Interviewer presence can affect learning with interview videos

Interviews with scholars and experts are becoming more and more popular as e-learning materials. Yet how an interview video should be edited is mostly based on personal preference rather than on rigorous scientific research. Thus this study tested whether showing the interviewer in educational interview videos can affect the learning outcome. Two interview learning materials on two topics (eye tracking and text–picture integration) were conducted by the author and edited in two versions. One version was with the interviewer and the other version was without the interviewer, the latter’s image and voice being edited out. Psychology students ( N = 180) watched either the video with or the one without the interviewer and answered the corresponding questions. Results in an online experiment yielded a better learning outcome in the video without the interviewer than in the video with the interviewer. It is probable that the absence of the interviewer can protect participants from extraneous processing and a split-attention effect. The without-interviewer video, segmented by displaying interview questions in keywords on slides, seemed to assist participants in managing the essential processing. The absence of the interviewer may avoid the confusion of multiple instructors, which fosters the generative processing. This study provides practical and pedagogical implications and suggests that removing the image and voice of the interviewer is likely to promote learning.

Does Augmented Reality Effectively Foster Visual Learning Process in Construction? An Eye-Tracking Study in Steel Installation

Augmented reality (AR) has been proposed to be an efficient tool for learning in construction. However, few researchers have quantitatively assessed the efficiency of AR from the cognitive perspective in the context of construction education. Based on the cognitive theory of multimedia learning (CTML), we evaluated the predesigned AR-based learning tool using eye-tracking data. In this study, we tracked, compared, and summarized learners’ visual behaviors in text-graph- (TG-) based, AR-based, and physical model- (PM-) based learning environments. Compared to the TG-based material, we find that both AR-based and PM-based materials foster extraneous processing and thus further promote generative processing, resulting in better learning performance. The results show that there are no significant differences between AR-based and PM-based learning environments, elucidating the advantages of AR. This study lays a foundation for problem-based learning, which is worthy of further investigation.

Applied linguistics

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