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Journal ArticleDOI

Integration of an intelligent tutoring system in a course of computer network design

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TLDR
The current paper describes the initial user test, which was conducted at the University of Valladolid for the course “Network Design” and shows that students with INTUITEL follow learning paths that are more suitable for them.
Abstract
INTUITEL is a research project aiming to offer a personalized learning environment. The INTUITEL approach includes an Intelligent Tutoring System that gives students recommendations and feedback about what the best learning path is for them according to their profile, learning progress, context and environmental influences. INTUITEL combines efficient pedagogical-based recommendations with freedom of choice and it introduces this tutoring support in different Learning Management Systems. During the INTUITEL project various software and pedagogical testing procedures were defined to provide the development teams with feedback, both summative and formative. The current paper describes the initial user test, which was conducted at the University of Valladolid for the course “Network Design”. The experiment was focused on real learners’ reactions to INTUITEL recommendations received by an INTUITEL-enabled LMS. Nineteen students participated in a two phase testing procedure in order to analyze the learners’ behavior with INTUITEL, as well as obtaining information about how learners perceive the influence and usefulness of the tutoring system in online learning courses. Results show that students with INTUITEL follow learning paths that are more suitable for them. Besides, the general satisfaction level of participants is high. Most learners appreciate INTUITEL, would follow its recommendations and consider the messages shown by INTUITEL as useful and caring.

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Intelligent tutoring system model based on fuzzy logic and constraint-based student model

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Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information Wikis

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References
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Journal ArticleDOI

Assessing What Really Matters to Student Learning Inside The National Survey of Student Engagement

TL;DR: In this paper, the authors assess what really matters to student learning inside The National Survey of Student Engagement and conclude that "student engagement is not the most important issue in higher education."
Proceedings Article

ELM-ART: An Adaptive Versatile System for Web-based Instruction

TL;DR: It is argued that versatility is an important feature of successful Web-based education systems and ELM-ART, an intelligent interactive educational system to support learning programming in LISP, demonstrates how some interactive and adaptive educational component can be implemented in WWW context and how multiple components can be naturally integrated together in a single system.
Journal ArticleDOI

A Review of Online Course Dropout Research: Implications for Practice and Future Research

TL;DR: In this paper, the authors identified 69 factors that influence students' decisions to dropout and classified them into three main categories: (a) Student factors, (b) Course/Program factors, and (c) Environmental factors.
Journal ArticleDOI

E-Learning personalization based on hybrid recommendation strategy and learning style identification

TL;DR: A recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners, is described, which shows suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.
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The many levels of inquiry

TL;DR: In this article, the authors present a continuum by which to evaluate an activity's level of inquiry and describe examples of each type of inquiry from low-level structured inquiry to high-level open inquiry.
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