A Roadmap for Education Technology

Research reportThis report describes the initial findings of several workshops convened in 2009 to consider the future of education and in particular the role of technology and computer science in education. Through a series of facilitated collaborative workshops, leaders in several disciplines engaged in conversations that cast computers in the role of facilitating education in the future and recommended a research agenda for federal funding

Tracking Student Propositions in an Inquiry System

We built software to support student reasoning about a phenomenon and development of hypotheses to explain it. The goal is to engage students in asking questions, generating hypotheses and testing predictions. Rashi, an intelligent tutor, tracks students’ investigations (e.g., hypotheses, questions, data collection, and inferences) and helps articulate how evidence and theories are related. The tutor provides advice, such as recognizing when data does not support a hypothesis Cases are presented in geology, biology or engineering, and students are scaffolded to use an inquiry-based approach to posit a theory to explain the situation. Generic and reusable structured tools guide students through exploration of ill-structured problem spaces, supporting student knowledge and scaffolding reasoning and diagnostic skills

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

Features of Intelligent Tutoring Systems

This slide deck was presented on October 26, 2017 as part of a National Academy of Sciences workshop on the role of digital tutors. This workshop, titled The Role of Digital Tutors, "describe[s] the evidence on the efficacy of Digital Tutors, exploring our current knowledge about the capabilities and applicability of this technology for use in day-to-day teaching and learning at all levels." The workshop also discussed "strategies and policies that might be used to implement digital tutoring more widely and to assess its impact at much larger scales." This presentation was from Panel I: Design, Development, and Effectiveness of Digital Tutors. The following topics are covered: motivation, emotion in learning, collaboration, intelligent training, and big data for education

Building intelligent interactive tutors: student-centered strategies for revolutionizing e-learning

Excerpts available on Google Books (see link below). For more info, go to publisher's website : http://www.sciencedirect.com/science/book/9780123735942#ancp1Computers have transformed every facet of our culture, most dramatically communication, transportation, finance, science, and the economy. Yet their impact has not been generally felt in education due to lack of hardware, teacher training, and sophisticated software. Another reason is that current instructional software is neither truly responsive to student needs nor flexible enough to emulate teaching. The more instructional software can reason about its own teaching process, know what it is teaching, and which method to use for teaching, the greater is its impact on education. Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a student's learning needs. Dr. Woolf taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible, whether for classroom or life-long learning. The book describes multidisciplinary approaches to using computers for teaching, reports on research, development, and real-world experiences, and discusses intelligent tutors, web-based learning systems, adaptive learning systems, intelligent agents and intelligent multimedia. (http://books.google.fr/books?id=MnrUj3J_VuEC&printsec=frontcover&hl=fr#v=onepage&q&f=false

Knowledge-based tutors: An artificial intelligence approach to education

A vehicle is suggested for bringing information technology into education. Knowledge-based systems are proposed as a way to explore, reason about, and synthesize large knowledge bases. These systems utilize resources such as artificial intelligence, multimedia, and electronic communication to reason about what, with whom, and how they should teach in order to tailor knowledge and communication to individual students. Teaching material does not consist of a repertoire of prespecified responses; rather, reasoning about the student and the complexity of the subject matter informs the system\u27s response so that inferences made by the machine become key features of the system\u27s response. Currently, such systems can reason about a student\u27s presumed knowledge, can solve the problems given to the student, and can begin to recognize plausible student misconceptions. This document provides a practical hands-on guide for people who are considering building knowledge-based systems. It identifies the requisite resources, personnel, hardware and software and describes artificial intelligence methodologies and tools that might become available. The document is directed both at increased production of knowledge-based systems and also at improving the dialogue among computer scientists, educators, researchers, and classroom practitioners around the issue of information technology in the schools

CONTEXT DEPENDENT PLANNING IN A MACHINE TUTOR (ARTIFICIAL INTELLIGENCE, TEACHING SYSTEMS, MENO-TUTOR)

This thesis describes the Meno-tutor, a computer program for tutoring. The program adapts its discourse to the context of student and discourse history, i.e., it responds differently to the knowledgeable student and the confused one. The program uses knowledge about tutoring strategies, complex communication skills, and its ability to infer the level of the student\u27s knowledge to generate reasonable tutoring discourse. It can question a student about the subject domain, probe him for possible misconceptions, and can change its strategies if the student is not progressing well. The planning mechanism for the Meno-tutor is best described as a set of decision-making states organized into three levels. Each state provides a constraint or decision about the form and context of the utterance. The levels successively refine the actions of the previous level and constrain the pedagogical, strategic and tactical formation of the utterance. States are linked to other states by a structure which is nominally an OR graph, but makes two notable deviations. The first is a set of default transitions that represent crosslinks through the states and are responsible for the traditional sections of discourse, e.g., introduction of a topic, questioning the student about the topic, and terminating that topic. The second deviation is a set of meta-rules which functionally represent the shifts observed in classic human tutoring; they express the high-level transitions characteristic of, for example, a change in strategy. The path of the tutor can be preempted before entrance to any state by meta-rules. The Meno-tutor is an attempt at a generic tutor, one not committed by design to a single subject. It teaches about the causal reasoning behind rainfall and the looping constructs in the programming language PASCAL. In both subject areas it adjusts its response to its own inferences about the student\u27s level of knowledge and to the success of the discourse

Knowledge-based Environments for Teaching and Learning

The Spring Symposium on Knowledge-based Environments for Teaching and Learning focused on the use of technology to facilitate learning, training, teaching, counseling, coaxing and coaching. Sixty participants from academia and industry assessed progress made to date and speculated on new tools for building second generation systems. Selection of topics and participants was motivated by a desire for ideological breadth and depth. Panel leaders included William J. Clancey and Alan Lesgold (researchers of realworld systems); Kurt VanLehn (champion of cognitive models); Beverly Park Woolf (defender of discourse systems); Elliot Soloway (advocate for alternative environments); and Sarah Douglas (spokesperson for supportive systems)

Multi-agent Protocol Recognition during Simulation

Domain experts often express their knowledge in a different form than knowledge engineers would like. We implemented a plan recognition system within a simulation based tutor using a protocol formalism designed to closely resemble a form used by domain experts to communicate with each other. The system was responsible for using knowledge encoded in a linear formalism to implicitly recognize parallel activity. The recognition system supports recovery from incorrect user actions and accounts for synchronization of multiple agents and plans. The protocol mechanism was a component in a tutoring system based upon a real time simulation of cooperating agents following the user`s orders. A robust model of expert behavior was compared with the user's actions which were classified as correct, incorrect or partially correct for use in constructing a model of the user's understanding of the task being taught. The user model was indexed into the simulation model so that the state of the simulation..