Browsing by Author "Olayiwola, Johnson Tumininu"

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    Leveraging Artificial Intelligence for Improving Students' Noticing of Practice during Virtual Site Visits
    Olayiwola, Johnson Tumininu (Virginia Tech, 2023-01-11)
    Complementing the theoretical concepts taught in the classroom with practice has been known to enhance students' contextual understanding of the subject matter. Exposing students to practical knowledge is crucial as employers are expressing discontent with the skills of newly hired graduates. In construction education, site visits have been identified as one of the most effective tools to support theory with practice. While site visits allow students to observe construction projects and engage with field personnel, numerous barriers limit its use as an effective educational tool. For instance, there are safety, cost, schedule, and weather constraints, in addition to the logistics of accommodating large class sizes. As a result, instructors employ videos of construction projects as an alternative to physical site visits. However, videos alone are insufficient to draw students' attention to essential practice concepts. Annotations can be used to attract students' attention to practical knowledge while reducing distractions and assumptions. Leveraging on the recent progress in computer vision techniques, this study presents an AI-annotated video learning tool that instructors can utilize to equip students with practice knowledge when there is limited access to physical construction sites. First, this study investigated the construction practice concepts that industry practitioners would want students to know when engaging them in site visits. Afterward, the design and development of the AI-annotated learning tool were guided by the identified practice concepts, cognitive theory of multimedia learning, and dual coding theory. To determine if the learning tool can call students' attention to annotated practice concepts in videos, a usability evaluation was conducted. Finally, this research investigated the influence of individual differences that could contribute to how learners notice practice concepts in videos. This study contributes to the body of knowledge by identifying what construction professionals notice about their work and what they would like students to notice about construction practice. This study reveals that annotations of learning contents in construction videos can direct students' focus to the annotated contents, thereby contributing to the cognitive theory of multimedia learning and dual coding theory. By leveraging machine learning classification algorithms, this research identified the extent to which individual differences such as gender, academic program, and cognitive load can be detected from the ways students notice information in construction videos. Results from this research provide opportunities for researchers to further advance the potential of annotated videos in the construction domain and other fields that employ video as a learning tool.