Effectiveness of the Asynchronous VBL Delivery System on Learning Outcomes: Analysis of Learner Control

Throughout the 2000s, there has been a shift in video-based learning (VBL) research towards asynchronous delivery, primarily due to the advancement of mobile technology, learners using non-interactive forms of VBL (Giannakos, 2013) and the exponential growth of online video production (Snelson, 2011). Martin (2016) found only one in 93 students did not have access to a mobile device to view short instructional video clips on YouTube, with little to no streaming and internet access issues reported. Such access suggests asynchronous VBL is common, and its use is likely on the rise. Considering this, asynchronous VBL technology will need to evolve along with the growing demand of mobile learners. Yet, most of the current research focuses on pedagogy and not the impact delivery system control has on learning. Accordingly, this paper explores the effectiveness of the asynchronous VBL delivery system on learning outcomes by analyzing the learner’s level of control over the delivery system and how these relationships affect human learning. An evaluation of the LinkedIn Learning delivery system and several research studies on VBL best practices and associated learning theories inform the findings in this paper. Asynchronous VBL delivery systems offer multiple levels of control that aid human learning, but learning benefits are highly subject to instructional design quality and learner motivation to activate learning through supplementary learning activities.


The term “asynchronous video-based learning” refers to learning through digitally mediated instructional videos in a different time and space from both the instructor and other learners (Jones & Paolucci, 1999). The technology utilizes the video delivery system as its primary learning tool, while supplementary learning activities such as recall exercises, quizzes, and other forms of practice activate working memory into usable skills and knowledge. Learners generally have limited access to instructors, resulting in a lack of learner-instructor interactivity, as is the case with LinkedIn Learning. The LinkedIn Learning platform is a standalone educational platform that offers various courses ranging from business to software programming and is reputable as an effective corporate training platform. LinkedIn learners can engage with pre-determined learning paths or create their own learning path by compiling various courses and learning activities. The LinkedIn learning experience features standard VBL technology, including fundamental video playback control, as well as various learning activities and social elements (e.g., chat) to facilitate learner connectedness and collaboration.


Jones and Paolucci’s (1999) framework for evaluating the effectiveness of educational technology (EdTech) systems on learning outcomes underpins this analysis of the asynchronous VBL delivery system. The framework targets three EdTech system characteristics that can influence learning: Instructional objectives (input), delivery system (process), and learning outcomes (output) (Jones & Paolucci, 1999). The framework design acknowledges the importance of aligning learning conditions with the capability and design of the delivery system to generate desired learning outcomes (Jones & Paolucci, 1999). Weller (2020) and Mayer (2021) state the importance of innovating Edtech to aid human learning as opposed to developing disruptive technologies. This notion demonstrates the primary strength of Jones and Paolucci’s framework: The EdTech system must first compliment the learner and satisfy the learning conditions before the effectiveness of the delivery system design can be accurately evaluated. This concept underpins the rationale for utilizing the framework to evaluate the asynchronous VBL delivery system. According to the framework, assessment of the delivery system should focus on control, presence, media, and connectedness. This paper addresses learner control only and will require further investigation of the remaining system characteristics to return complete findings. Evaluation of the LinkedIn Learning delivery system controls focus on three key system characteristics: Video library, video interface, and learning activities.


Video library

LinkedIn learners, much like YouTubers, have direct control over deep video libraries, which span a variety of subjects. For instance, learners studying software development can easily navigate related learning paths to supplement their learning. This aspect of the delivery system is learner-centered, where the learner acts as an explorer, searching through content and generating information pathways that fit their specific needs. Two primary learning processes result from this level of exploration, which directly and indirectly impacts learning outcomes: Video content sampling and the creation of personalized learning paths.

Learners can sample content to find video styles with media qualities that suit their learning preferences to improve learning outcomes. Choe et al. (2019) discovered that many learners prefer the learning glass video style over commonly used styles such as the talking head and pen and tablet due to the “positive engagement and connection with the instructor” (p.7). Brame (2015) explains videos that include visual elements such as animation with narration promote not only student understanding (e.g., better transfer on tests (Sorden, 2005)) but also engagement with the lesson. Kreiner (1997) discusses how videos with guided questions lead to improved retention and transfer on tests. Whether centered around pedagogy or production quality, the choice of media in the instructional video plays a crucial role in fostering ideal learning outcomes.

