Abstract
For this dissertation, Academic Support Technology (AST) is defined as any institutionalized technology activities and purchases that are performed in support of the instructional efforts of that institution. AST is the technology support that includes, but is not limited to, Student Information Systems, Learning Management Systems, Classroom Projection Systems, Lecture Capture Technology, Email, and research servers. It is suggested that a well-planned AST strategy can play a pivotal role in delivering quality online education. To fully endorse the implementation of online course technology, school administrators need to understand that the return on investment is heavily impacted by acceptance by their faculty, staff, and internal stakeholders. Lecture Capture Technology (LCT) is one such AST system that, although no longer considered ‘new,’ is being repurposed to support additional eLearning activities in the schools. The current study first investigates the broad usage of LCT to determine its validity. Then, it introduces an individual case of a particular use of LCT (i.e., Hyflex/Blendflex) in a K12 environment to provide insights as to what would need to be done to gain wider acceptance by stakeholders. Studies on how preconceived notions about LCT can affect its use and implementation have not appeared regularly or consistently in the literature until recently (Dommett, Gardner, & van Tilburg, 2019). It is presumed that a thorough review could help those in K-12 to implement LCT and other similar forms of virtual course delivery. This study attempts to provide answers for academic leaders who wish to implement online course delivery technologies in the future.For this study, Lecture Capture Technology (LCT) is defined as an underlying delivery platform that can record and broadcast live classroom activities for students. An everyday use of LCT in the literature is the flipped classroom, in which students review introductory and/or contextual information about lessons. The recordings are archived and searchable so students can review materials independently (Albert & Beatty, 2014; O'Flaherty & Phillips, 2015). More recently, and significantly, as there is a need to find alternative ways to deliver courses due to a global pandemic and other natural disasters, LCT has been significantly expanded to provide opportunities for those unable to attend class in person and who need to participate remotely. This alternative use of LCT course delivery is sometimes called Blendflex or Hyflex learning (Beatty, 2019; Wright, 2016).
Researchers have generally suggested that LCT can foster increased student learning outcomes without specifically differentiating its use in a flipped classroom or other alternative virtual course delivery modality (Albert & Beatty, 2014; Gautreau, 2011; Newton, Tucker, Dawson, & Currie, 2014; Raths, 2014; Villiers, McFarlane, O'Connor-Benson, & Felke, 2016). The difficulties with its implementation and/or lack of acceptance of K-12 teachers are not as well researched as their higher education counterparts and significantly less than students (Jensen, Kummer, & Godoy, Patricia D. d. M., 2015; Rui, Gupta, Grudin, & He, 2004; Zhang, Rui, Crawford, & He, 2005). Most of the studies and reviews of LCT have focused on its use in the post-secondary environment. Reviews of its use at the K-12 level appear scarce (Arnesen, Walters, Borup, & Barbour, 2020).
Therefore, the current study's purpose is to review and identify strategies used in a specific K -12 setting that attempt to increase the acceptance of LCT, especially for remote learning, and how teachers and staff perceive the attempt to adopt this unique use of LCT technology more widely. The theoretical basis of this study is the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate which factors or domains of the UTAUT can predict more accurately user perception(s) of the implementation of LCT and which factor(s) as noted in the survey administered to these teachers and staff are potentially the most relevant to their ability to predict student performance.