By Julie A. Vera, David W. McDonald, and Mark Zachry
https://doi.org/10.55177/tc152088
Abstract
Purpose: TikTok’s rise in popularity has invited creators across a broad spectrum of interests to contribute content to the platform, including non-expert, instructional subject matter. Previously, technical communication scholars have described ways to assess video instruction online, in relatively long-format lengths. Our project outlines a framework for assessing the video production qualities of instructional content across TikTok.
Method: We performed a content analysis of existing frameworks and sets of heuristics for assessing long-format instructional videos. We then analyzed a set of instructional content found across the TikTok platform and analyzed them using previous frameworks. After comparing and contrasting, we developed a new framework for assessing short-format video instructional content.
Results: After assessing long-format instructional video frameworks and instructional content found across TikTok, we found that many dimensions and heuristics from previous frameworks applied to short-format video. Unique to short-form video were the dimensions of tempo and level of detail, which describe the pacing of the video from a temporal perspective and the fidelity of instruction, respectively. Instruction on TikTok can take place without explicit step-by-step instruction.
Conclusion: We found that many dimensions and heuristics from long-form frameworks carry over to short-form video, but there are features, social norms, and creative norms on TikTok that lend themselves well to “bite-sized” instruction.
Keywords: instruction, TikTok, multimedia, short-form, video
Practitioner’s Takeaway:
- There is a wealth of scholarly literature on the topic of instructional content found across the web, particularly on video instruction.
- Early literature on video instruction assessed rhetorical dimensions for assessing the content, such as design, task orientation, and error representation.
- We introduce dimensions that are relevant to short-form video with special attention paid to the level of detail within instruction as well as the temporal structure of instruction.
- Our analysis shows that while many production qualities from long-format instructional video can apply to short-form, there are dimensions that are more important for short-form video, such as tempo and the level of detail in instruction.
Introduction
TikTok, the short-form video platform, has seen a meteoric rise in popularity over the past few years. With the primary method of contributing to the platform in the palm of our hands, it is no surprise that 83% of the TikTok user base has posted at least one video (Doyle, 2023). As more short-form content comes online, it is critical to understand how “older” forms of content are being translated to a new, shorter medium. We have seen, over the past decade, a massive shift of content from written mediums to video mediums, especially on the topic of how-to, DIY, and instructional content. YouTube is an example of a platform that has been used widely for instructional content, by both creators and people who require instruction or simply want to learn something new. YouTube has also given increased power to amateur creators. It has never been so easy to record, edit, and upload content for others to see. A creator can choose formal or informal styles of video. The affordances of the platform also allow for conversation to take place, giving space for the social construction of better creations. Historically, YouTube has been the go-to platform to find instructional content on almost any subject. But, how do the production characteristics change when instruction is portrayed in much shorter periods of time? In this study, we investigated what instructional, how-to, DIY, tutorial-like, and other instructional video looks like on TikTok and how we can begin to assess its production features. Short-form video, and TikTok in particular, may have important implications not just for informal learning but also for instructors and content creators who communicate instruction to others.
Background
Instructional content has a long history in digital spaces, and much of that content relies exclusively on the content creators, makers, educators, and DIY’ers sharing their knowledge with the public. With how-to content predating digital platforms and formats, mostly in the form of written communication or video lecture-style demonstrations, it is almost an inevitability that instructional content would eventually thrive on social media and social video platforms such as YouTube, Vine, and TikTok. To explore the relevant literature, we will briefly outline our definition of instructional or how-to content. Then, we will address the topic of digital instructional content and early frameworks for assessing efficacy, content, and structure, primarily on YouTube. We will then explore the concept of short-form video and specific affordances available on the TikTok platform as well as modalities that are not available on long-format platforms, such as YouTube. Lastly, we will briefly note some of the more recent applications of frameworks from the era of YouTube tutorials.
What is Instructional Content?
