Title: Question selection algorithm for dynamic question requirements in different learning environments from an unstructured repository

Authors: G.M. Shivanagowda; R.H. Goudar; Umakanth P. Kulkarni

Addresses: Department of Computer Science Engineering, Shri Dharmasthala Manjunatheshwara College of Engineering and Technology, Dharwad, India ' Department of Computer Network Engineering, Visvesvaraya Technological University, Belgaum, India ' Department of Computer Science Engineering, Shri Dharmasthala Manjunatheshwara College of Engineering and Technology, Dharwad, India

Abstract: In the personalised learning environments like CRETAL, all possible type of learning resources are brought together in a common platform to improve the learning efficiency. In the blended environment where such systems are used along with conventional classroom teaching and learning, a huge amount of the data related to student behaviour and knowledge models is generated. This data can be used to generate the recommendation to learners regarding either next topic to be learned to meet the objectives of the course, repetition of the previous topics or current topic with increased Bloom's taxonomy of educational objective, etc. The most important subsystem in such learning environments is a questioning system which could be used now and then either in freelance mode or in the monitored prescription mode to assess the learning achieved. The questioning system also plays a key role in generating question papers during the conventional examinations in the institutes. In this paper, we discuss the design of questioning systems as a strongly coupled subsystem of CRETAL's personalised learning environments. We also discuss the similarities of the question paper generation problem with bounded knapsack problem (BKP) and an algorithm for selecting questions from the unstructured repository as its solution.

Keywords: questioning system; knowledge modelling; domain modelling; question modelling; personalised learning systems; selection algorithm; concept maps; graph database.

DOI: 10.1504/IJCCIA.2020.105572

International Journal of Computational Complexity and Intelligent Algorithms, 2020 Vol.1 No.3, pp.259 - 276

Received: 28 Jun 2018
Accepted: 01 Jan 2019
Published online: 03 Mar 2020 *

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