Although the learning benefits associated with access and control over video libraries are debatable, the process of creating an educational pathway through video playlists can indirectly impact learning. Mayer (2004) suggests discovery-based approaches are less effective than guided ones, arguing the “debate about discovery has been replayed many times in education, but each time, the evidence has favoured a guided approach to learning” (p.18). Weller (2011) acknowledges that with everyone able to publish content over the Web, “the dangers inherent in constructivism become more pronounced” (p.8), but since everyone must operate within the same environment, the ability to construct appropriate and rigorous knowledge from a range of sources is even more relevant. From an inquiry- and connectivist-based learning perspective, building educational learning paths encourages learners to think critically about what they need to know and how they want to learn it, which helps establish critical thinking skills, systems thinking, media literacy, and self-directional skills (Organisation for Economic Co-operation and Development, 2010). Further, compiling courses and videos from an array of learning pathways utilizes established networks of information that foster a diversity of opinions to support learning and knowledge acquisition, a method that sets learners’ ability to “see connections between fields, ideas, and concepts” (Weller, 2011, p.9).

In this case, the potential skill development through the discovery process, the creation of personalized learning paths via video libraries, is considered an employable skill (Organisation for Economic Co-operation and Development, 2010) and, from the perspective of this author, supports the learning outcomes of life-long learners. Although asynchronous video-based learners can benefit from the control of video libraries, its effectiveness solely depends on their motivation to expend the time and energy to sift through video content skillfully and meaningfully. Not all learners possess the capacity to do so, which suggests this aspect of control is dependent on the individual, making its impact on learning outcomes highly situational.

Video Interface

The LinkedIn Learning video interface, as with most online video interfaces, contains a set of controls that allow learners to manipulate many aspects of video playback which can influence learning outcomes. Learners have direct control of the video interface, garnering it mostly learner-centered, although control is dependent upon the capabilities of the technology instance. For example, some video interfaces incorporate on-screen interactive controls that utilize prompted questions and answers to personalize video content in real-time (e.g., guided questions or H5P (2021)), while other interfaces, such as LinkedIn Learning’s, contain only traditional controls (e.g., start, stop, pause, rewind, fast forward, etc.). The LinkedIn Learning video interface contains two most notable controls which are shown to influence learning outcomes: The start-stop function and content segmentation options.

Studies show the start-stop function generally improves learning (Zhang, Zhoe, Briggs, and Nunamaker, 2006). The ability to rewatch lectures offers learners the opportunity to deeply contemplate learning material. If one section of the lecture is complex and confusing, the learner can simply pause the video and replay the section until the information makes sense. Further, by stopping the video at certain points, the learner has effectively segmented the content into shorter durations or chunks. Gou et al. (2014) observed “the median engagement time for videos less than six minutes long was close to 100%” (Brame, 2015, p.4); meaning, students tend to watch the whole video when the presentation is 6 minutes or shorter. As video duration increases, student engagement drops off along with learning outcomes: “Making videos longer than 6-9 minutes is … likely to be a wasted effort” (Brame, 2015, p.4). Considering this, when students shorten their viewing sessions through segmentation, they are more likely to be engaged in the material and may put more effort into understanding the material as they watch, thus increasing the potential for meaningful learning to occur (Mayer, 2021).

The LinkedIn Learning video interface also features video content and transcript sections that encourage learners to segment content. The video content section offers learners quick access to specific chunks of courses, sorted by minutes and seconds, whereas the transcript section allows learners to select narrated text to link to that part of the video. Both options are under the complete control of the learner and aid human learning. Such design practices conform to Mayer’s segmentation principle, in that video content is broken into small segments which are presented sequentially and allow learners control of pacing from one part to the next (Brame, 2015; Mayer, 2021). The learning benefits of content segmentation are plentiful and well understood in current literature. Sorden (2005) states that better transfer occurs when the pace of the presentation is controlled by the learner. Sung and Mayer (2013) evaluated student performance on a subsequent transfer posttest after watching a 6-minute narrated multimedia lesson which resulted in the segmented group outperforming the non-segmented group with an effect size of d = 0.67. Sweller (1998), Mayer (2021), and Brame (2015) suggest multimedia segmentation (e.g., video, narrated animation, etc.) manages intrinsic load so the learner can mentally represent the complexity of the essential material and improves germane load by emphasizing the structure of the information.

All such learning benefits are however dependent upon the complexity of the lesson, the pace, and the experience of the learner. Mayer (2021) notes that multimedia segmentation effects are greatest when learners are a novice, have limited working memory capacities, and the lesson is cognitively challenging; this notion suggests that not all learners receive equal benefits from video segmentation, but as many studies suggest, most learners will be more engaged in the content (Guo et al., 2014; Ibrahim et al., 2012; Brame, 2015), which can indirectly influence learning outcomes. By enabling learners’ control of video playback and presenting them with various features of content segmentation, like LinkedIn Learning’s interface affords, there is a strong correlation with improved learner performance and learning outcomes, making this aspect of control essential to learner success.

