Automatic feedback in online learning environments: A systematic literature review

被引：0
作者：
Cavalcanti A.P. [1 ]
Barbosa A. [2 ]
Carvalho R. [2 ]
Freitas F. [1 ]
Tsai Y.-S. [3 ]
Gašević D. [3 ,4 ,5 ]
Mello R.F. [2 ]
机构：
[1] Centro de Informática, Universidade Federal de Pernambuco, Recife
[2] Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife
[3] Centre for Learning Analytics, Faculty of Information Technology, Monash University
[4] School of Informatics, University of Edinburgh
[5] Faculty of Computing and Information Technology, King Abdulaziz University
来源：
Computers and Education: Artificial Intelligence | 2021年 / 2卷
关键词：
Automatic feedback; Educational feedback; Online learning environments; Systematic review;
D O I：
10.1016/j.caeai.2021.100027
中图分类号：
学科分类号：
摘要：
Feedback is an essential component of scaffolding for learning. Feedback provides insights into the assistance of learners in terms of achieving learning goals and improving self-regulated skills. In online courses, feedback becomes even more critical since instructors and students are separated geographically and physically. In this context, feedback allows the instructor to customize learning content according to the students' needs. However, giving feedback is a challenging task for instructors, especially in contexts of large cohorts. As a result, several automatic feedback systems have been proposed to reduce the workload on the part of the instructor. Although these systems have started gaining research attention, there have been limited studies that systematically analyze the progress achieved so far as reported in the literature. Thus, this article presents a systematic literature review on automatic feedback generation in learning management systems. The main findings of this review are: (1) 65.07% of the studies demonstrate that automatic feedback increases student performance in activities; (2) 46.03% of the studies demonstrated that there is no evidence that automatic feedback eases instructors’ workload; (3) 82.53% of the studies showed that there is no evidence that manual feedback is more efficient than automatic feedback; and (4) the main method used for automatic feedback provision is the comparison with a desired answer in some subject (such as logic circuits or programming). © 2021 The Authors
引用
收藏
相关论文
共 138 条
  • [1] Akcapinar G., How automated feedback through text mining changes plagiaristic behavior in online assignments, Computers & Education, 87, pp. 123-130, (2015)
  • [2] Aleman J.L.F., Palmer-Brown D., Draganova C., Evaluating student response driven feedback in a programming course, 2010 10th IEEE international conference on advanced learning technologies, pp. 279-283, (2010)
  • [3] Alencar M., Netto J.F., Tutor collaborator using multi-agent system, International conference on collaboration technologies, pp. 153-159, (2014)
  • [4] Al-Hamad B., Mohieldin T., E-assessment as a tool to augment face-to-face teaching and learning environment, 2013 fourth international conference on e-learning” best practices in management, design and development of e-courses: Standards of excellence and creativity”, pp. 348-359, (2013)
  • [5] Ali L., Asadi M., Gasevic D., Jovanovic J., Hatala M., Factors influencing beliefs for adoption of a learning analytics tool: An empirical study, Computers & Education, 62, pp. 130-148, (2013)
  • [6] Arends H., Keuning H., Heeren B., Jeuring J., An intelligent tutor to learn the evaluation of microcontroller i/o programming expressions, Proceedings of the 17th Koli calling international conference on computing education research, pp. 2-9, (2017)
  • [7] Baneres D., Clariso R., Jorba J., Serra M., Experiences in digital circuit design courses: A self-study platform for learning support, IEEE Transactions on Learning Technologies, 7, pp. 360-374, (2014)
  • [8] Barbosa G., Camelo R., Cavalcanti A.P., Miranda P., Mello R.F., Kovanovic V., Gasevic D., Towards automatic cross-language classification of cognitive presence in online discussions, Proceedings of the tenth international conference on learning analytics & knowledge, pp. 605-614, (2020)
  • [9] Becheikh N., Landry R., Amara N., Lessons from innovation empirical studies in the manufacturing sector: A systematic review of the literature from 1993–2003, Technovation, 26, pp. 644-664, (2006)
  • [10] Belcadhi L.C., Personalized feedback for self assessment in lifelong learning environments based on semantic web, Computers in Human Behavior, 55, pp. 562-570, (2016)
12345678910