Making Sense of Video-Based Learning Analytics
Michail N. Giannakos, Konstantinos Chorianopoulos, and Nikos Chrisochoides
Norwegian University of Science and Technology, Norway, Ionian University, Greece, Old Dominion University, USA
Traditional lectures may no longer primarily serve the purpose of disseminating information, which can be easily retrieved from many online video lecture repositories at any time. Video lectures have given rise to flipped (or inverted) classrooms and even assist SPOCs (Fox, 2013). This specific type of blended-learning classroom utilizes technology, such as video, to move lectures outside the classroom, thereby giving students and teachers time for active learning in the classroom (Roehl, 2012). By using learning materials as a supplement to classroom teaching rather than being viewed as a replacement for it, those techniques are attempting to increase instructor leverage, student throughput, student mastery, and engagement (Fox, 2013). At the same time, recent technical and infrastructural developments (Giannakos, 2013) make the potential of video-assisted learning ripe for exploration. Capturing and sharing learners’ diverse interactions with the emerging learning technologies can clearly provide scholars and educators with valuable information.
With the widespread adoption of online video lecture communities, such as Khan Academy and VideoLectures.net, conducting research to understand how students learn via video lectures has become critical. Despite the present significant body of related research into the impact of video lectures (Giannakos, 2013), the majority of previous efforts have mainly focused on: a sporadic or single use of video lectures in an educational context (Evans, 2008) and/or the investigation of only one factor like student performance (Kazlauskas and Robinson, 2012). Therefore, the longitudinal collection of diverse LA and the interpretation of them through triangulation will allow us to better understand how students learn and interact with videos.
In particular, this study was designed to assess and make sense of the analytics within video lectures and to investigate the relationship between these analytics and students’ attitudes and learning performance. To do so, we designed and deployed a longitudinal (7—week) study based on our video learning analytics system. Using this system, we collected and analyzed students’: 1) video navigation, 2) learning performance, and 3) attitudes; based on these diverse sources, learning analytics, and the respective interpretation through data triangulation, we provide the new information for further development and refinement of video-assisted courses and practices.
When learners watch the videos, questions are embedded to test if they are paying attention, the questionnaire consisted of very simple questions that could not be answered through previous knowledge of the users.
The authors transformed the system into an online service (http://www.socialskip.org/; see also Chorianopoulos et al. 2014). Users visit the link rather than going through an installation process. If there is an updated version, they simply have to refresh the page. Additionally, the system’s architecture is modular and allows re-use of the components. Web developers might employ the open-source application logic (https://code.google.com/p/socialskip/) in order to develop new or custom features. Practically every user with a Google account can be a researcher.
Some interesting findings:
- Video lecture production results in different video navigations, which affects students’ learning performance; and that “attractive” videos result in better learning outcomes.
- The main quality of the “attractive” video segments was the rich and useful amount of transferred information and knowledge.(the video segments with information related to the answers of the assessment and at the segments where the presenter was giving the solution of the respective problem)
- There is a correspondence between the level of cognition/thinking each question required and the size of the respective activity peak. Using the revised taxonomy of Bloom, it’s found that all the global maximums were identified in questions where higher order thinking/cognitive skills was required.