. J. Artif. Intell. Educ. 25(1), 60–117 (2015) Butler, A.C., Karpicke, J.D., Roediger III, H.L.: The effect of type and timing of feedback on learning from multiple-choice tests. J. Exp. Psychol. Appl. 13(4), 273 (2007) Desmarais, M.C., Baker, R.S.: A review of recent advances in learner and skill modeling in intelligent learning environments. User Model. User-Adap. Interact. 22(1–2), 9–38 (2012) Desmarais, M., Beheshti, B., Xu, P.: The refinement of a q-matrix: assessing methods to validate tasks to skills mapping. In: Proceedings of Educational Data Mining (2014) Drasgow, F., Levine, M.V., Tsien, S., Williams, B., Mead, A.D.: Fitting polytomous item response theory models to multiple-choice tests. Appl. Psychol. Meas. 19(2), 143–166 (1995) Eagle, M., Hicks, D., Peddycord III, B., Barnes, T.: Exploring networks of problem-solving interactions. In: Proceedings of Learning Analytics and Knowledge, pp. 21–30. ACM (2015) Effenberger, T., Pelánek, R.: Towards making block-based programming activities adaptive. In: Proceedings of Learning at Scale, p. 13. ACM (2018) Effenberger, T., Pelánek, R.: Measuring students’ performance on programming tasks. In: Proceedings of Learning at Scale. ACM (2019) Gierl, M.J., Bulut, O., Guo, Q., Zhang, X.: Developing, analyzing, and using distractors for multiple-choice tests in education: a comprehensive review. Rev. Educ. Res. 87(6), 1082–1116 (2017) Goldin, I., Koedinger, K., Aleven, V.: Hints: you can’t have just one. In: Proceedings of Educational Data Mining (2013) Hattie, J., Gan, M.: Instruction based on feedback. In: Mayer, R.E., Alexander, P.A. (eds.) Handbook of Research on Learning and Instruction, pp. 249–271. Routledge, Abingdon (2011) Inventado, P.S., Scupelli, P., Ostrow, K., Heffernan, N., Ocumpaugh, J., Almeda, V., Slater, S.: Contextual factors affecting hint utility. Int. J. STEM Educ. 5(1), 13 (2018) Klinkenberg, S., Straatemeier, M., Van der Maas, H.: Computer adaptive practice of maths ability using a new item response model for on the fly ability and difficulty estimation. Comput. Educ. 57(2), 1813–1824 (2011) Masters, G.N.: A rasch model for partial credit scoring. Psychometrika 47(2), 149–174 (1982) McTavish, T.S., Larusson, J.A.: Labeling mathematical errors to reveal cognitive states. In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds.) Open Learning and Teaching in Educational Communities, pp. 446–451. Springer, Cham (2014) Merceron, A., Yacef, K.: Clustering students to help evaluate learning. In: Courtiat, J.P., Davarakis, C., Villemur, T. (eds.) Technology Enhanced Learning, pp. 31–42. Springer, Boston (2005) Mettler, E., Massey, C.M., Kellman, P.J.: Improving adaptive learning technology through the use of response times. In: Proceedings of Conference of the Cognitive Science Society, pp. 2532–2537 (2011) Molenaar, I., Knoop-van Campen, C.: Teacher dashboards in practice: usage and impact. In: Proceedings of European Conference on Technology Enhanced Learning, pp. 125–138. Springer (2017) Nam, S., Frishkoff, G.A., Collins-Thompson, K.: Predicting short-and long-term vocabulary learning via semantic features of partial word knowledge. In: Proceedings of Educational Data Mining (2017) Ostrow, K., Donnelly, C., Adjei, S., Heffernan, N.: Improving student modeling through partial credit and problem difficulty. In: Proceedings of Learning at Scale, pp. 11–20. ACM (2015) Papoušek, J., Pelánek, R., Řihák, J., Stanislav, V.: An analysis of response times in adaptive practice of geography facts. In: Proceedings of Educational Data Mining, pp. 562–563 (2015) Paquette, L., Baker, R.S., Sao Pedro, M.A., Gobert, J.D., Rossi, L., Nakama, A., Kauffman-Rogoff, Z.: Sensor-free affect detection for a simulation-based science inquiry learning environment. In: International Conference on Intelligent Tutoring Systems, pp. 1–10. Springer (2014) Pelánek, R.: Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. User Model. User-Adap. Interact. 