Online learning has gained importance in education over the last 20 years, but the well-known problem of high dropout rates still persists. According to the multi-dimensional learning tasks model, the cognitive (over)load of learners is essential to attrition when dealing with five challenges (e.g. technology, user interface) of an online training (Tyler-Smith, 2006). The experienced load might depend on learner characteristics. The study explored the extent that learners dropping out from a vocational video-based online training about media design for employees of micro, small and medium-sized enterprises differ from working learners’ online learning experience, computer attitude, and computer anxiety. The data were collected from 72 of 128 registered employees who completed a questionnaire before starting the course to analyze differences between the dropout group (submitted no solutions to online training tasks; n = 19) and the active learner group (submitted at least one of 13 task solutions; n = 53). No differences were found in online learning experience, but the dropout group reported more negative attitudes towards computers and a higher level of computer anxiety than the active learner group.

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Background:According to research based on cognitive load theory, the way of presenting information in an instructional environment is essential to the learning outcome. By avoiding unnecessary extraneous load caused by badly designed instructions and other sources, learners are more likely to successfully construct knowledge. In addition, learner characteristics are known to affect learning.Objective:This study explores the effects of learners’ online learning experience, domain-specific prior knowledge, computer attitude and computer anxiety on their perceived intrinsic, extraneous and germane load and on their learning outcome in a video-based training course about media design for employees.Method and Results:Learning outcome was assessed by ratings of subjective learning success, ratings of professional competence, the number of completed modules and performance. None of the learning outcome variables could be modelled when entering learner characteristics in a regression analysis, but all could be modelled using the cognitive load ratings.Conclusion:Thus, extraneous, intrinsic and germane load were the most important factors for explaining the learning outcome. This result points to the importance of instructional design and particularly to managing cognitive load in online training scenarios.

Thomas Bieker, Malte Dreyer, Annamaria Köster, Gudrun Oevel und Nicole Terne befassen sich am Beispiel drei deutscher Hochschulen mit den Herausforderungen und Umsetzungen des digitalen Lehrens und Lernens zu Corona-Zeiten. Die Humboldt-Universität zu Berlin, die Hochschule Ruhr West sowie die Universität Paderborn geben einen Einblick in die die Umsetzung digitaler Lehr- und Lernformate sowie die dazugehörigen Herausforderungen, vor die IT- und Medien-Einrichtungen der Hochschulen gestellt wurden. Die Fallbeispiele werden mit Ergebnissen aus Studien verglichen und mit eigenen Schlussfolgerungen abgeschlossen.

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