In its most basic form, a how-to video is “a video that gives basic, step-by-step instructions on how to accomplish a certain task” (Purcariu, 2019, p. 65). Bétrancourt and Benetos (2018) spoke of YouTube tutorials as “demonstrating how to perform a procedure such as mathematic computation, hand manipulation tasks, professional behavior, or software operations” (p. 472). Instructional content can also describe another, more nuanced genre of content: the do-it-yourself (DIY). Wolf described an array of online DIY content, including material related to home life (home repair, decoration, cooking, and gardening), crafting, personal style and fashion, making and tinkering with computers, and many more areas of niche interests. Although the subject matter in DIY instruction and culture can address nearly any topic, “the common thread is that individuals ‘do-it-yourself,’ meaning amateur, untrained individuals learn how to do specialized, expert tasks” (Wolf, 2016, para. 3). ten Hove and van der Meij also found a wide range of instructional, DIY-related content on YouTube (ten Hove & van der Meij, 2015). Van Ittersum pointed toward Moeller and McAllister’s call to “reclaim the techne – creative, ingenious, tricky, unpredictable, and utterly human,” in Craft and Narrative in DIY Instruction (Moeller & McAllister, 2002, p. 204; Van Ittersum, 2014, p. 227). From the literature, we can determine that the descriptors of how-to, instructional, tutorial, educational, do-it-yourself (DIY), or explanatory are similar enough to be analyzed systematically. While each term we use for instructional content has nuance, they all attempt to describe a phenomenon: a method of learning. Even from the instructor’s perspective, the idea is similar: an instructor, amateur or not, explains an idea, process, or concept.
Frameworks for Analyzing Instructional Content
The analysis and characterization of video instructional content has roots in many disciplines and modalities, from technical communication to rhetoric to media studies. From a technical communication standpoint, there is an early moment in which we can use to situate our analysis, which begins with the work of David Farkas and The Logical and Rhetorical Construction of Procedural Discourse (Farkas, 1999). In this paper, Farkas argues that “not every form of procedural discourse…consists of distinct procedures” (p. 42). The paper outlines several relationships that underlie procedural discourse but does so with the understanding that procedures exist within a social context: Information must be designed with the end user in mind. While the paper largely aims to outline a step model for delivering instruction, we get a glimpse of other social features that exist within instructional contexts, such as the credibility of the person explaining the content or “‘selling’ the domain itself” (p. 44) as something the user can realistically understand and master.
The following year, Carliner developed a framework for broadening our understanding of information design to serve a more complex organizational and informational landscape (Carliner, 2000). He argued that we must consider broader channels of communicating information (graphics, text, and reader goals); otherwise, information design would simply remain stuck in “document design” mode, which is primarily concerned with text and document appearance. Carliner outlined three levels of information design: physical design (the ability to find information), cognitive design (the ability to understand information), and affective design (the ability to feel comfortable with the presentation of information) (Carliner, 2000). In this framework, video as a modality is only mentioned twice—once in relation to the cognitive design of a communication product and once in relation to the physical design.
Post-2000, we start to see a shift in the rhetoric of the internet itself: What was once an electronic connection between two parties for a specific purpose had evolved to become a vast array of interfaces, tools, and networks of people operating those instruments. It is around this time that we start thinking about an all-encompassing rhetoric that can account for the communication of instruction that is taking place in online contexts. Selber observed in 2010 that “what is noteworthy, however, [are] the ways in which the genres of technical communication are being articulated and rearticulated on the World Wide Web” (p. 95). Referencing Miller’s work on textual features and meaning-making within sociotechnical contexts, Selber notes, “As the activities and settings of workers evolve in the context of sociotechnical development, so too do the genres of technical communication” (Miller, 1984; Selber, 2010, p. 96). What is also significant in this period of time is how instruction is being approached in different sociotechnical contexts:
These interfaces no longer position data and information—or people, for that matter—in one context or another. Nor do they care very much about the boundaries the field has used to define technical communication. Although the range of user-generated content is extensive and includes a wide variety of materials (new media and not), instructional discourse occupies a conspicuous position in the landscape of online participatory culture. (Selber, 2010, p. 99)
By understanding the complexity behind the changing contexts, especially as many more people begin to gain access to the internet, we can see the need for descriptive models or heuristics of instructional material. Selber (2010) described a model in which there are “four dimensions in which to imagine the territories of self-contained, embedded, and open instruction sets: metaphors, modes, activities, and emphases” (p. 111). On another axis, instruction sets are described as self-contained, embedded, and open. Self-contained instruction sets are tightly bound, containing unmovable, fixed instruction. At the other end of the spectrum, open sets encourage regular folks to become “authors and editors” of instruction sets.
