ABSTRACT
Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. Intelligent tutoring systems can potentially facilitate this work for teachers by supporting the automatic adaptation of examples based on a student model of the induction process. However, current models of inductive reasoning usually lack two main characteristics helpful to adaptive learning environments, individual differences of students and tracing of students' learning as they receive feedback. In this paper, we describe a model to predict and simulate inductive reasoning of students for a categorization task. Our approach uses a Bayesian model for describing the reasoning processes of students. This model allows us to predict students' choices in categorization questions by accounting for their feature biases. Using data gathered from 222 students categorizing three topics, we find that our model has a 75% accuracy, which is 10% greater than a baseline model. Our model is a contribution to learning analytics by enabling us to assign different bias profiles to individual students and tracking these profile changes over time through which we can gain a better understanding of students' learning processes. This model may be relevant for systematically analysing students' differences and evolution in inductive reasoning strategies while supporting the design of adaptive inductive learning environments.
- John R Anderson, C Franklin Boyle, and Brian J Reiser. 1985. Intelligent tutoring systems. Science 228, 4698 (1985), 456--462.Google Scholar
- Alejandro Bogarín, Rebeca Cerezo, and Cristóbal Romero. 2018. A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8, 1 (2018), e1230.Google ScholarCross Ref
- Hao Cen, Kenneth Koedinger, and Brian Junker. 2006. Learning factors analysis-a general method for cognitive model evaluation and improvement. In International Conference on Intelligent Tutoring Systems. Springer, 164--175.Google ScholarDigital Library
- Incheol Choi, Richard E Nisbett, and Edward E Smith. 1997. Culture, category salience, and inductive reasoning. Cognition 65, 1 (1997), 15--32.Google ScholarCross Ref
- Constantinos Christou and Eleni Papageorgiou. 2007. A framework of mathematics inductive reasoning. Learning and Instruction 17, 1 (2007), 55--66.Google ScholarCross Ref
- John Coley, Patrick Shafto, Olga Stepanova, and Elizabeth Baraff. 2005. Knowledge and Category-Based Induction. (2005).Google Scholar
- Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 4 (1994), 253--278.Google Scholar
- Sharon J Derry, Cindy E Hmelo-Silver, Anandi Nagarajan, Ellina Chernobilsky, and Brian D Beitzel. 2006. Cognitive transfer revisited: Can we exploit new media to solve old problems on a large scale? Journal of Educational Computing Research 35, 2 (2006), 145--162.Google ScholarCross Ref
- Nicholas Diana, John Stamper, and Ken Koedinger. 2018. An instructional factors analysis of an online logical fallacy tutoring system. In International Conference on Artificial Intelligence in Education. Springer, 86--97.Google ScholarCross Ref
- Louis Faucon, Lukasz Kidzinski, and Pierre Dillenbourg. 2016. Semi-Markov Model for Simulating MOOC Students. International Educational Data Mining Society (2016).Google Scholar
- JHM Hamers, E De Koning, and K Sijtsma. 1998. Inductive reasoning in third grade: Intervention promises and constraints. Contemporary Educational Psychology 23, 2 (1998), 132--148.Google ScholarCross Ref
- Brett K Hayes and Ben R Newell. 2009. Induction with uncertain categories: When do people consider the category alternatives? Memory & Cognition 37, 6 (2009), 730--743.Google ScholarCross Ref
- Evan Heit. 1997. Features of similarity and category-based induction. In An Interdisciplinary Workshop On Similarity And Categorisation (SimCat). Edinburgh, UK.Google Scholar
- Evan Heit. 1998. A Bayesian analysis of some forms of inductive reasoning. Rational models of cognition (1998), 248--274.Google Scholar
- Cindy E Hmelo-Silver. 2004. Problem-based learning: What and how do students learn? Educational psychology review 16, 3 (2004), 235--266.Google Scholar
- Natasha G Holmes, James Day, Anthony HK Park, DA Bonn, and Ido Roll. 2014. Making the failure more productive: scaffolding the invention process to improve inquiry behaviors and outcomes in invention activities. Instructional Science 42, 4 (2014), 523--538.Google ScholarCross Ref
- Manu Kapur and Katerine Bielaczyc. 2012. Designing for productive failure. Journal of the Learning Sciences 21, 1 (2012), 45--83.Google ScholarCross Ref
- Tanja Käser, Severin Klingler, Alexander Gerhard Schwing, and Markus Gross. 2014. Beyond knowledge tracing: Modeling skill topologies with bayesian networks. In International Conference on Intelligent Tutoring Systems. Springer, 188--198.Google ScholarDigital Library
- Katharina Loibl, Ido Roll, and Nikol Rummel. 2017. Towards a theory of when and how problem solving followed by instruction supports learning. Educational Psychology Review 29, 4 (2017), 693--715.Google ScholarCross Ref
