Contents lists available at ScienceDirect Computers & Education journal homepage: www.elsevier.com/locate/compedu Elaborated feedback and learning: Examining cognitive and motivational influences Zhen Wang a,b , Shao-Ying Gong a,b,∗ , Sheng Xu a,b , Xiang-En Hu a,b a School of Psychology, Central China Normal University, Wuhan, 430079, China b Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, 430079, China ARTICLEINFO Keywords: Evaluation of CAL systems Interactive learning environments Pedagogical issues Teaching/learning strategies ABSTRACT The effects of feedback vary widely and little is known about how feedback influences learning. This study investigated the complexity of elaborated feedback (additional instructional in- formation of the correct answer) and item format as influences on learning in a computer-based formative assessment. Undergraduate students (N = 107) were randomly assigned to one of four experimental conditions formed by a 2 (complexity of elaborated feedback: detailed explanation in the form of concise problem-solving steps or cue in the form of key formula) × 2 (item format: multiple-choice items or constructed-response items which require learners to produce answers without response alternatives) factorial design. Results revealed that item format moderated the effect of feedback complexity on learning: Detailed explanation promoted learners' feedback perception (learners’ response to the quality and use of feedback) and transfer performance in the constructed-response item condition. Detailed explanation resulted in lower extraneous cognitive load but higher germane cognitive load and learning motivation than cue; constructed-response items resulted in lower intrinsic and extraneous cognitive load but higher germane cognitive load than multiple-choice items. Furthermore, the assumed indirect effects were verified: Feedback perception fully mediated the moderating effect of item format on feedback complexity and transfer performance; feedback complexity had an indirect effect on transfer performance via germane cognitive load. The results have implications for matching item type and feedback type to maximize learning. 1. Introduction Computer-based feedback is one of the most important elements in computer-based learning environments. It refers to the in- formation provided by the computer about learners' performance (Narciss, 2013; Shute, 2008). It can provide information about the current state of learning and how to eliminate the discrepancy between the current state and the goal, thus allowing learners to adjust their cognition or behavior to promote learning (Hattie & Timperley, 2007). However, recent research found that computer-based feedback did not always promote learning and might even hinder learning (Fyfe, Rittle-Johnson, & Decaro, 2012; Roelle, Berthold, & Fries, 2011). In addition, elaborated feedback is not necessarily more effective than simple feedback (Fyfe, 2016; Van der Kleij, Eggen, Timmers, & Veldkamp, 2012). These heterogeneous findings suggest that the effects of feedback are not straightforward. Therefore, researchers have begun to pay attention to individual and situational characteristics when discussing the impact of computer-based feedback on learning. For example, learners’ prior knowledge and task difficulty have been identified as important https://doi.org/10.1016/j.compedu.2019.04.003 Received 12 January 2019; Received in revised form 1 April 2019; Accepted 2 April 2019 ∗ Corresponding author. School of Psychology, Central China Normal University, Wuhan, 430079, China. E-mail address: gongsy@mail.ccnu.edu.cn (S.-Y. Gong). Computers & Education 136 (2019) 130–140 Available online 03 April 2019 0360-1315/ © 2019 Elsevier Ltd. All rights reserved. T