A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System

A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System

Ninni Singh (University of Petroleum and Energy Studies, Dehradun, India), Neelu Jyothi Ahuja (University of Petroleum and Energy Studies, Dehradun, India) and Amit Kumar (University of Petroleum and Energy Studies, Dehradun, India)
Copyright: © 2018 |Pages: 20
DOI: 10.4018/JCIT.2018070101
OnDemand PDF Download:
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

An ideal face-to-face tutor learner interaction aims to offer learning to the learner in a manner that best suits an individual learner's learning level and learning style. This ability of differentiated instruction has been built in Seis-Tutor Intelligent Tutoring system, developed to offer subject matter knowledge of ‘Seismic Data Interpretation,' a field of geo-physics. The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described. An algorithm for learner-centric curriculum sequencing, a mathematical model and proposed implementation using a case study has been elaborated.
Article Preview
Top

Background And Preliminaries

Very limited work is reported in learner path sequencing in intelligent tutoring systems. However, a reasonably sizable work on curriculum sequencing is reported in e-Learning systems, web-based hypermedia systems and Learning Management Systems. But our systematic review reveals that the ITS systems have remained unexplored in this context so far. Till now, the organization of learning material in intelligent tutoring system, is commonly based on heuristic rules, i.e. during construction of domain, programmer defines rules for all possible situations. This means that initially all learners are provided with same predefined curriculum, but as learners progress, the remedial actions on each activity of the learner are recommended, much the same way like the human tutors do. For example, ITS helps the learner to solve a particular question by providing necessary hints for the question (Brusilovsky, Schwarz, & Weber, 1996, Skinner et al., 1958, Wu et al., 1991).

Some work on curriculum sequencing in ITS is reported below: A depth first traversal algorithm was used in Knowledge-based systems, Hyperbook systems as a sequencing technique (Baldoni, Baroglio, & Patti, 2001).

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 24: 4 Issues (2022): Forthcoming, Available for Pre-Order
Volume 23: 4 Issues (2021): 1 Released, 3 Forthcoming
Volume 22: 4 Issues (2020)
Volume 21: 4 Issues (2019)
Volume 20: 4 Issues (2018)
Volume 19: 4 Issues (2017)
Volume 18: 4 Issues (2016)
Volume 17: 4 Issues (2015)
Volume 16: 4 Issues (2014)
Volume 15: 4 Issues (2013)
Volume 14: 4 Issues (2012)
Volume 13: 4 Issues (2011)
Volume 12: 4 Issues (2010)
Volume 11: 4 Issues (2009)
Volume 10: 4 Issues (2008)
Volume 9: 4 Issues (2007)
Volume 8: 4 Issues (2006)
Volume 7: 4 Issues (2005)
Volume 6: 1 Issue (2004)
Volume 5: 1 Issue (2003)
Volume 4: 1 Issue (2002)
Volume 3: 1 Issue (2001)
Volume 2: 1 Issue (2000)
Volume 1: 1 Issue (1999)
View Complete Journal Contents Listing