The desktop interface in intelligent tutoring systems

The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine

Possibilistic networks for uncertainty knowledge processing in student diagnosis

In this paper, a possibilistic network implementation for uncertain knowledge modeling of the diagnostic process is proposed as a means to achieve student diagnosis in intelligent tutoring system. This approach is proposed in the object oriented programming domain for diagnosis of students learning errors and misconception. In this expertise domain dependencies between data exist that are encoded in the structure of network. Also, it is available qualitative information about these data which are represented and interpreted with qualitative approach of possibility theory. The aim of student diagnosis system is to ensure an adapted support for the student and to sustain the student in personalized learning process and errors explanation

Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain

Language acquisition and machine learning

In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition

Learning Pedagogical Policies from Few Training Data

[Poster of] 17th European Conference on Artificial Intelligence (ECAI'06). Workshop on Planning, Learning and Monitoring with Uncertainty and Dynamic Worlds, Riva del Garda, Italy, August 8, 2006Learning a pedagogical policy in an Adaptive Educational System (AIES) fits as a Reinforcement Learning (RL) problem. However, to learn pedagogical policies requires to acquire a huge amount of experience interacting with the students, so applying RL to the AIES from scratch is infeasible. In this paper we describe RLATES, an AIES that uses RL to learn an accurate pedagogical policy to teach a course of Data Base Design. To reduce the experience required to learn the pedagogical policy, we propose to use an initial value function learned with simulated students, whose model is provided by an expert as a Markov Decision Process. Empirical results demonstrate that the value function learned with the simulated students and transferred to the AIES is a very accurate initial pedagogical policy. The evaluation is based on the interaction of more than 70 Computer Science undergraduate students, and demonstrates that an efficient guide through the contents of the educational system is obtained.This work was supported by the project GPS (TIN2004/07083

Intelligent tutoring systems for systems engineering methodologies

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype

Los desarrollos hipermedia y el aprendizaje de la resta

Los resultados de la investigación que presentamos demuestran que la influencia de los desarrollos hipermedia sobre el conocimiento formal y procedimental en el proceso de aprendizaje algorítmico son decisivos y favorecedores del mismo. Específicamente hemos estudiado este hecho en el algoritmo de la sustracción. A través de los datos que presentamos podemos concluir que la instrucción apoyada en una metodología didáctica con apoyo hipermedial actúa sobre el dominio conceptual que sustenta el aprendizaje del algoritmo. Del mismo modo, los resultados han permitido comprobar la inestabilidad del error y su transformación en otras tipologías

Los desarrollos hipermedia y el aprendizaje de la resta

[ES] Los resultados de la investigación que presentamos demuestran que la influencia de los desarrollos hipermedia sobre el conocimiento formal y procedimental en el proceso de aprendizaje algorítmico son decisivos y favorecedores del mismo. Específicamente hemos estudiado este hecho en el algoritmo de la sustracción. A través de los datos que presentamos podemos concluir que la instrucción apoyada en una metodología didáctica con apoyo hipermedial actúa sobre el dominio conceptual que sustenta el aprendizaje del algoritmo. Del mismo modo, los resultados han permitido comprobar la inestabilidad del error y su transformación en otras tipologías,[EN] The results of the investigation that we present demonstrate that the influence of the developments hipermedia on the formal and procedural knowledge in the process of learning algorithmic is decisive. Specifically we have studied this fact in the algorithm of the subtraction. Through the data that we present we can conclude that the instruction supported in a didactic methodology with support hipermedial would have acted on the conceptual domain that sustains the learning of the subtractions. In the same way, the results have allowed to check the uncertainty of the error and their transformation in other errors.[PO] Os resultados da pesquisa que apresentamos demonstram que a influência dos desenvolvimentos hipermídia sobre o conhecimento formal e procedimental no processo da aprendizagem algorítmica são decisivos e favorecedores do mesmo. Especificamente estudamos este fato no algoritmo da subtração. Através dos dados que apresentamos podemos concluir que a instrução apoiada numa metodologia didática com apoio hipermidial atua sobre o domínio conceitual que sustenta a aprendizagem do algoritmo. Do mesmo modo, os resultados permitiram comprovar a instabilidade do erro e sua transformação em outras tipologias