Automated Cognitive Analyses for Intelligent Tutoring Systems

Authors: Banno, S., Murad, H. and Sallal, M.

Journal: Proceedings - 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2020

Pages: 171-178

ISBN: 9780738123967

DOI: 10.1109/BDCAT50828.2020.00007

Abstract:

Designing an Intelligent Tutoring System (ITS) that simulates human learning with regard to different knowledge levels is a challenge as it reflects an accurate way of estimating the students' performance level. Most developed ITSs typically focus on the normal cognitive factors such as the students' prior success and failure scores without paying appropriate consideration to the sensitive cognitive factors that have a great impact on the student performance prediction such as the integration of the human current skills and given items skills, particularly when the learning items require multiple skills, which thus reduce student's learning efficiency due to an incomplete representation of the student's knowledge. This paper presents a modified student modeling approach, called modified Performance Factor Analysis (ModPFA), based on a previously developed model called Performance Factor Analyses (PFA). ModPFA was developed by adding the hinting parameter to the original PFA formula. This extension has scoring procedure and knowledge level estimation for each student. Results have shown great improvement in terms of performance estimation in the ModPFA compared to PFA.

Source: Scopus