A Gradually Developing Adaptive Tutoring System as the Course Progresses (original) (raw)

Dynamic Generation of Adaptive Tutoring

Systems Design and Development, 2012

Adaptive Educational Systems are able to alter an online course as per the needs of each student. Existing technologies require significant time and effort to design and build such courses. This chapter offers a solution allowing instructors to build a practical adaptive system as they upload their lessons and tests to the online site. The system asks the instructor to associate multiple choice answers that are incorrect with error pattern names and to associate the error patterns with lessons students need to review. The result is that the adaptable system is dynamically built as the course progresses. A student views a student profile screen that is adapted to that student's level of knowledge and displays that student's misconceptions. On the other hand, an instructor can use a reports view of the system to extract common error co-occurrences and infer information about the difficulties faced by students in that course.

CIA: Framework for the creation and management of Adaptive Intelligent Courses

This document presents the description of CIA, a Web platform that uses dynamic pages to create and manage virtual courses with Intelligent Tutoring System features that has the next functions: First, they have a tree structure where the course is the root and going down the Learning Objects are found as the leafs. Such structure may be seemed as a text book that is separated in chapters, sub chapters, etc. Second, they perform an intelligent sequencing of the curriculum based on the courses structure and the results of the assessments. Third, they consider the needs and characteristics of the students, adapting the presented content using their learning styles. Forth, they use the LOM metadata standard to describe and manage the Learning Objects that are saved in a repository in order to facilitate the reusability. And fifth, they have tools that contribute to collaborative work as forums and mail exchange.

An adaptive assessment tool integrable into Internet-based learning systems

International Conference on ICT in Education

SIETTE is an adaptive web-based assessment system. It implements Computerized Adaptive Tests. In this system, the finalization decision, the item selection criteria and the estimation of the student's knowledge level are accomplished following a psychometric theory called Item Response Theory. In this paper, SIETTE is presented as an open tool that can be easily integrated into web-based learning systems. It can be also integrated into Intelligent Tutoring Systems by a more sophisticated mechanism. The main goal of the integration is that the student does not notice that he is making a test in a different tool. This system save time to the developers of learning system, since they do not have to implement specific tools for evaluation inside their systems.

A Web-Based Adaptive and Intelligent Tutor by Expert Systems

Advances in Computing and Information …, 2013

Todays, Intelligent and web-based E-learning is one of regarded topics. So researchers are trying to optimize and expand its application in the field of education. The aim of this paper is developing of E-learning software which is customizable, dynamic, intelligent and adaptive with Pedagogy view for learners in intelligent schools. This system is an integration of adaptive web-based Elearning with expert systems as well. Learning process in this system is as follows. First intelligent tutor determines learning style and characteristics of learner by a questionnaire and then makes his model. After that the expert system simulator plans a pre-test and then calculates his score. If the learner gets the required score, the concept will be trained. Finally the learner will be evaluated by a post-test. The proposed system can improves the education efficiency highly as well as decreases the costs and problems of an expert tutor. As a result, every time and everywhere (ETEW) learning would be provided via web in this system. Moreover the learners can enjoy a cheap remote learning even at home in a virtual simulated physical class. So they can learn thousands courses very simple and fast.

Principles of Natural Language Processing and Adaptive Courseware in E-Assessments

Advances in Higher Education and Professional Development

Over the last few decades, researchers put efforts to improve intelligent tutoring systems' abilities with the aim to get them as close as possible to the ultimate goal of one-to-one tutoring. CoLaB Tutor and AC-ware Tutor are intelligent tutoring systems based on conceptual knowledge learning and are notable due to the fact they are relatively easy to generalize to multiple knowledge domains. CoLaB Tutor's forte lies in teacher-learner communication in controlled natural language, while AC-ware Tutor focuses on the automatic and dynamic generation of adaptive courseware. In order to compare various intelligent tutoring system supported education environments, in this chapter, the authors summarize several empirical evaluations of CoLaB Tutor and AC-ware Tutor. The results of intelligent tutoring systems' effectiveness in these environments offer the possibility to observe the specific intelligent tutoring system across various education levels, as well as to compare the intelligent tutoring systems' supported education environments.

Designing and Implementing an Adaptive Online Examination System

Procedia - Social and Behavioral Sciences, 2014

A design and application of adaptive online exam system are carried out in this paper. Adaptive exam systems determine different question sets automatically and interactively for each student and measure their competence on a certain area of discipline instead of comparing their gains with each other. Through an adaptive exam technique, a student's distraction and motivation loss that is led by the questions with quite lower hardness level than his/her competency is prevented. In addition, negative effects of questions requiring higher knowledge than his/her competency over a student's self confidence and morale are dismissed. Since questions are specialized so that they can allow making clear deductions about student gains, they are able to detect student competencies more effectively. Requiring less total time for measuring and being more flexible in the exam management are among the advantages provided by the system. Self sufficiency of the system in terms of planning, repeating and assessment of the measurement process especially allows itself to be used in the individual education sets. Through this system, student competencies can be determined more effectively in cases such as distant-learning, in which some challenges are experienced frequently.

Knowledge-Based Adaptive Assessment in a Web-Based Intelligent Educational System

Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), 2006

In this paper, we present an adaptive and intelligent webbased educational system that uses AI techniques for personalized assessment of the learners. More specifically, we focus on a mechanism for on-line creation of a useradapted test, which can be used alongside the predetermined test. The user can ask for such a test any time he/she is willing to do so, even if he/she has not studied all predetermined concepts of a learning goal. A small rule base is used by an expert system inference engine for making decisions on the difficulty level of the exercises to be included in the test. This is based on the evaluation of the learner during concept studying. Adaptive assessment of the learner can be repeatedly used until there is no further need. Another small rule-base is used for deciding on whether a new test is suggested or not. This is based on the learner's previous test assessment results. Preliminary experimental results show that the users need less time to study a learning goal when using the adaptive assessment capability of the system.

Running Header: Web-based Adaptive Educational Systems

2008

A Simple Web-based Adaptive Educational System (SWAES) An adaptive educational system is complex to model. Such a system should allow various didactic, educational and student styles and is traditionally divided into four modules for analysis and building: Domain Knowledge Base, Tutoring, the Student Interface, and the Student. In particular, the system should adapt to differences between students and the changes in a particular student. One of the main problems when trying to develop an adaptive educational system is that definitions used for instructional design by psychologists and pedagogy experts are vague, while developers work with concrete terms like variables, stochastic values and objects. To make the concept of an adaptive educational system more concrete, this work models the simplest possible adaptive educational system that could exist. The contribution of the paper is that it gives a good description of a basic adaptive educational system and reasons about concrete so...

Auto-Adaptive Questions in E-Learning System

Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), 2006

All books entitled "Learn … with 1000 exercises" have in common the same basic principle. They aim to supply enough material to students so that they may better understand the studied subject, starting from their own practice. If there is no instructor who helps students during the reading of the book, the students will not be able to understand the subject, as the excessive amount of information provided in this kind of books does not enable learners to pursue the learning goals.

Development of an Adaptive Learning System

We investigate the requirements for an adaptive learning system. A conceptual model is explored which links together a student model, a tutor model and a knowledge model. We further consider the use of an adaptive engine which allows the system to respond to the needs of individual students, present learning objects according to the preferences of individual tutor styles, allows automatic self-exploration at the level of student maturity and encodes the curriculum in a form that is accessible to the adaptive engine. Our model accurately represents both the structure and content of learning objects in contrast with less structured data models implicit in ontological hierarchies.