Review Adaptive Behavior 2017, Vol. 25(5) 217–234 Ó The Author(s) 2017 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1059712317727590 journals.sagepub.com/home/adb Adaptive feedback in computer-based learning environments: a review Andrew Thomas Bimba 1 , Norisma Idris 1 , Ahmed Al-Hunaiyyan 2 , Rohana Binti Mahmud 1 and Nor Liyana Bt Mohd Shuib 3 Abstract Adaptive support within a learning environment is useful because most learners have different personal characteristics such as prior knowledge, learning progress, and learning preferences. This study reviews various implementation of adaptive feedback, based on the four adaptation characteristics: means, target, goal, and strategy. This review focuses on 20 different implementations of feedback in a computer-based learning environment, ranging from multimedia web-based intelligent tutoring systems, dialog-based intelligent tutoring systems, web-based intelligent e-learning systems, adaptive hypermedia systems, and adaptive learning environment. The main objective of the review is to compare computer-based learning environments according to their implementation of feedback and to identify open research questions in adaptive feedback implementations. The review resulted in categorizing these feedback implementations based on the students’ information used for providing feedback, the aspect of the domain or pedagogical knowledge that is adapted to provide feedback based on the students’ characteristics, the pedagogical reason for providing feedback, and the steps taken to provide feedback with or without students’ participation. Other information such as the common adaptive feedback means, goals, and implementation techniques are identified. This review reveals a distinct relationship between the char- acteristics of feedback, features of adaptive feedback, and computer-based learning models. Other information such as the common adaptive feedback means, goals, implementation techniques, and open research questions are identified. Keywords Adaptation, learning environment, problem-solving, student modeling, learner model Associate Editor: Tom Froese 1. Introduction The process of learning involves mistakes and errors. In these situations, students often review course mate- rials and search the Internet or other sources to assist them in solving their problems (Ghauth & Abdullah, 2010). Seeking solution is usually time consuming and does not always insinuate a better learning experi- ence. Having a system which generates effective feed- back that guides students to the solution can improve the learning process (Mun˜oz-Merino et al., 2011). Feedback is frequently provided in a typical class- room setting; however, most of the information is poorly received because feedback is presented to groups and so often students do not believe such feed- back is relevant to them (Hattie & Gan, 2011). Currently, the gap between students who excel the most and those who excel less is a challenge that teachers, school administrators, and government offi- cials face frequently (Luckin & Holmes, 2016). Adaptive learning environments provide personaliza- tion of the instruction process based on different para- meters such as sequence and difficulty of task, type and time of feedback, learning pace, and others (Brusilovsky et al., 1999; Stoyanov & Kirchner, 2004). One of the key features in learning support is the personalization of feedback (Advisors, 2013). Adaptive feedback support within a learning environment is useful because most learners have different personal characteristics such as 1 Department of Artificial Intelligence, University of Malaya, Kuala Lumpur, Malaysia 2 Computer & Information Systems Department, College of Business Studies, The Public Authority for Applied Education & Training (PAAET), Kuwait City, Kuwait 3 Department of Information Systems, University of Malaya, Kuala Lumpur, Malaysia Corresponding author: Norisma Idris, Department of Artificial Intelligence, University of Malaya, 50603 Kuala Lumpur, Malaysia. Email: norisma@um.edu.my