Using dialogue to learn math in the LeActiveMath project

We describe a tutorial dialogue system under development that assists students in learning how to differentiate equations. The system uses deep natural language understanding and generation to both interpret students ’ utterances and automatically generate a response that is both mathematically correct and adapted pedagogically and linguistically to the local dialogue context. A domain reasoner provides the necessary knowledge about how students should approach math problems as well as their (in)correctness, while a dialogue manager directs pedagogical strategies and keeps track of what needs to be done to keep the dialogue moving along.

Natural‐language processing applied to an ITS interface

The aim of this paper is to show that with a subset of a natural language, simple systems running on PCs can be developed that can nevertheless be an effective tool for interfacing purposes in the building of an Intelligent Tutoring System (ITS). After presenting the special characteristics of the Smalltalk/V language, which provides an appropriate environment for the development of an interface, the overall architecture of the interface module is discussed. We then show how sentences are parsed by the interface, and how interaction takes place with the user. The knowledge‐acquisition phase is subsequently described. Finally, some excerpts from a tutoring session concerned with elementary geometry are discussed, and some of the problems and limitations of the approach are illustrated

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora

The effectiveness of using intelligent tutoring systems to increase student achievement

Intelligent Tutoring Systems could be used to provide differentiated instruction. This review examines qualities of Intelligent Tutoring Systems and their impact on student achievement. Thirty peer-reviewed research studies published from 1997 to 2019 were selected for analysis. This review considers how intelligent tutoring systems compare with other methods of instruction, and how an intelligent tutoring system’s on-screen tutor impacts student achievement. Finally, this review considers methods of ITS personalization and how those methods impact student achievement. The reviewed research studies indicated that ITS was more effective than all forms of instruction except small group and individualized instruction. Additionally, on-screen agents in and personalization of Intelligent Tutoring Systems often have a positive impact on student learning. Recommendations for classroom implementation of intelligent tutoring systems and suggestions for future research are discusse

WoZ Pilot Experiment for Empathic Robotic Tutors: Opportunities and Challenges

We discuss the challenges and opportunities in building empathic robotic tutors based on a preliminary Wizard-of-Oz (WoZ) pilot study. From the data collected in this study, we identify situations where empathy in a robotic tutor could have helped the conversation between the learner and the tutor. The video presented with this paper captures these situations where two children participants are interacting with a map application and a robot tutor operated by a wizard

Building Intelligent Tutoring Systems

This project\u27s goal was to improve the ASSISTments intelligent tutoring system\u27s algebraic capabilities. We worked towards three main objectives. First, we built support for parsing expressions and comparing them for algebraic equality. Second, we implemented an interactive grapher capable of plotting a variety of expressions. Third, we added support for rendering expressions to well formatted images. Finally, we implemented a basic tutoring system including sample problems that demonstrate our work, establishing our tools\u27 usability and integrability

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)