@article{Amigud_2013, title={Institutional level identity control strategies in the distance education environment: A survey of administrative staff}, volume={14}, url={https://www.irrodl.org/index.php/irrodl/article/view/1541}, DOI={10.19173/irrodl.v14i5.1541}, abstractNote={<p>Physical separation of students and instructors creates the gap of anonymity and limited control over the remote learning environment. The ability of academic institutions to authenticate students and validate authorship of academic work at various points during a course is necessary for preserving not only perceived credibility but also public safety. With the growing scope of distance education programs that permeate critical areas such as healthcare, airspace, water management, and food solutions, universities have a moral obligation to employ secure measures to verify learning outcomes. This study examines the measures universities with large distance education programs employ to align identity of learners with the academic work they do, as well as the effectiveness of and challenges and barriers to their implementation. The research was undertaken using a multiple case approach and examined survey responses from five academic administrators at five officially accredited post secondary institutions in three countries. The cases examined in the study include: Athabasca University, Open University UK, Penn State University World Campus, University of Maryland University College, and eConcordia,Concordia University’s distance learning facility. This study is not an exhaustive attempt to examine all aspects of academic integrity, but rather to create awareness about various learner authentication strategies. This study confirms that secure learner authentication in the distance education environment is possible. However, with greater pressure to enhance security of learner authentication, the openness of open learning is challenged and may change as we know it.</p>}, number={5}, journal={The International Review of Research in Open and Distributed Learning}, author={Amigud, Alexander}, year={2013}, month={Dec.} }