These systems emulate the decision making ability of a human expert for solving complex problems by reasoning about knowledge, represented by rules (rule-based reasoning systems) or cases (case-based reasoning systems) by using conventional procedural codes. It essentially has two parts – a knowledge base, which is composed of several ‘if-then’ rules of cases and an inference engine that deduces new facts (decisions) based on the old facts or rules or cases.
Published in Chapter:
Applications of Artificial Intelligence in Assessment for Learning in Schools
Subhagata Chattopadhyay (Indus Training and Research Institute (ITARI), India), Savitha Shankar (Indus Training and Research Institute (ITARI), India), Ramya B. Gangadhar (Indus Training and Research Institute (ITARI), India), and Karthik Kasinathan (Indus Training and Research Institute (ITARI), India)
Copyright: © 2018
|Pages: 22
DOI: 10.4018/978-1-5225-2953-8.ch010
Abstract
Assessment for Learning (AfL) is a process in measuring the learning outcome in students. Current practices in assessing the academic performance of students in most of the countries are still manual. It is based on the qualitative and quantitative feedbacks, obtained by expressed statement and marks, respectively. The issues associated with such assessment-practices are that it (a) lacks autonomy in students and the teachers to assess themselves for (1) better learning (ABeL) and (2) to learning (AtoL) with greater accuracy; (b) Self, peer and parents' involvements in the assessment process has often been underestimated, and (c) involved human bias while giving the qualitative and quantitative feedbacks. Given the background, this chapter attempts to showcase how various Artificial Intelligence (AI)-based solutions, such as Expert Control System (ECS)-based tutoring platform and Agent-based tutoring systems (AbS) can be used for the AfL, which in turn, improve ABeL and AtoL in students.