Domain-General Tutor Authoring with Apprentice Learner Models

Abstract

Intelligent tutoring systems are effective for improving students’ learning outcomes (Pane et al. 2013; Koedinger and Anderson, International Journal of Artificial Intelligence in Education, 8, 1–14, 1997; Bowen et al. Journal of Policy Analysis and Management, 1, 94–111 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray 2003; Murray, International Journal of Artificial Intelligence in Education, 10, 98–129, 1999). In this paper, we explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, for authoring expert-models via demonstrations and feedback (Matsuda et al. International Journal of Artificial Intelligence in Education, 25(1), 1–34 2014) across a wide range of domains. To support these investigations, we present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning. We use this architecture to create two models: the Decision Tree model, which non-incrementally learns skills, and the Trestle model, which instead learns incrementally. Both models draw on the same small set of prior knowledge (six operators and three types of relational knowledge) to support expert model authoring. Despite their limited prior knowledge, we demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of learning an expert model for seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). This work shows that apprentice learner models are efficient for authoring tutors that would be difficult to build with existing non-programmer authoring approaches (e.g., experimental design or stoichiometry tutors). Further, we show that these models can be applied to author tutors across eight tutor domains even though they only have a small, fixed set of prior knowledge. This work lays the foundation for new interactive machine-learning based authoring paradigms that empower teachers and other non-programmers to build pedagogically effective educational technologies at scale.

The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis

“…Institutions considering the use of online proctoring technologies will find assistance in [23,24] demonstrates the amazing accuracy of AI in predicting student grades. The section [25] discusses the development of educational tutors that may be deployed by anyone with no programming experience. Reference assesses the effectiveness of an I-MIB undergraduate evaluation methodology.…”

“…Coghlan [23] study on the implications of Online Proctoring (OP) technology on stakeholders and ethical issues Expanding algorithm parameterization and incorporating demographic data are provided for future research in [24]. Using apprentice learning paradigms, Maclellan [25] explores teacher development and presents a unique structure for producing instructors of experimental design. The section [26] discusses the use of data mining to evaluate student performance and improve curriculum quality.…”

Configurable Intelligent Design Based on Hierarchical Imitation Models

“…Developing effective learning systems is a well-known challenge. Paper [73] explores the use of computational models of student learning to create expert models across various domains. The paper introduces a learner-learner architecture that defines the components required for learner learning, resulting in decision tree and trestle models.…”

The Intertwined Histories of Artificial Intelligence and Education

“…At least from the titles, I could identify almost no papers that tackle the kind of interdisciplinary approach to human and machine learning that has been the focus of this historical review. One possible exception is a paper on using Apprentice Learner models for interactively creating tutoring systems using feedback and demonstrations (MacLellan & Koedinger, 2022). While this paper still has an applied focus-more efficient ITS authoring-work on Apprentice Learner models in general (MacLellan, et al, 2016), and earlier work on SimStudent (Li et al, 2015;Matsuda et al, 2013), draw on the cognitive science tradition of using computational models of learning to develop insights on how people learn and to make practical changes in educational technology.…”