Learning Activities 

Asynchronous VBL and the LinkedIn Learning platform offer learners complete control over how they learn instructional video content. Learners can take a passive approach, where they extent little effort to understand and activate learning material during and after video playback; an active approach, where they consciously attempt to understand the presented material during playback and participate in associated supplementary learning activities (e.g., LinkedIn Learning offers exercise files, a notebook, and quizzes as learning activities), or a combination of the two. Whatever the approach, it will affect the learning outcome.

For instance, Mayer’s (2021) generative activity principle states that “people learn better when they are guided in carrying out generative learning activities during learning (e.g. mapping, imagining, summarizing, drawing, self-testing, self-explaining, teaching, or enacting)” (p.370).  Mayer explains that such activity causes learners to cognitively process the learning material by doing such things as “selecting important material, mentally organizing it into a coherent structure, and integrating it with relevant prior knowledge activated from long-term memory” (p.370). The result of such activity is shown to improve learner performance on transfer tests as compared to those that do not actively consume learning presentations (Mayer, 2021). Further, learners who choose cognitive learning activities, in addition to watching instructional video content, are shown to generate meaningful learning outcomes that produce good retention of the learning material (Mayer, 2021). Research shows that well-design questions, through exercise files, quizzes, or instructor prompts, can potentially improve learning by 150% via the following: (1) provide learners with practise retrieving information from memory, (2) give learners feedback about their misconceptions, (3) focus learners’ attention on the most important learning material, and (4) repeat core concepts, giving learners a second chance to learn, relearn, or reinforce what they previously learned or tried to learn (Thalheimer, 2014).

The challenge with asynchronous VBL learning activities is that oftentimes the guidance Mayer (2004) speaks to is missing; meaning, the learners are 100% accountable for their learning activation, unless an instructor or course guidance is offered. In the case of LinkedIn Learning, learners are free to choose whether to watch an entire video, take notes, complete exercises, etc., or to passively consume the information to later return little to no learning outcome. The level of control for this aspect of the delivery system is learner-centered, but perhaps this dyad is only suitable for certain types of learners, garnering its effectiveness inconclusive.


Learner control over the LinkedIn Learning delivery system is statistically linked to solid learning outcomes, but only when learners are active participants throughout the entire learning experience. Video library control enables learners to consume instructional video styles that suit their learning needs and preferences. Still, not all learners possess the dedication and knowledge to find complimentary video styles, nor do all LinkedIn Learning courses offer multiple video styles on the same subject matter. Hence, the benefit of matching video styles with learner preferences is likely inconsistent without supplementary videos from other platforms. Further, there is a general lack of consistency pertaining to video production quality within LinkedIn Learning plans, limiting the effectiveness of some courses to produce ideal learning outcomes. Considering many learning plans are sequential, learners may not be prepared for later courses if they cannot cognitively engage in foundational courses. Establishing and regulating a new standard of video production (e.g., media used, instructor prompts, etc.) could enhance the effect of video library control. The video interface offers control over accessibility options, playback, and segmentation, but learner-content and learner-instructor interactivity are limited, negatively impacting learner engagement. As a remedy, the introduction of interactive technologies such as H5P or even virtual reality in some instances should be considered, especially as online education moves towards fully immersive learning experiences. However, there is a delicate balance between integrating interactive learning elements and overloading a learner’s cognitive processing capacity, so the incorporation of interactive video elements should conform to Mayer’s Theory of Multimedia Learning to return ideal learning outcomes. Lastly, supplementary learning activity offerings are consistent throughout all LinkedIn Learning plans. Yet, additional motivational strategies are needed to engage an increasingly diverse mobile learner population if video content remains instructor-centered and lecture-focused.

Research into the impact of learner control over the asynchronous VBL delivery system on learning outcomes can act as a guide for pedagogical strategy and technological innovation. By understanding the relationships between VBL delivery system control and human learning, in addition to further establishing VBL pedagogical best practices through research, innovations of VBL technology are more likely to aid learning outcomes as opposed to disrupting them through a technology-centered lens. As stated earlier, the findings in this paper are inconclusive since the analysis did not account for all delivery system characteristics. Further research into the effectiveness of the remaining delivery system characteristics is recommended.




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