ELEMENTOS DE LA ADMINISTRACIÓN PÚBLICA

There are many intelligent tutoring systems that exist. Three real systems will be described to understand the concept of these examples.

2.2.2.1 Tactical Action Officer(TAO ITS)

The Tactical Action Officer is an intelligent tutoring system which has the advantage that it can be utilised both for individual students and for a class of students. The software‘s main aim is to extend active training of learners and to improve their knowledge of concepts and tactics.

There are three main difficulties usually related to intelligent tutoring systems: how to integrate subject expert knowledge in the system, how much the system would cost, and how long it would take to develop the system. In order to deal with these difficulties, case-based reasoning technique [133] was used in the Tactical Action Officer. This refers to the process of solving new problems based on the solutions of similar or past problems [133]. The software comprises of three components: scenario generator, intelligent tutoring and the instructor interface tool. The scenario generator allows the teacher, with minimal aid from a programmer, to develop various simulated scenarios. Scenarios are chosen by the software for the students to practice. The adaptivity of the system allows it to choose scenarios that the learner has not yet done or has performed poorly on in the past. It also allows the student to select any scenario. What is important about this software is that in addition to the intrinsic feedback that it gives, it also provides the learner with a detailed extrinsic feedback on his/her performance on the different parts of the scenario [118].

2.2.2.2 Intelligent Tutoring System for teaching 1styear engineering students

The intelligent tutoring system for teaching 1st year engineering students [134] is a software system that adapts teaching to each learner‘s individual learning speed.

Basically, the system observes the advancement of the learner and is able to select the next step in the teaching process.

This system was developed on the basis of fuzzy logic and fuzzy rule-based reasoning, presented by Lotfi Zadeh [135]. The composition of the system is a database with questions, a database with the learners in the course and their advancement, and an expert system with fuzzy-rule based decision-making that leads the behaviour of the intelligent tutoring system. Each question has three parameters assigned: concepts encompassed in the questions, difficulty level, and level of importance. The expert system extracts details about each student on the basis of their achievements and performance, as well as the concepts in each topic, the difficulty and the level of importance. After obtaining those new details, the system automatically selects the questions to be covered based on the achievement level of the learner. Since some topics are of higher importance than others, the system first selects questions from the most significant topics and after that, if the performance level of the student is satisfactory, proceeds to questions of topics with lower importance respectively. The learner‘s database keeps track of each learner‘s achievements. This database is updated automatically by the system and keeps record of how deep and how broad the student´s knowledge acquisition is. Feedback is available for learners at all times during the learning session.

The reasons for the selection of fuzzy logic lie behind its similarity in decision-making to that of humans. It does not rely on complicated calculations. Instead, it bases its judgment on basic rules that a human instructor would base his/her judgment on. Another reason is that it is not difficult to use, and it can be adapted to everybody‘s needs and requirements. The assessment of the learner‘s advancement is also done in a flexible way, exactly as a human instructor would do, and not with the aid of complex formulas.

2.2.2.3 ELM-ART

The ELM Adaptive Remote Tutor [136] (abbreviated as ELM-ART) is a web-based intelligent text-book. It has two main characteristics. The first one is that it is familiar with the instruction material taught to the learners and offers help and support throughout the learning process. The second one is that none of the examples and problems in the system are merely text. They resemble more a 'live experience'. ELM-

ART helps students navigate through the lesson by utilising two hypermedia techniques: adaptive annotation and adaptive sorting of links. Adaptive annotation means that the software utilises visual cues to show the learning stage of each link. Adaptive sorting is utilised to show similar links between cases. After that links are shown in sorted order starting from the most relevant to the case ones. If the learner attempts to enter a page with material that was not yet covered, the system advises him/her that this material was not learned yet and guides him to the links in the textbook where the material can be found. The system also shows links with the pages in the textbook, if a student has trouble with the presented material or cannot understand a question, he/she can ask for assistance in such a case. The ELM Adaptive Remote Tutor can foresee the way in which students will solve a given problem and retrieve the most adequate example from the student‘s studying history. If a learner is incapable of finding the solutions to the problem and is unable to find the mistake reported by the system, he/she can ask for assistance and the system will provide him/her with a chain of help messages, which get progressively more and more in depth [136, 137].

It is clear that these systems offer students a chance to learn material in a way that best suits them. This approach is referred to as adaptivity and will be discussed in more detail below.