Field Of Intelligent Tutoring Systems Research Articles

Dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning

Sequence-wise recommendation, where recommend exercises to each student step by step, is one of the most exciting tasks in the field of intelligent tutoring systems (ITS). It is important to develop a personalized sequence-wise recommendation framework that immerses students in learning and helps them acquire as much necessary knowledge as possible, rather than merely focusing on providing non-mastered exercises, which is referred to optimize a single objective. However, due to the different knowledge levels of students and the large scale of exercise banks, it is difficult to generate a personalized exercise recommendation for each student. To fully exploit the multifaceted beneficial information collected from e-learning platforms, we design a dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning, i.e., DMoSwR-DRL, which automatically select the most suitable exercises for each student based on the well-designed domain-objective rewards. Within this framework, the interaction between students and exercises can be explicitly modeled by integrating the actor–critic network and the state representation component, which can greatly help the agent perform effective reinforcement learning. Specifically, we carefully design a state representation module with dynamic recurrent mechanism, which integrates concept information and exercise difficulty level, thus generating a continuous state representation of the student. Subsequently, a flexible reward function is designed to simultaneously optimize the four domain-specific objectives of difficulty, novelty, coverage, and diversity, providing the students with a trade-off sequence-wise recommendation. To set up the online evaluation, we test DMoSwR-DRL on a simulated environment which can model qualitative development of knowledge level and predicts their performance for a given exercise. Comprehensive experiments are conducted on four classical exercise-answer datasets, and the results show the effectiveness and advantages of DMoSwR-DRL in terms of recommendation quality.

Methods and standards for research on explainable artificial intelligence: Lessons from intelligent tutoring systems

AbstractThe DARPA Explainable Artificial Intelligence (AI) (XAI) Program focused on generating explanations for AI programs that use machine learning techniques. This article highlights progress during the DARPA Program (2017‐2021) relative to research since the 1970s in the field of intelligent tutoring systems (ITSs). ITS researchers learned a great deal about explanation that is directly relevant to XAI. We suggest opportunities for future XAI research deriving from ITS methods, and consider the challenges shared by both ITS and XAI in using AI to assist people in solving difficult problems effectively and efficiently.

Going deeper: Automatic short-answer grading by combining student and question models

As various educational technologies have rapidly become more powerful and more prevalent, especially from the 2010s onward, the demand of automated grading natural language responses has become a major area of research. In this work, we leverage the classic student and domain/question models that are widely used in the field of intelligent tutoring systems to the task of automatic short-answer grading (ASAG). ASAG is the process of applying natural language processing techniques to assess student-authored short answers, and conventional ASAG systems often mainly focus upon student answers, referred as answer-based. In recent years, various deep learning models have gained great popularity in a wide range of domains. While classic machine learning methods have been widely employed to ASAG, as far as we know, deep learning models have not been applied to it probably because the lexical features from short answers provide limited information. In this work, we explore the effectiveness of a deep learning model, deep belief networks (DBN), to the task of ASAG. Overall, our results on a real-world corpus demonstrate that 1) leveraging student and question models to the conventional answer-based approach can greatly enhance the performance of ASAG, and 2) deep learning models such as DBN can be productively applied to the task of ASAG.

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching. This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education. Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs. Finally, recent innovative ideas in ITS systems are presented. This paper concludes with some of the author's views regarding future work in the field of intelligent tutoring systems.

Our System IDCBR-MAS: from the Modelisation by AUML to the Implementation under JADE Platform

This paper presents our work in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learner's traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces on the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors.

Student Modeling: Supporting Personalized Instruction, from Problem Solving to Exploratory Open‐Ended Activities

The field of intelligent tutoring systems (ITSs) has successfully delivered techniques and applications to provide personalized coaching and feedback for problem solving in a variety of domains. The core of this personalized instruction is a student model: the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs during problem solving. There are however, other educational activities that can help learners acquire the target skills and abilities at different stages of learning including, among others, exploring interactive simulations and playing educational games. This article describes research on creating student models that support personalization for these novel types of interactions, their unique challenges, and how AI and machine learning can help.