Starting in 2012, platforms like YouTube are given serious scholarly attention. Morain and Swarts pioneered this topic with YouTutorial (Morain & Swarts, 2012) greatly on the levels of information design put forth by Carliner in 2000. In their study, Morain and Swarts (2012) investigate YouTube tutorials in the context of enabling students to become “critical consumers and eventual producers” (p. 7) of educational content, particularly YouTube videos. The authors also indirectly add to the work of Carliner by expanding the three-dimensional framework to include points of assessment related to modes (sound and visual information) and rhetorical work (how information was conveyed: explaining, demonstrating, doing).
That same year, Ploetzner and Lowe developed a sweeping survey of the main characterizations of expository animations that have been identified for research purposes. Their analysis identifies 30 different topics in 14 different domains. While not necessarily referring to their own thematic analysis of the topics as a framework, the authors provided a framework for the evaluation and consideration of future work. They found the overarching themes of presentation, user control, scaffolding, and configuration. Each theme contains sub-themes that expand it. For example, presentation, which was defined as “the fundamental characteristic of an animation is how it presents the subject matter to the learners,” (p. 784) contains the sub-themes of representations employed, abstraction, explanatory focus, viewer perspective, spatiotemporal arrangement, and duration (Ploetzner & Lowe, 2012).
The following year, Pflugfelder developed a model for “reconceptualizing a form of short video instruction manual” (2013, p. 131). Using principles from IBM, Pflugfelder argued that designers of educational content should adhere to minimal practices in instructional video; minimalism as a design heuristic is more human-centered and can help both instructors and students in the classroom. Pflugfelder had students assess the content and effectiveness of a “web app” video along dimensions such as language use, task orientation, guided exploration, correspondence, action, entertainment, production, and error representation (Pflugfelder, 2013).
Most recently, in 2015, ten Hove and van der Meij explored a set of 250 instructional videos from across YouTube along an axis of popularity. In their analysis, they found that there are several factors that predict whether or not a video is successful and popular on the platform. Videos with more success tend to have higher production quality, frequent pictures/overlays, more dynamics in terms of static/animated content, short on-screen text, subtitling in different languages, background music, less background noise, and a faster rate of speaking (ten Hove & van der Meij, 2015). Table 1 provides a summary of the dimensions related to the assessment of long-form instruction.
The reviewed literature illustrates the evolution of frameworks for assessing instructional or tutorial-like content across the web, particularly via video. This is certainly not a comprehensive overview of all video tutorial frameworks in existence; in fact, we sought out frameworks that specifically were meant to describe video content, not necessarily judge the effectiveness of it or provide heuristics on how to create better video instruction. For this reason, frameworks such as Eleven Guidelines for the Design of Instructional Videos for Software Training (van der Meij & Hopfner, 2022) were left out of our analysis.
On the surface, much like Selber (2010) mentioned, very little changes with the use of a framework to understand instructional communication in sociotechnical contexts. People will find new and interesting ways to instruct using the tools available and will combine them with innate human creativity. Fitting with this trajectory, our study explores the next chapter in this developing social trend. Guided by this previous work, we have questions to motivate our exploration of instructional content in short-form, internet-based videos: How can we leverage rhetorical and technical frameworks to help educators reach different audiences with different needs? As communication on the internet shifts toward more informal styles, with ordinary people often supplying most of the instructional content, how might we use frameworks to help “non-educators” contribute their knowledge?
A Short History of Short-Form Video
Short-format video is a relatively new phenomenon. Perhaps one of the earliest platforms to embrace short-format video was Vine, which was bought by Twitter in 2012 and subsequently shut down fully in 2017 (Newton, 2016). Vine was a mobile-only platform for creating micro-video content with a maximum length of 6 seconds. Similar to TikTok, Vine contained features and functionalities such as “Home/Feed,” “Discover,” “Activity,” “Profile,” and “Explore.” It was also possible to search for specific content using natural language and hashtags (Wightman, 2020).
Since the emergence of Vine, various other media and social platforms have experimented with short-form video, including Snapchat, Instagram, Facebook, and YouTube. However, none of the short-form features in these platforms have approached the success of TikTok. TikTok is a popular social media platform where users create and share short-format videos via their mobile devices. TikTok has exploded in popularity in recent years. The mobile app was released in 2016 by its parent company, ByteDance (Fannin, 2019). By the first quarter of 2022, the app had 1 billion global active users (Ruby, 2022) and 3 billion total installs (Chan, 2021). As of the second quarter of 2022, it was reported that 67% of American teens have used TikTok (Vogels et al., 2022). Approximately 25% of TikTok content creators are aged 10–19, with a fairly even distribution of users in other age ranges, with an exception of a steep drop-off at around age 50 (Connell, 2021). Approximately 83% of the TikTok user base has posted at least one video (Doyle, 2023).
