0 Synthesizing Optimal Domain Models for Educational Applications

Many applications in educational technology, from student modeling to problem generation, are built on a formal model of the operational knowledge for a given domain. These domain models consist of rules that experts apply to solve problems in the domain; for example, factoring, ax + bx → (a + b)x , is one such rule for K-12 algebra. In practice, domain models are handcrafted at great expense, so applications rely on a single generic model. But many models can capture the operational knowledge for a domain, and they differ in how well they meet specific educational objectives (such as maximizing problem-solving efficiency for advanced users or minimizing cognitive load for novices). Rapid creation of custom domain models is thus a key challenge in the development of personalized educational tools that adapt to their user’s needs. This paper presents RuleSy, a new framework for computer-aided authoring of optimal domain models for educational applications. RuleSy takes as input a set of example problems (e.g., x + 1 = 2), a set of basic axiom rules for solving these problems (e.g., factoring), and a function expressing the desired educational objective. Given these inputs, it first synthesizes a set of tactic rules (e.g., combining like terms) that integrate multiple axioms into advanced problem-solving strategies. The axioms and tactics are then searched for a domain model that optimizes the objective. RuleSy is based on new algorithms for mining tactic specifications from examples and axioms, synthesizing tactic rules from these specifications, and selecting an optimal domain model from the axioms and tactics. We evaluate RuleSy on the domain of K-12 algebra, finding that it recovers textbook tactics and domain models, discovers new tactics and models, and outperforms a prior tool for this domain by orders of magnitude. But RuleSy generalizes beyond K-12 algebra: we also use it to (re)discover proof tactics for propositional logic, demonstrating its potential to aid in the development of custom models for a variety of educational domains.

[1]  Derek C. Oppen,et al.  Reasoning about recursively defined data structures , 1978, POPL.

[2]  Richard E. Korf,et al.  Macro-Operators: A Weak Method for Learning , 1985, Artif. Intell..

[3]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[4]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[5]  Robin Milner,et al.  Handbook of Theoretical Computer Science (Vol. B) , 1990 .

[6]  K. VanLehn Mind Bugs: The Origins of Procedural Misconceptions , 1990 .

[7]  John R. Anderson,et al.  Cognitive Tutors: Lessons Learned , 1995 .

[8]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[9]  Tom Murray,et al.  Authoring Intelligent Tutoring Systems: An analysis of the state of the art , 1999 .

[10]  Neil T. Heffernan,et al.  Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[11]  Kenneth R. Koedinger,et al.  Applying Programming by Demonstration in an Intelligent Authoring Tool for Cognitive Tutors , 2005 .

[12]  Sanjit A. Seshia,et al.  Combinatorial sketching for finite programs , 2006, ASPLOS XII.

[13]  Cesare Tinelli,et al.  An Abstract Decision Procedure for Satisfiability in the Theory of Recursive Data Types , 2007, PDPAR/PaUL@FLoC.

[14]  Zohar Manna,et al.  The calculus of computation - decision procedures with applications to verification , 2007 .

[15]  C. R. Ramakrishnan,et al.  Tools and Algorithms for the Construction and Analysis of Systems, 14th International Conference, TACAS 2008, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2008, Budapest, Hungary, March 29-April 6, 2008. Proceedings , 2008, TACAS.

[16]  Pat Langley,et al.  Cognitive architectures: Research issues and challenges , 2009, Cognitive Systems Research.

[17]  Viktor Kuncak,et al.  Decision procedures for algebraic data types with abstractions , 2010, POPL '10.

[18]  G. Harel,et al.  Current Contributions Toward Comprehensive Perspectives on the Learning and Teaching of Proof , 2010 .

[19]  Michael I. Jordan,et al.  Learning Programs: A Hierarchical Bayesian Approach , 2010, ICML.

[20]  K. VanLehn The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems , 2011 .

[21]  Ute Schmid,et al.  Inductive rule learning on the knowledge level , 2011, Cognitive Systems Research.

[22]  Kenneth R. Koedinger,et al.  A Machine Learning Approach for Automatic Student Model Discovery , 2011, EDM.

[23]  Kenneth R. Koedinger,et al.  Efficient Complex Skill Acquisition Through Representation Learning , 2012 .

[24]  Ryan Shaun Joazeiro de Baker,et al.  New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization , 2013, AI Mag..

[25]  Sumit Gulwani,et al.  A trace-based framework for analyzing and synthesizing educational progressions , 2013, CHI.

[26]  Sumit Gulwani,et al.  Automated feedback generation for introductory programming assignments , 2013, PLDI.

[27]  Rajeev Alur,et al.  Syntax-guided synthesis , 2013, 2013 Formal Methods in Computer-Aided Design.

[28]  Emina Torlak,et al.  A lightweight symbolic virtual machine for solver-aided host languages , 2014, PLDI.

[29]  Sumit Gulwani,et al.  Example-based learning in computer-aided STEM education , 2014, CACM.

[30]  Nikolai Tillmann,et al.  Constructing coding duels in Pex4Fun and code hunt , 2014, ISSTA 2014.

[31]  Ivan Bratko,et al.  Data-Driven Program Synthesis for Hint Generation in Programming Tutors , 2014, Intelligent Tutoring Systems.

[32]  Sumit Gulwani,et al.  A Framework for Automatically Generating Interactive Instructional Scaffolding , 2015, CHI.

[33]  Sumit Gulwani,et al.  FlashMeta: a framework for inductive program synthesis , 2015, OOPSLA.

[34]  Yun-En Liu,et al.  Large-Scale Educational Campaigns , 2015, ACM Trans. Comput. Hum. Interact..

[35]  Emina Torlak,et al.  A Framework for Parameterized Design of Rule Systems Applied to Algebra , 2016, ITS.