- Christopher J Maclellan, Erik Harpstead, Rony Patel, and Kenneth R Koedinger. 2016. The Apprentice Learner Architecture: Closing the Loop between Learning Theory and Educational Data. International Educational Data Mining Society (2016).Google Scholar
- Christopher J MacLellan, Kenneth R Koedinger, and Noboru Matsuda. 2014. Authoring tutors with SimStudent: An evaluation of efficiency and model quality. In International conference on intelligent tutoring systems. Springer, 551--560.Google ScholarDigital Library
- Douglas L Medin, John D Coley, Gert Storms, and Brett L Hayes. 2003. A relevance theory of induction. Psychonomic Bulletin & Review 10, 3 (2003), 517--532.Google ScholarCross Ref
- Eva Millán and José Luis Pérez-De-La-Cruz. 2002. A Bayesian diagnostic algorithm for student modeling and its evaluation. User Modeling and User-Adapted Interaction 12, 2--3 (2002), 281--330.Google ScholarDigital Library
- Andrew J Milson and Brian D Earle. 2008. Internet-based GIS in an inductive learning environment: A case study of ninth-grade geography students. Journal of geography 106, 6 (2008), 227--237.Google ScholarCross Ref
- Todd K Moon. 1996. The expectation-maximization algorithm. IEEE Signal processing magazine 13, 6 (1996), 47--60.Google ScholarCross Ref
- Daniel N Osherson, Edward E Smith, Ormond Wilkie, Alejandro Lopez, and Eldar Shafir. 1990. Category-based induction. Psychological review 97, 2 (1990), 185.Google Scholar
- Zachary Pardos and Neil Heffernan. 2010. Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm. In Educational Data Mining 2010.Google Scholar
- Amy Perfors, Joshua B Tenenbaum, Thomas L Griffiths, and Fei Xu. 2011. A tutorial introduction to Bayesian models of cognitive development. Cognition 120, 3 (2011), 302--321.Google ScholarCross Ref
- Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In Advances in neural information processing systems. 505--513.Google Scholar
- Michael J Prince and Richard M Felder. 2006. Inductive teaching and learning methods: Definitions, comparisons, and research bases. Journal of engineering education 95, 2 (2006), 123--138.Google ScholarCross Ref
- Anna N Rafferty, Emma Brunskill, Thomas L Griffiths, and Patrick Shafto. 2016. Faster teaching via pomdp planning. Cognitive science 40, 6 (2016), 1290--1332.Google Scholar
- Daniel L Schwartz and Taylor Martin. 2004. Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction 22, 2 (2004), 129--184.Google ScholarCross Ref
- Linda B Smith, Susan S Jones, Barbara Landau, Lisa Gershkoff-Stowe, and Larissa Samuelson. 2002. Object name learning provides on-the-job training for attention. Psychological science 13, 1 (2002), 13--19.Google Scholar
- Joshua B Tenenbaum. 1999. Bayesian modeling of human concept learning. In Advances in neural information processing systems. 59--68.Google Scholar
- Joshua B Tenenbaum, Charles Kemp, Thomas L Griffiths, and Noah D Goodman. 2011. How to grow a mind: Statistics, structure, and abstraction. science 331, 6022 (2011), 1279--1285.Google Scholar
- Amos Tversky. 1977. Features of similarity. Psychological review 84, 4 (1977), 327.Google Scholar
- Michael V Yudelson, Kenneth R Koedinger, and Geoffrey J Gordon. 2013. Individualized bayesian knowledge tracing models. In International conference on artificial intelligence in education. Springer, 171--180.Google ScholarCross Ref
Index Terms
A bayesian model of individual differences and flexibility in inductive reasoning for categorization of examples
Recommendations
Predicting Individual Differences for Learner Modeling in Intelligent Tutors from Previous Learner Activities
UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and PersonalizationThis study examines how accurately individual student differences in learning can be predicted from prior student learning activities. Bayesian Knowledge Tracing (BKT) predicts learner performance well and has often been employed to implement cognitive ...
Investigating Differences in Wiki-based Collaborative Activities between Student Engagement Profiles in CS1
SIGCSE '16: Proceedings of the 47th ACM Technical Symposium on Computing Science EducationIntroductory computer science courses are being increasingly taught using technology-mediated instruction and e-learning environments. The software and technology in such courses could benefit from the use of student models to inform and guide ...
Individual Differences and Joyful Assessment-Based Learning
ICALT '14: Proceedings of the 2014 IEEE 14th International Conference on Advanced Learning TechnologiesE-assessment learning, which combines the advantages of e-assessment and learning, was proposed in educational settings. However, it still makes most of learners feel anxious. Thus, this study develops a Joyful Assessment-based Learning System (JALS), ...
Comments