Like Vine, TikTok contains some typical features that allow a platform like this to grow, including ways of finding videos (universal search), ways of sharing (video embed code, video links, direct messages to other users), ways of signaling (number of likes, number of shares, number of bookmarks), and “sticky” features that enable conversations to occur and keep going (comments, video “stitching”). One innovative feature of TikTok is the way the platform encourages the inclusion of sound in videos. Users are able to search through sounds to find trending sounds (typically new music releases), soundscapes (audio without words), and audio memes (audio that is a sound effect or expresses an idea that is then replicated across many spheres of TikTok content) that add an extra layer of information to create with. Additionally, TikTok users can use a wide range of modifications to their videos, including adding title text on top of the video itself, creating accessible content via closed captioning overlays, using greenscreens, and including videos created outside the platform.
Emergent studies on TikTok and instructional content
Our study aims to capture how “old” concepts like instructional or how-to videos translate into today’s social media landscape, particularly on TikTok. Some studies have asked whether TikTok could be used as a tool in the classroom (Middleton, 2022). Recent studies have also assessed motivations for joining TikTok and sharing videos. Lu and Lu found the category of “knowledge sharing” as one area that TikTok viewers were highly engaged with. Videos that shared knowledge “[covered] one or two key points of certain knowledge” (Lu and Lu, 2019, p. 239). Instructional frameworks have also been used to help describe the technicalities and intricacies of beauty rituals. Chong analyzed popular hair and makeup tutorials on YouTube from the perspective of Swarts’ Best Practices (Chong, 2018; Swarts, 2012). TikTok is even used for highly technical instruction, such as instruction related to beginner programmers. Interestingly, it was found that many programming communities on TikTok did not subscribe to more normative practices, such as memetic dances set to popular music (Garcia et al., 2022).
Short-form instructional content, particularly on TikTok, is exploding in popularity. The existing frameworks for assessing instructional rhetoric and its associated production values have not yet been extended to short-form videos. Our study aims to bring established frameworks to the modern short-form video to understand what aspects remain salient in short-form video and what new dimensions must be developed to support this newer but immensely popular type of media format.
Methods
We collected a set of TikTok videos that met our criteria for instructional short-form video and created a separate set of dimensions that represented aspects that are relevant to short-form. Then, we compared and contrasted the long-form and short-form dimensions, noting new dimensions and those that were no longer relevant. We then derived a set of dimensions that can be used to assess the production qualities and rhetoric of short-form video, particularly on TikTok.
Our framework for assessing short-form videos integrates our knowledge from two sources. First, we examined the literature and gathered the various pieces of frameworks, characterizations, dimensions, and points of assessment that were put forth by previous scholarship. We then examined a body of TikTok content on how-to and instructional topics, and analyzed them from the perspective of how they were utilizing TikTok features to perform the instruction. Noting the differences between long-format and short-format videos, as well as the variations in platform affordances, we derived a set of dimensions for the assessment of short-form instructional content.
Gathering a Corpus of Instructional TikToks
For this study, we collected the first 50 videos for a set of queried hashtags related to how-to, DIY, instructional, and other explanatory content on TikTok. The extensive number of search results for each hashtag made it difficult to precisely determine the true size of the hashtags using the tools available to us. Given our objective to qualitatively code videos based on their content, we decided that coding 50 videos per hashtag would be a reasonable quantity and provide a realistic, representative sample for each hashtag’s dataset. The search was conducted using the TikTok universal search feature, currently found by tapping the magnifying glass in the top right corner. Each video from this set of 50 was screened through a set of inclusion criteria: communication in the English language, a valid URL that resolved to a publicly available, non-deleted TikTok, and adherence to a reasonable definition of “how-to,” “DIY,” “explanatory” or “instructional” content. We then coded each video for its thematic content and noted other predetermined dimensions of analysis, such as subject matter themes and production complexity. We used audio-visual content analysis to systematically analyze the collected videos (Bell, 2004).
