Video and multimedia technologies are becoming more prominent in the world of education. Most of today’s learning environments have video affordances. In addition, advanced video repository systems have seen enormous growth (e.g., Khan Academy, Lynda.com, PBS Teachers) through social software tools and the possibilities to enhance videos on them. During the last years we have seen several research studies in interactive and innovative features, such as slide-video separation, annotation, social categorization and navigation, and advanced search, that have become standards for any state of-the-art video-based learning system (Kim et al., 2014; Kleftodimos & Evangelidis, 2016a; Wachtler & Ebner, 2015). Thus, video-based learning is an emerging field with its elements available in most of the contemporary learning systems. In this section, we will see aspects related with video-based learning, the analytics around that as well as the potentialities for integrating “smartness”.
Video-based learning
The advances of technology-supported open access to education indicate an increased use of video technology; video technology has tremendous potential when pedagogically appropriate and designed purposely to facilitate teaching and learning. From current research, it is difficult to tell what aspects of the video-lectures and video-based learning systems can have a positive impact. In order to employ videos that serve as powerful pedagogical tools, care should be taken to examine their impact on the overall learner experience. As such, exploring how smart learning analytics can help us to improve video-systems learning potential is of great importance.
Existing empirical research (e.g., Giannakos, 2013) has begun to identify the educational advantages and disadvantages of video-based learning. However, there still remain many essential unexplored aspects of video-based learning and the related challenges and opportunities; such as, how to use all the data obtained from the learner, how to combine data from different sources, how to make sense heterogeneous learning analytics, how to synchronize and take the full advantage of learning analytics coming from different sources, how to use analytics to inform and tune smart learning and so on. Videos have long been used for learning, for instance more than forty years ago Spivack (1973) used VHS video simulations to help train counselors to be more effective. Throughout the years the video formats, quality and delivery (e.g., CD, DVD, VHS, web) have changed dramatically. Today what makes again video-based learning ripe for exploration is the possibility to incorporate interactive elements and smart behavior and enable effective, efficient, engaging and personalized learning. In this turning point, smart learning analytics is expected to have critical role.
Video learning analytics
Millions of learners enjoy video streaming from different platforms (e.g., YouTube, Coursera, Khan Academy, EdX, Udacity, Iversity) on a diverse number of devices (desktop, smart phone, tablets) and create large volume of interactions. This amount of learning activity might be converted via analytics into useful information (Giannakos et al., 2015) for the benefit of all video learners. As the number of learners' watching videos on Web-based systems increases, more and more interactions have the potential to be gathered. Capturing, sharing and analyzing these interactions (big datasets) can clearly provide scholars and educators with valuable information (Giannakos et al., 2015). We also expect that the combination of various learning analytics (e.g., content metadata, learners’ profile) as well as the state-of-the-art statistical analysis techniques (e.g., Kidziński et al. 2015; Pappas et al., 2016) will allow us to better understand complex learning phenomena by making sense of heterogeneous big learning analytics; this is of particular interest to video-based learning due to the large and complex datasets.
Smart video-based learning
The International Association for Smart Learning Environments (IASLE: http://www.iasle.net/) provides a broad interpretation of what a smart learning environment is. In particular, IASLE states that a learning environment can be considered smart when various innovative features and attributes like adaptation, flexibility, thoughtfulness and so on are associated with the system (Spector, 2014). In a general sense, a smart learning can be described as a learning process characterized from effectiveness, efficient and engaging for a wide variety of learners with different levels of prior knowledge (adaptation affordances). Smart learning is enabled by technologies that rely on sensors, big data, open data, new ways of connectivity and exchange of information (e.g., Internet of Things, RFIDs); those integrated environments belong on the broad sense of smart learning environments. Like any other type of learning environments, video-based learning environments need to follow the same principles, and while video-based learning environments are becoming more flexible, thoughtful and adaptive (e.g., Khan Academy, Udacity) as well as several new such environments that incorporate “smart behavior” are created (e.g., Adaptemy, Dreambox, SmartSparrow); there is a lack of empirical analytics-based research on what ingredients of smart behavior can indeed increase effectiveness, efficiency and engagement. There is therefore a need to conduct empirically-driven research in the area of Smart Learning Analytics.