Effective questioning strategies in online videos: evidence based on electroencephalogram data

Abstract

Online videos are a popular means of imparting education. This study investigated the effects of different questioning strategies used in online videos on learners’ attention levels, as well as the mediating effect of attention levels on the relationship between questioning strategies and learning performance. One hundred students from a Chinese University were randomly assigned videos with one of five questioning strategies: the pre-leading questioning (PLQ), middle-enhancing questioning (MEQ), and post-assessment questioning (PAQ) strategies as well as a combination of PLQ and MEQ and PLQ and PAQ. By using an electroencephalogram (EEG) to measure the learners’ brainwaves, this study found that embedding questions in online videos could increase learners’ attention levels. The results demonstrated that learners exposed to a combination of the two questioning strategies paid better attention than those exposed to a single strategy. Furthermore, attention level was found to be the only mediator in the relationship between the PLQ + MEQ strategy and learning performance while it played a suppressive role in the relationship between the PLQ + PAQ strategy and learning performance. These findings have significant implications for education. Instructors should design questions for online videos based on their teaching objectives at a given stage and consider the potentially negative consequences of other factors (such as cognitive load) when using multiple questioning strategies in the same video.

Keywords:

• Questioning
• online videos
• attention
• performance
• online learning
• electroencephalography

Reviewing Editor:

• Shuyan Wang, The University of Southern Mississippi, United States

Subjects:

• Technology in Education
• Design and Delivery
• ICT

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Social Science Fund of China.

Notes on contributors

Qingchao Ke

Qingchao Ke is a professor in the school of Information Technology in Education at South China Normal University. His research interests include technology enhanced learning and education digitalization.

Tingting Bao

Tingting Bao is a doctoral candidate of South China Normal University. Her research interests include technology enhanced learning and AI in Education.

Jieni Zhu

Jieni Zhu is a postgraduate student of South China Normal University. Her research interests include MOOC and instructional design.

Xiufang Ma

Xiufang Ma is an Associate professor in the school of Information Technology in Education at South China Normal University. Her research interests include instructional design and AI curriculum in K12.