Intelligent Tutoring Systems Research Research Articles

Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view

Intelligent tutoring systems (ITSs) are a promising integrated educational tool for customizing formal education using intelligent instruction or feedback. In recent decades, ITSs have transformed teaching and learning and associated research. This study examined the evolution and future trends of ITS research with scientometric methods. First, a dataset comprising 1173 relevant publications was compiled from the Web of Science Core Collection databases (including the Science Citation Index Expanded and the Social Science Citation Index). Then, the publication distributions by time, author, institution, country/region, and knowledge sources were analyzed to reveal the multidisciplinary integration paths. Dataset co-occurrence and co-citation analyses were conducted to identify the most popular research issues, the research chronology, and the emerging trends. It was found that: (a) ITS research has been growing in recent years. According to the Price literature exponential growth curve, this field is still in its initial stage while has high potential; (b) computer science, education, psychology, and engineering were the main ITS research knowledge sources, with ITS social science publications since 2007 being higher than ITS natural sciences publications; (c) interactive learning environments, student modeling, teaching/learning strategies, and machine learning have been the most popular research foci; and (d) the Coh-Metrix, problem-centered instruction, and STEM are the current research trends.

Intelligent Learning Support for Reusability Oriented Knowledge Organization in Programming Learning

Recently, the demand for programming education is increasing worldwide. Enhancing intelligent tutoringsystems (ITSs) in programming education is therefore very important. For a computer to intelligently support suchlearning, it is desirable that it be adaptive to individual learning. In ITS research, learning effectiveness is enhancedby (A) controlling features of the question or problem to be asked by indexing based on characteristics of targetdomains, or by (B) making appropriate interventions such as feedback by grasping problem-solving processes basedon explainable problem-solving models.

An agent-based intelligent tutoring systems review

This paper presents the current state of research in the fields of intelligent tutoring systems and intelligent agents. The architecture of intelligent tutoring systems typically consists of four major components: the domain knowledge module, the learner diagnosis module, the tutoring module, and the communication module. An agent-based approach to intelligent tutoring systems research and development is applied as the set of agents that constitute the general architecture, and as animated characters - pedagogical agents. Therefore, the paper focuses on the components of intelligent tutoring systems that can be implemented as agents, as well as on functions that these intelligent agents can perform in each intelligent tutoring system component. Also, we present the recent endeavours in the intelligent tutoring systems community that moves from standalone systems focused on a single instructional domain to adaptive instructional systems that comprise learners, intelligent tutoring systems, and external (non-adaptive) environment.

An Analysis of Student Model Portability

Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning Environments, i.e. their student models, tend to be more heterogeneous and complex than traditional models used in Recommender Systems. To share and reuse student models we must first understand the restrictions for porting or reusing student models in new ITSs or ILEs. This paper proposes a classification of student models in terms of their portability. Portability is measured via each model’s accessibility, complexity, architecture, popularity, and description. We use this classification to analyse and then grade student models that have been published in the AIED, EDM and ITS research communities in 2013 and 2014. The classification is intended to be used by researchers both as a methodology to measure the portability of a student model and as a guide to find existing reusable models.

SMILI☺: a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields

The SMILI☺ (Student Models that Invite the Learner In) Open Learner Model Framework was created to provide a coherent picture of the many and diverse forms of Open Learner Models (OLMs). The aim was for SMILI☺ to provide researchers with a systematic way to describe, compare and critique OLMs. We expected it to highlight those areas where there had been considerable OLM work, as well as those that had been neglected. However, we observed that SMILI☺ was not used in these ways. We now reflect on the reasons for this, and conclude that it has actually served a broader role in defining the notion of OLM and informing OLM design. Since the initial SMILI☺ paper, much has changed in technology-enhanced learning. Notably, learning technology has become far more pervasive, both in formal and lifelong learning. This provides huge, and still growing amounts of learning data. The fields of Learning Analytics (LA), Learning at Scale (L@S), Educational Data Mining (EDM) and Quantified Self (QS) have emerged. This paper argues that there has also been an important shift in the nature and role of learner models even within Artificial Intelligence in Education and Intelligent Tutoring Systems research. In light of these trends, and reflecting on the use of SMILI☺, this paper presents a revised and simpler version of SMILI☺ alongside the original version. In both cases there are additional categories to encompass new trends, which can be applied, omitted or substituted as required. We now offer this as a guide for designers of interfaces for OLMs, learning analytics and related fields, and we highlight the areas where there is need for more research.

