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1. Automated Extraction of Domain Models from Textbook Indexes for Developing Intelligent Tutoring Systems

   https://doi.org/10.1007/978-3-031-32883-1_11  

   Banjade, R.; Oli, P.; Rus, V. (May 2023, Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891)

   Frasson, C.; Mylonas, P.; Troussas, C. (Ed.)

   Domain modeling is an important task in designing, developing, and deploying intelligent tutoring systems and other adaptive instructional systems. We focus here on the more specific task of automatically extracting a domain model from textbooks. In particular, this paper explores using multiple textbook indexes to extract a domain model for computer programming. Our approach is based on the observation that different experts, i.e., authors of intro-to-programming textbooks in our case, break down a domain in slightly different ways, and identifying the commonalities and differences can be very revealing. To this end, we present automated approaches to extracting domain models from multiple textbooks and compare the resulting common domain model with a domain model created by experts. Specifically, we use approximate string-matching approaches to increase coverage of the resulting domain model and majority voting across different textbooks to discover common domain terms related to computer programming. Our results indicate that using approximate string matching gives more accurate domain models for computer programming with increased precision and recall. By automating our approach, we can significantly reduce the time and effort required to construct high-quality domain models, making it easy to develop and deploy tutoring systems. Furthermore, we obtain a common domain model that can serve as a benchmark or skeleton that can be used broadly and adapted to specific needs by others. 

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2. An Interaction Design for Machine Teaching to Develop AI Tutors

   https://doi.org/10.1145/3313831.3376226  

   Weitekamp, Daniel; Harpstead, Erik; Koedinger, Ken R. (April 2020, CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   Intelligent tutoring systems (ITSs) have consistently been shown to improve the educational outcomes of students when used alone or combined with traditional instruction. However, building an ITS is a time-consuming process which requires specialized knowledge of existing tools. Extant authoring methods, including the Cognitive Tutor Authoring Tools' (CTAT) example-tracing method and SimStudent's Authoring by Tutoring, use programming-by-demonstration to allow authors to build ITSs more quickly than they could by hand programming with model-tracing. Yet these methods still suffer from long authoring times or difficulty creating complete models. In this study, we demonstrate that Simulated Learners built with the Apprentice Learner (AL) Framework can be combined with a novel interaction design that emphasizes model transparency, input flexibility, and problem solving control to enable authors to achieve greater model completeness in less time than existing authoring methods. 

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3. Preliminary Experiments with Transformer based Approaches To Automatically Inferring Domain Models from Textbooks

   Banjade, Rabin; Oli, Priti; Tamang, Lasang Jimba; Rus, Vasile (July 2022, Proceedings of the 15th International Conference on Educational Data Mining)

   Domain modeling is a central component in education technologies as it represents the target domain students are supposed to train on and eventually master. Automatically generating domain models can lead to substantial cost and scalability benefits. Automatically extracting key concepts or knowledge components from, for instance, textbooks can enable the development of automatic or semi-automatic processes for creating domain models. We explore in this work the use of transformer based pre-trained models for the task of keyphrase extraction. Specifically, we investigate and evaluate four different variants of BERT, a pre-trained transformer based architecture, that vary in terms of training data, training objective, or training strategy to extract knowledge components from textbooks for the domain of intro-to-programming. We report results obtained using the following BERT-based models: BERT, CodeBERT, SciBERT and RoBERTa. 

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4. Preliminary Experiments with Transformer based Approaches To Automatically Inferring Domain Models from Textbooks

   https://doi.org/10.5281/zenodo.6853051  

   Rabin Banjade; Priti Oli; Lasang Jimba Tamang; Vasile Rus (July 2022, Proceedings of the 15th International Conference on Educational Data Mining)

   Antonija Mitrovic and Nigel Bosch (Ed.)

   Domain modeling is a central component in education technologies as it represents the target domain students are supposed to train on and eventually master. Automatically generating domain models can lead to substantial cost and scalability benefits. Automatically extracting key concepts or knowledge components from, for instance, textbooks can enable the development of automatic or semi-automatic processes for creating domain models. We explore in this work the use of transformer based pre-trained models for the task of keyphrase extraction. Specifically, we investigate and evaluate four different variants of BERT, a pre-trained transformer based architecture, that vary in terms of training data, training objective, or training strategy to extract knowledge components from textbooks for the domain of intro-to-programming. We report results obtained using the following BERT-based models: BERT, CodeBERT, SciBERT and RoBERTa. 

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5. Leveraging Deep Reinforcement Learning for Metacognitive Interventions Across Intelligent Tutoring Systems.

   Abdelshiheed, M; Hostetter, J W; Barnes, T; Chi, M (July 2023, Springer)

   Intelligent Tutoring Systems (ITSs) leverage AI to adapt to individual students, and many ITSs employ \emph{pedagogical policies} to decide what instructional action to take next in the face of alternatives. A number of researchers applied Reinforcement Learning (RL) and Deep RL (DRL) to induce effective pedagogical policies. Much of prior work, however, has been developed \emph{independently} for a specific ITS and \emph{cannot directly be applied to another}. In this work, we propose a \textbf{M}ulti-\textbf{T}ask \textbf{L}earning framework that combines Deep \textbf{BI}simulation \textbf{M}etrics and DRL, named \textbf{MTL-BIM}, to induce a unified pedagogical policies for two different ITSs across different domains: logic and probability. Based on empirical classroom results, our unified RL policy performed significantly better than the expert-crafted policies and independently induced DQN policies on both ITSs. 

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