Choosing hashtags
We chose to investigate hashtags that could reasonably accompany how-to, instructional, or explanatory-type TikToks. The authors initially selected the hashtags #explained, #explainit, and #howto to begin the video search, as previous experience with instructional material and the literature suggested these hashtags would be strongly associated with our subject of study.
We defined “how-to,” “instructional,” or “explanatory” TikToks as “content that explains an idea, concept, or process.” These types of videos can also add context to an idea, concept, or process in order to help someone else understand.
We ran preliminary searches for each of these hashtags to ensure that the subject matter, broadly speaking, aligned with our definition of “how-to,” “instructional,” or “explanatory” content. Table 2 shows all hashtags that were considered for our final dataset. Most of the content appeared to be explaining concepts, providing examples, showing how to build circuitry, explaining a do-it-yourself (DIY) process, or containing other strong, how-to features. However, not all hashtags were fit for our dataset. The #howto hashtag, for which we expected more instructional-type content, contained a large amount of sexually explicit, non-instructional content. Rather than hunt for TikToks within this hashtag that fit our definition, and risk creating a biased dataset, we chose to remove this hashtag entirely from our study.
Data collection
After conducting a cursory search through each hashtag to ensure that they aligned with how-to content, we turned our attention to systematically collecting TikTok videos. TikTok allows users to search for any string of characters, including hashtags, via the global search feature. Using the logged-in TikTok account of the lead author, we searched for videos that contained hashtags related to how-to, instructional, tutorial, or DIY subject matter. The global search feature returns a list of content, which is divided into several categories, including “top,” “users,” “videos,” “sounds,” “LIVE,” and “hashtags.” We chose to sample videos categorized by the platform as “top” for our dataset. The ranking of the videos appeared to be determined by the number of likes each video garnered. We recorded the URLs for the first 50 videos we were served for this search under each hashtag. This resulted in an initial data set of 400 TikToks related to instructional topics.
Dataset: Inclusion and exclusion criteria
Each TikTok was screened by the lead author as part of a quality assurance check to ensure the video URL was working and resolved to the TikTok domain. We also checked to see if the video was still available and accessible on the platform. Creators have the power to delete videos or make them private at any point; it was important that these links remained accessible to the coding team. We then randomly sampled videos from across all hashtags to scan for any unforeseen issues with the content.
In our initial scan, we found a significant number of TikToks were not conducted primarily in English; because the authors’ primary language is English, we chose to exclude these from our final data set. Videos that contained non-English captions or additional hashtags were permitted to remain in the data set if they could be reasonably understood by an English-speaking audience.
Each TikTok was then scanned for its content. We removed videos that did not do the work of explaining, demonstrating, or doing. Specifically, we removed any video that asked for an explanation of any phenomenon. For example, if a video cataloged under the #explain hashtag showed a video of lights in a dark sky, implying that the video is about unidentified flying objects (UFOs) along with the caption or voiceover “explain it,” then this would be considered asking for an explanation. Another example of content that did not meet our criteria for explaining was any video that was tagged with #explainit that was more joking in nature. A video of a woman explaining that “two plus two equals six” and providing a haphazard, non-serious explanation of why this calculation made sense was not included in the dataset. While we could not directly ask the creator about intent with these TikToks, we can reasonably exclude data that appeared to be nonsensical, insincere, or otherwise asking for engagement.
Videos that were set for inclusion were much closer to the conventions of instructional content familiar to our field, exhibiting a wide range of “traditional” features. For example, a video of someone dancing in slow motion to a popular TikTok song, along with a real-time overlay of the TikTok recording user interface, was included. This video was likely meant to help other TikTok users perform and record the dance on their own, using the “tricks” and “hacks” that were perhaps less intuitive to some users. Our original dataset contained 450 TikToks. After eliminating non-English videos, those that did not attempt to explain any subject matter, or were significantly outside our definition of how-to type content, we were left with 199 TikToks that satisfied our requirements. A detailed description of our inclusions and exclusions can be found in Figure 1.
Analysis
Developing a Set of Dimensions
We sampled our videos at random to begin our analysis, noting what dimensions TikTok creators were using to get their ideas across. From our initial analysis, we developed a set of dimensions. We retrieved descriptive information from the TikToks, such as their length in minutes and seconds, subject matter, type of explanation, subject complexity, production complexity, audio characteristics, and overlay characteristics. A detailed description of these content areas is provided in Table 3.