27(3), 313–350 (2017) Pelánek, R.: The details matter: methodological nuances in the evaluation of student models. User Model. User-Adap. Interact. 28, 207–235 (2018a) Pelánek, R.: Exploring the utility of response times and wrong answers for adaptive learning. In: Proceedings of Learning at Scale. ACM (2018b) Pelánek, R.: Measuring similarity of educational items: an overview. IEEE Trans. Learn. Technol. (2019). https://doi.org/10.1109/TLT.2019.2896086 Pelánek, R., Jarušek, P.: Student modeling based on problem solving times. Int. J. Artif. Intell. Educ. 25(4), 493–519 (2015) Pelánek, R., Řihák, J.: Properties and applications of wrong answers in online educational systems. In: Proceedings of Educational Data Mining, pp. 466–471 (2016) Pelánek, R., Řihák, J.: Analysis and design of mastery learning criteria. New Rev. Hypermed. Multimed. 24, 133–159 (2018) Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L.J., Sohl-Dickstein, J.: Deep knowledge tracing. In: Proceedings of Advances in neural Information Processing Systems, pp. 505–513 (2015a) Piech, C., Huang, J., Nguyen, A., Phulsuksombati, M., Sahami, M., Guibas, L.: Learning program embeddings to propagate feedback on student code. In: Proceedings of International Conference on Machine Learning, volume 37 of ICML’15, pp. 1093–1102 (2015b) Řihák, J., Pelánek, R.: Choosing a student model for a real world application. In: Proceedings of Building ITS Bridges Across Frontiers (ITS Workshop) (2016) Shih, B., Koedinger, K.R., Scheines, R.: A response time model for bottom-out hints as worked examples. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R.S. (eds.) Handbook of Educational Data Mining, pp. 201–212. CRC Press, Boca Raton (2011) Stephens-Martinez, K., Ju, A., Parashar, K., Ongowarsito, R., Jain, N., Venkat, S., Fox, A.: Taking advantage of scale by analyzing frequent constructed-response, code tracing wrong answers. In: Proceedings of International Computing Education Research, pp. 56–64. ACM (2017) Straatemeier, M.: Math Garden: A new educational and scientific instrument. PhD thesis, Universiteit van Amsterdam, Faculty of Social and Behavioural Sciences (2014) Van Der Linden, W.: Conceptual issues in response-time modeling. J. Educ. Meas. 46(3), 247–272 (2009) Van Inwegen, E.G., Adjei, S.A., Wang, Y., Heffernan, N.T.: Using partial credit and response history to model user knowledge. In: Proceedings of Educational Data Mining (2015) Wang, Y., Heffernan, N.T., Heffernan, C.: Towards better affect detectors: effect of missing skills, class features and common wrong answers. In: Proceedings of Learning Analytics And Knowledge, pp. 31–35. ACM (2015) Wang, Y., Heffernan, N.: Extending knowledge tracing to allow partial credit: using continuous versus binary nodes. In: Proceedings of Artificial Intelligence in Education, pp. 181–188. Springer (2013) Wang, L., Sy, A., Liu, L., Piech, C.: Learning to represent student knowledge on programming exercises using deep learning. In: Proceedings of Educational Data Mining, pp. 324–329 (2017) Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. Int. J. Learn. Technol. 4(3–4), 129–164 (2009) Zhang, L., Xiong, X., Zhao, S., Botelho, A., Heffernan, N.T.: Incorporating rich features into deep knowledge tracing. In: Proceedings of Learning at Scale, pp. 169–172. ACM (2017)