E-learning and Education (Jun 2017)
J-Quizmaker
Abstract
Instructional videos enjoy great popularity in knowledge transfer due to recent developments in the field of online teaching (video platforms, MOOCs) on the one hand and a huge selection as well as an easy production and distribution on the other hand. Nevertheless, videos lead to crucial disadvantages, which are in the nature of the data format. Thus, the search for specific contents in a video as well as the semantic processing for automated linkage with other related materials are associated with high expenditure. Consequently, the learning success-oriented selection of appropriate video segments and their arrangement to control individual learning processes are inhibited. While watching a video, already known facts may be repeated or can only be skipped by manually moving within the video. The same problem occurs when attempting to specifically repeat certain video sections. To solve this problem, a web application is introduced, which allows the semantic processing of videos towards adaptive learning contents: by integrating self-test tasks with defined follow-up activities, video segments can automatically be skipped or repeated and external contents are linked, based on the current user knowledge. The presented approach is based on an extension of the behaviorist learning theory of Branched Teaching Programs by Crowder, which includes learning progress-adapted sequences of learning units. At the same time, learner’s motivation and attention are promoted according to rules of Skinner’s Programmed Instruction and the reinforcement theory by regularly included self-test tasks. Additionally, by explicit distinction of related sections in the video, information are available in machine-readable form, so that further possibilities for finding and linking learning contents are established.