Cognitive Demand of Model Tracing Tutor Tasks: Conceptualizing and Predicting How Deeply Students Engage

Model tracing tutors represent a technology designed to mimic key elements of one-on-one human tutoring. We examine the situations in which such supportive computer technologies may devolve into mindless student work with little conceptual understanding or student development. To analyze the support of student intellectual work in the model tracing tutor case, we adapt a cognitive demand framework that has been previously applied with success to teacher-guided mathematics classrooms. This framework is then tested against think-aloud data from students using a model tracing tutor designed to teach proportional reasoning skills in the context of robotics movement planning problems. Individual tutor tasks are coded for designed level of cognitive demand and compared to students’ enacted level of cognitive demand. In general, designed levels predicted how students enacted the tasks. However, just as in classrooms, student enactment was often at lower levels of demand than designed. Several contextual design features were associated with this decline. Implications for intelligent tutoring system design and research are discussed.

Multi-Agent Based Intelligent Tutoring System Source Code Generation Using MASITS Tool

Multi-Agent Based Intelligent Tutoring System Source Code Generation Using MASITS ToolSeveral agent development tools have been proposed. At the same time, specific tools for agent based Intelligent Tutoring System (ITS) development do not exist. However, ITSs have some specific characteristics that must be taken into consideration during the development. General purpose Agent Oriented Software Engineering (AOSE) methodologies and therefore development tools do not sufficiently correspond to the characteristics of ITSs. Additionally, the general purpose AOSE methodologies and tools do not allow plugging in domain specific rules and diagrams. Thus, usage of knowledge gained in ITS research during the development process is limited. In the absence of general tools that allow plugging in knowledge from ITS research, a specific tool named MASITS has been developed. The tool supports a specific agent based ITS development methodology named MASITS which takes into consideration specific characteristics of ITSs and integrates knowledge from ITS research. The tool supports all phases of ITS development, starting from requirements analysis and ending with deployment. Requirements analysis and design phases are supported by appropriate diagram creation tools. Implementation is supported by source code generation from diagrams created during the design phase. JADE platform is used for agent implementation. Thus, Java classes for ontology, agents and behaviours are generated from the design diagrams. The paper includes a brief overview of diagrams used in the source code generation and detailed algorithms for source code generation from the diagrams.

Requirements analysis of Multi-Agent Based Intelligent Tutoring Systems

Requirements analysis of Multi-Agent Based Intelligent Tutoring SystemsThe agent oriented software engineering research proposes general assumptions for agent oriented software development, while intelligent tutoring system (ITS) research proposes specific ITS architecture and other specific knowledge for ITS development. Both of these views should be taken into consideration while developing multi-agent based ITSs. Thus there is a need for specific approaches for all phases of agent based ITS development which take into consideration main ideas from both agent oriented software engineering and ITS research. In this paper we propose a requirements analysis approach for multi-agent based ITSs. A case study of a simple ITS is included, too. Requirements analysis in the proposed approach consist of two main steps, namely goal modelling and use case modelling. During the goal modelling the main goals of the system are identified and a goal hierarchy for the system is created. During the use case modelling use cases needed to achieve each lower level goal and their descriptions are created. The proposed approach of the requirements analysis is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The developed use case model (especially use case scenarios) is used during the agent interaction design and task definition. Goal hierarchy during the design phase is mainly used for checking, if the results of design achieve all system's goals.