Extracting learning concepts from educational texts in intelligent tutoring systems automatically

This paper argues that the educational support systems can give a meaning to an educational content semantically, and an answer has been sought to the question of “what should be taught to students” in the field of intelligent tutoring systems. With reference this aim, a system, which automatically detects the concepts to be learned by students, has been designed. The developed system uses the statistical language models together with conceptual map modeling as a student model to extract the minimal set of learning concepts within an educational content. In the study, ten corpora have been generated as a learning domain, which consist of two different subjects in mathematics. For each subject, five distinct chapters have been quoted from the books written by various authors. After extracting the candidate concepts from the given content, the system checks to clarify whether these candidates are in a dictionary within postprocessing. The dictionary consists of approximately 9500 technical terms related to the learning domain. The system performance has also been analyzed using Recall, Precision and F-measure scores. The results indicate that the postprocessing step increases precision with a small loss of recall.

Course sequencing techniques for large-scale web-based education

We argue that traditional sequencing technology developed in the field of intelligent tutoring systems could find an immediate place in large- scale web-based education. This paper discusses two models that have been explored by the authors - the dynamic course generation system DCG and the concept-based course maintenance system CoCoA. DCG includes components for domain authoring and for automatic generation of adaptive courses on the WWW. It allows automatic generation of individualised courses according to the learner's goal and previous knowledge, and can dynamically adapt the course according to the learner's success in acquiring knowledge. . CoCoA can check the consistency and quality of a course at any moment of its life and also assists course developers in some routine operations.

The friendly intelligent tutoring environment

The advancement of using the Artificial Intelligence (AI) methods and techniques in design Intelligent Tutoring Systems (ITSs) makes understanding them more difficult, so that teachers are less and less prepared to accept these systems. As a result, the gap between researchers in the field of ITSs and the educational community is constantly widening. While ITSs are becoming more common and proving to be increasingly effective, each one must still be built from scratch at a significant cost. Also present ITSs need quite big development environments, huge computing resources and, in consequence, are expensive and hardly portable to personal computers. This paper describes our efforts toward developing uniform data, explanation and control structures that can be used by a wide circle of authors who are involved in building ITSs (e.g., domain experts, teachers, curriculum developers, etc.) that is, the model of the ITSs framework, the GET-BITS model.

Intelligent Tutoring Tools in a Computer‐Integrated Learning Environment for Introductory Numeric Disciplines

SUMMARY Research in the field of intelligent tutoring systems has failed to provide any substantial or viable systems that could be used in real academic environments. This situation appears to be the result of two factors: first, the failure to identify clearly the objectives and the scope of such systems; and second, the continuously shifting technological platform on which such systems are built. This paper examines the possible objectives for the development of tutoring systems and presents an approach adopted by the Byzantium Project. It describes a model for a computer integrated learning environment (CILE) and discusses the role of an intelligent tutoring tool (ITT) within this model. The paper also considers the potential of the Internet for various learning environments. Based on our experience of designing and implementing four ITTs that have the same look and feel (but which address diverse subject areas) the paper suggests a possible extension of the Byzantium approach to the Internet through the conversion of ITTs into intelligent tutoring applets (ITAs).

Is There a Place in OR for Intelligent Tutoring Systems?

Intelligent tutoring systems (ITS) have recently found a wide range of potential applications across a multitude of disciplines and subject areas. This paper investigates the suitability and feasibility of developing ITS in the general context of OR as well as in a specific OR activity, simulation. It also examines how a current teaching aid for simulation model debugging can be transformed into an ITS and proposes a specification for the transformation process. The paper concludes that ITS could be useful in OR either for use as prime training systems or integrated in existing OR teaching packages. However, the field of ITS is in its infancy and development efforts are likely to be labour-intensive.