Intelligent Tutoring Systems for the New Learning Infrastructure

2005 ◽  
pp. 225-250 ◽  
Author(s):  
Marko Rosic ◽  
Vlado Glavinic ◽  
Slavomir Stankov
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Computer Systems ◽  
Tutoring Systems ◽  
Learning And Teaching ◽  
New Learning ◽  
Learning Capabilities ◽  
The Web

Intelligent tutoring systems (ITS) are a generation of computer systems which provide students with learning and teaching environments adapted to their knowledge and learning capabilities. In this chapter, we analyze the conceiving of intelligent tutoring systems in the new learning infrastructure environment, encompassing technologies like the Semantic Web and the Web Services.

Recent Advances in Intelligent Tutoring Systems

2013 ◽  
pp. 631-647
Author(s):  
Joel J. P. C. Rodrigues ◽  
Pedro F. N. João ◽  
Isabel de la Torre Díez
Keyword(s):  
Learning Process ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Field Tests ◽  
Study Guide ◽  
Web Based ◽  
Tutoring Systems ◽  
Recent Advances ◽  
The Web

Intelligent Tutoring Systems (ITS) include interactive applications with some intelligence that supports the learning process. Some of ITS have had a very large impact on educational outcomes in field tests, and they have provided an important ground for artificial intelligence research. This chapter elaborates on recent advances in ITS and includes a case study presenting an ITS called EduTutor. This system was created for the Web-Based Aulanet Learning Management System (LMS). It focuses on subjects for the first cycle of studies of the Portuguese primary education system, between the first and the fourth year. It facilitates the perception of the learning process of each student, individually, in a virtual environment, as a study guide. Moreover, EduTutor has been designed and its architecture prepared for being easily integrated into higher levels of studies, different subjects, and several languages. Currently, it is used in the Aulanet LMS platform.

A Comparison of Three Learning Strategies in Intelligent Tutoring Systems

1996 ◽  
Vol 14 (4) ◽  
pp. 371-383 ◽  
Author(s):  
Claude Frasson ◽  
Esma Aimeur
Keyword(s):  
Knowledge Acquisition ◽  
Learning Strategies ◽  
Learning Process ◽  
Intelligent Tutoring Systems ◽  
Learning Strategy ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
New Approaches ◽  
Self Confidence ◽  
New Learning

New approaches in Intelligent Tutoring Systems imply a more active participation of the learner in the learning process. The motivation of the learner can be increased by interaction with a companion who strengthens the knowledge acquisition in a cooperation climate. In this article we introduce a new learning strategy called learning by disturbing intended to improve student self-confidence. We compare it to directive learning and peer learning, discussing the advantage and the inconvenience of each one. We present some experiments realized to show in which condition a strategy can be useful or not. We analyze and discuss results obtained.

Artificial Tutoring Systems: What Computers Can and Can't Know

1997 ◽  
Vol 16 (2) ◽  
pp. 107-124 ◽  
Author(s):  
Theodore W. Frick
Keyword(s):  
Intelligent Tutoring Systems ◽  
Clinical Evidence ◽  
Intelligent Tutoring ◽  
Computer Systems ◽  
Classroom Teachers ◽  
Language Understanding ◽  
Tutoring Systems ◽  
Natural Systems ◽  
Teachers And Students

After more than four decades, development of artificially intelligent tutoring systems has been constrained by two interrelated problems: knowledge representation and natural language understanding. G. S. Maccia's epistemology of intelligent natural systems implies that computer systems will need to develop qualitative intelligence before these problems can be solved. Recent research on how human nervous systems develop provides evidence for the significance of qualitative intelligence. Qualitative intelligence is required for understanding of culturally bound meanings of signs used in communication among intelligent natural systems. S. I. Greenspan provides neurological and clinical evidence that emotion and sensation are vital to the growth of mind—capabilities that computer systems do not currently possess. Therefore, we must view computers in education as media through which a multitude of teachers can convey their messages. This does not mean that the role of classroom teachers is diminished. Teachers and students can be empowered by these additional learning resources.

A Meta-Analytic Estimation of a Common Effect Size from a Series of Experiments Related To an E-Learning System Effectiveness Evaluation

2011 ◽  
pp. 327-341
Author(s):  
Ani Grubišic
Keyword(s):  
Student Performance ◽  
Effect Size ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Meta Analysis ◽  
Learning System ◽  
Tutoring Systems ◽  
Learning And Teaching ◽  
E Learning ◽  
Series Of Experiments

As the acquisition of knowledge is often an expensive and time-consuming process, it is important to know whether it actually improves the student performance. The e-learning is a revolutionary paradigm that has lately been significantly evolving and it is closely related to the intelligent tutoring systems. Methodology for evaluating the educational influence of learning and teaching process, questions whether and in what amount, students learn effectively. Our contribution to this compulsive field of research is a meta-analysis of a series of experiments based on the same two-group methodology that reveals a more precise effect size of one particular e-learning system - eXtended Tutor-Expert System, a representative of web-based authoring shells for building intelligent tutoring systems.

scholarly journals Data-Driven Intelligent Tutoring Systems for STEM+C Learning and Teaching

2019 ◽  
Vol 4 (9) ◽  
pp. 202-206
Author(s):  
Hieu Trong Bui
Keyword(s):  
Problem Solving ◽  
Intelligent Tutoring Systems ◽  
Student Behavior ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Data Driven ◽  
Tutoring Systems ◽  
Learning And Teaching ◽  
Past Data ◽  
Problem Solving Behavior

It is wide known that one of the most effective ways to learn is through problem solving. In recent years, it is widely known that problem solving is a central subject and fundamental ability in the teaching and learning. Besides, problem solving is integrated in the STEM+C (Science, Technology, Engineering, and Math plus Computing, Coding or Computer Science) fields. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. Intelligent tutoring systems provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. In this work, the main goal of this paper is to: 1) present ITSs used in the STEM+C education; and 2) introduce data-driven ITSs for STEM+C education.

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