Our final codebook was developed iteratively as we cycled through the dataset, eventually coding and describing all 199 TikToks in the dataset. The primary author coded all videos by hand on each of the content areas listed above in a single coding session per area. The dimensions described above were then edited and modified to add depth and detail. After all the videos were coded by the primary author, two independent coders reviewed all codes to ensure agreement in the code application. All coders met to discuss any differences in coding. Coders resolved disagreements, raised questions, and resolved any differences in codes through discussion and, ultimately, majority rule. Any changes to existing codes or applications of codes were implemented. All coders again met to discuss any questions, concerns, or changes. Based on this iteration of the codebook, along with previous definitions of explainer videos, no TikToks were deleted from the dataset at this point. Videos were viewed multiple times at multiple points throughout the analysis stage to ensure that there was consistency in the coding and interpretation of the videos.
We used audio-visual content analysis to approach the 199 TikToks in our dataset (Bell, 2004). Because of the complexity of audio-visual media, we focused our analysis on what affordances and narrative methods were being used by creators to get the main points across. Focusing on these two aspects allowed us to cover the groundwork laid by authors of long-format video assessments. Having completed our analysis of dimensions from long-format videos, we noted which dimensions these short-form videos borrowed from long-form content. We also paid special attention to dimensions that could be unique to TikTok in particular. The analysis phase was both comparative and generative; while we kept previous dimensions in mind while performing our analysis, we were also mindful of the differences between long-format and short-format video; namely, video length and the use of text overlays, which are normative to TikTok. We approached each video at random and began describing the affordances and ways of going about telling the instructional story, developing dimensions that were appropriate along the way. After reviewing a small subset of videos, dimensions such as audio meme, and the use of text overlays began to reveal themselves. These dimensions were written into a spreadsheet and were changed iteratively as we reviewed a larger number of videos, in the tradition of grounded theory (Corbin & Strauss, 2008).
Findings
We analyzed each TikTok video in our dataset of 199 TikToks that explained or otherwise instructed on any topic. Our findings below are presented thematically by dimension.
Dimension: Instruction Type
We found videos that explained concepts or processes in a range of styles, including explaining through character acting, explaining through interaction (with the viewer), explaining using lists, explaining with demonstration, explaining using outside video, and watching someone tell or explain a story. This dimension harkens back to Farkas’ (1999) work, which put forth the dimension of rhetorical work in instructional material. Rhetorical work asks us to describe how instruction is being conveyed. We found evidence of all three types of rhetorical work, doing, demonstrating, and explaining. With the large number of TikToks available in our dataset, and with the wealth of affordances available to TikTok and the video format, we were able to identify more nuanced types of explanations. Table 4 shows each instruction type found and their percent representation within our dataset.
For example, videos where one or more subject(s) were acting out scenarios in a movie-like manner, with the camera choosing a slightly different angle for each speaker, were characterized as explaining through character acting. Figure 2 shows screenshots from a TikTok in which a creator is explaining how French learned in American schools is often quite different from the French used conversationally in France. This example shows demonstration in action by assisting the viewer through a concept with concrete artifacts, both on-screen and within the video itself through character acting.
One video instructed on concepts through interaction between the viewer and the creator. For example, one TikTok relied on the viewer to interact with the video in order to complete the “feedback loop” of instruction. In Figure 3, a TikTok creator uses a set of prompts for the viewer to respond to for the purpose of practicing their English skills. The creator outlines a speaking role for themselves in the red background text and the viewer in the green background text. A viewer can verbally respond to the green background text to complete the “loop” of instruction. This technique is reminiscent of the types of instruction used in classrooms and language-learning apps such as Duolingo.
Explaining through lists was a distinctive type of video we found in our dataset, demonstrated in Figure 4. Videos in this category used sequences of text, pictures, or another video, occasionally numbered or set to bullet points, to explain a concept. These types of videos were often set to the beat of an audio sound, such as a song by a popular artist or an instrumental track.
A small portion of videos utilized outside video, or video that was found outside the TikTok platform, to explain concepts. We used deductive reasoning to determine if the added video was filmed outside the platform. In cases like these, a video was often featured as a small overlay over the creator’s video or used as the background for the video. Creators acknowledged the presence of these overlays or background videos in obvious ways, sometimes pointing to where the overlay or a subject of interest is on the screen, so viewers understand what to direct their attention toward.