Design of Multi-Agent Based Intelligent Tutoring Systems

Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the external design phase the behaviour of agents and interactions among them are designed. The following steps are done: task modelling and task allocation to agents, use case map creation, agent interaction design, ontology creation and holon design. During the external design phase agents and holons are defined according to the holonic multi-agent architecture for ITS development. During the internal design stage the internal structure of agents is specified. The internal structure of each agent is represented in the specific diagram, called internal view of the agent, consisting of agent's actions and interactions among them, rules for incoming message and perception processing, incoming and outgoing messages, and beliefs of the agent. The proposed approach is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The approach is developed using the same concepts as JADE agent platform and is suitable for agent code generation from the design diagrams.

An Intelligent Tutoring System (ITS) view on AOSE

The creation of Intelligent Tutoring System (ITS) applications involves tasks that require the modelling of complex agents (i.e., human students); therefore, a cognitive agency approach is more appropriate than a weak agency approach. ITS applications need to know, plan and understand several aspects of the other intelligent entities that inhabit the same environment. This is the viewpoint not considered in Agent-Oriented Software Engineering (AOSE) methodologies that this work intends to cover. This aspect is the main innovation of this work, and also presents a critical requirement that should be fulfilled by applications before someone tries to use the AOSE methods proposed here. In this context, the paper presents a set of AOSE methods derived from ITS research, which defines applicability criteria, designs principles and implementation guidelines to be applied in the software analysis, design and development process. The application of such methods is exemplified by several ITSs developed in our research group.

A Prototype for an Intelligent Tutoring System for Students Learning to Program in Java™

The Java™ Intelligent Tutoring System (JITS) research projectinvolves the development of a programming tutor designed for students in their first programming course in JavaTM at the collegeor university level.

Domain and task representation for tutorial process models

The teaching of procedural skills is a fruitful area of intelligent tutoring system research. Students often learn such skills most effectively by computer when a guided discovery mode is employed. So far, no general aids to developing associated software have emerged. A domain and task representation scheme is proposed that facilitates a cognitive modelling approach to the development of discovery learning systems. It is based on a classical formalism used in AI planning and utilizes recent modifications to the technique to allow hierarchical feedback to be included, efficiency to be improved, the important distinction between domain and task to be made, and task representation to be simplified.

An approach to developing intelligent tutors in mathematics

Mathematics is highly structured and underpins most of science and engineering. For this reason it has proved a very suitable test domain for intelligent tutoring system (ITS) research with the result that probably more tutoring systems have been structured for the domain than for any other. However, there still exists no consensus on any approach for the design of such systems. Consequently, existing ITSs in the domain suffer from a considerable number of shortcomings which render them ‘unintelligent’. This paper after rigorously examining the shortcomings of existing approaches, presents an alternative approach to constructing ITSs in mathematics which has been demonstrated to have improved on at least some of the shortcomings of existing approaches. It also provides a flavour of how this approach has been implemented in the FITS system.

A knowledge acquisition tool for intelligent computer tutors

This research addresses the widening gap between research in intelligent tutoring systems and practical use of this technology by the educational community. In order to insure that intelligent tutoring systems (ITSs) are effective, teachers must be involved in their design and evaluation. We have followed a user participatory design process to build a set of ITS knowledge acquisition tools tailored for usability by teachers. The system facilitates rapid prototyping and testing of curriculum and multiple tutoring strategies. Teachers use the system to create, modify, and test the system's domain content and tutoring strategy knowledge bases. The design includes novel methodologies for strategy representation and overlay student modeling, and incorporates considerations from instructional design theory. Tools have been designed to provide the user with visual models of the concepts and structures of the underlying framework. In close collaboration with a veteran high school teacher, we have used the interface to design a tutor for statics (part of a high school physics course). In this paper we describe the system (called KAFITS), report on our experience involving educators in ITS development, discuss issues of ITS knowledge representation and acquisition, and compare the system with related research in generic tutoring systems and knowledge acquisition.

Engineering design education: the potential offered by artificial intelligence techniques

Current approaches to the teaching of engineering design are reviewed, as are current approaches to the use of computers in designing and in design education. Recent trends in research into the application of artificial intelligence techniques to education are identified. Some ways in which design education might benefit from intelligent tutoring systems research results (in terms of a growing understanding of teaching techniques and learning processes) are proposed. Some potential advantages of applying those results directly in design education are identified.