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Our study identified online lecture video styles that improved student engagement and satisfaction, while maintaining high learning outcomes in online education. We presented different lecture video styles with standardized material to students and then measured learning outcomes and satisfaction with a survey and summative assessment. We created an iterative qualitative coding scheme, “coding online asynchronous lectures” (COAL), to analyze open-ended student survey responses. Our results reveal that multimedia learning can be satisfying and effective. Students have strong preferences for certain video styles despite their equal learning outcomes, with the Learning Glass style receiving the highest satisfaction ratings. Video styles that were described as impersonal and unfamiliar were rated poorly, while those that were described as personal and engaging and evoked positive affective responses were rated highly. The students in our study rated lecture video styles that aligned with Mayer’s multimedia learning principles as highly satisfying, indicating that student feedback can be a valuable resource for course designers to consider as they design their own online courses. Finally, we provide guidelines for creating engaging, effective, and satisfying asynchronous lecture videos to support establishment of best practices in online instruction.

Purpose – There is a growing requirement for a rating system of building information modelling maturity (BIMM) to compare the effectiveness of modelling processes in construction projects. The literature related to BIMM contains theoretical proposals and description of their maturity models. However, the research efforts are limited and lacking substantial theoretical and empirical justifications. This paper is a unique attempt to integrate previous models by performing empirical investigations of key factors for measuring BIMM in construction projects. The paper aims to discuss these issues. Design/methodology/approach – A national survey was designed to extract the perception of 124 BIM-related practitioners and academicians about the conceptual model. Then, exploratory and confirmatory factor analyses were employed to identify and test the key factors underlying the 27 areas. Findings – A principal component factor analysis of the collected data had suggested a five-factor model, which explained 69.839 per cent of the variance. The construct validity of the model was further tested by confirmatory factor analysis. The results indicated that all factors were important in measuring BIMM; however, compared with the factors of technology and people, more emphasis was put on the factors of process and information. Originality/value – The key value of the paper is to increase the understanding of multi-dimension nature of BIMM through empirical evidence and to provide practitioners and researchers with the insight regarding particular emphasis on the factors related to modelling process and information.

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