We found a small subset of videos where we watched creators tell us a story. This style of video was quite distinctive as it was often void of many text overlays or supplemental audio. Many creators simply pointed their mobile device camera toward themselves and began explaining a concept primarily through narration.
By far, the largest category of instruction type we found were videos that simply demonstrated a concept. Much like the definition provided by Farkas (1999) and Morain and Swarts (2012), these types of videos can be described as simply illustrating a step in a process and not accompanied by an explanation. In Figure 5, a creator demonstrates their process to create a decorative display of a “cyborg beetle” inside a snow globe. In this video, there is no narration or text guiding the viewer to perform any particular steps. The audio in the background is an instrumental song that was popular on the TikTok platform at the time the video was created.
Dimension: Level of Detail
We developed a dimension related to the level of detail present in each instructional TikTok. We found that short-format videos could be instructional without a large amount of detail or density of information in the content of the video. Whereas previous authors noted that exceptional instructional content was step-by-step, the instructional content on TikTok ranged from detailed (showing distinct steps, providing text lists, etc.) to notional (little narration, perhaps a series of videos over the course of a long process), or having little detail. Videos were coded as being either notional or step-by-step. This dimension embraces the artistic norms of TikTok and creators often express the level of detail in creative ways. One way to assemble a how-to video is to provide a detailed narrative explanation of a task while providing less informative visual detail, allowing the viewer to focus on the narration. An example of this in our dataset included the restoration of a marble table. The visual information was a series of shorter videos showing the creator having difficulty removing the table’s epoxy surface with a spatula. The narration, on the other hand, added detailed explanation of what was difficult and the reasons why the creator decided to try a different strategy with the epoxy removal. Still, another type of video, a more notional one, might provide exceptional visual detail with little to no narration or subtitled text. An example of this style included a video in which a creator reupholsters an old chair. There is a narration at the beginning of the video that explains “Got this old sofa for $20. Loved the shape.” The remainder of the video are quick snapshots of the sofa pre-augmentation, materials that went into the sofa such as blankets as filling, scissors to cut the blanket to size, snapshots of a staple gun and threading, spray paint to coat the wooden legs, and, lastly, a photo of the final result. There is, overall, less detail provided. This style can feel quite “dreamlike” and gives the illusion of an easy DIY process.
Dimension: Subject Complexity
We noted the subject complexity in our analysis. In this dimension, we asked ourselves how easy or difficult the content was to understand for a non-expert in the topic. Low complexity was defined as content or subject matter that was easy for almost everyone to understand. Little to no outside knowledge is needed to understand the video. High complexity was defined as content or subject matter that would be difficult for the average person to understand, especially without some foundational background knowledge of the topic. For example, one video in our dataset was on the topic of explaining stock market “patterns.” By pointing at visual patterns on a document and then referring back to another stock chart, it taught the viewer that certain graph shapes on a chart meant that it was time to either buy or sell. There was no accompanying narration and little use of overlay text. A person without knowledge of how the stock market works would likely be lost. We deemed this video as being of highly complex subject matter. On the other end of the spectrum, we also saw low complexity instruction, though this was less common. One example of a low complexity video was one involving the proper way to shave one’s legs. While the video met our definition of an instructional video, it was also quite practical and was meant for a general audience. This video demonstrated that, to get a closer shave, one should “utilize the swivel action” of the razor instead of picking up the razor off the skin. Nearly everyone would be able to understand how to use this “trick” with almost no background knowledge necessary. Table 5 shows subject complexity and its percent representation within our dataset.
Dimension: Production Complexity
Production complexity was assessed for each video on a scale of low to high. In low-complexity productions, a camera or mobile device was usually pointed directly at the video subject. Typically, there were a low number of “cuts” in the videos, or areas where the recording was stopped and restarted, or there was clear evidence of editing within the TikTok app. In high-complexity videos, narratives become more intricate. Cameras moved in movie-like ways or there were multiple cameras in the production capturing different angles of the subject. These videos also incorporated extra elements found in other content areas, such as outside video or multiple types of audio inputs, such as sound effects and music. There may have also been evidence of editing outside of the TikTok platform, with transitions that would not have been possible from within TikTok itself.
Dimension: Overlay Content
On TikTok, text overlays and closed captioning features are important. We noted any overlay content used in videos. As a first step in the analysis, we determined if there was any overlay content at all. If so, we categorized the type of content. Some videos used no overlays, such as the example in Figure 6, which successfully conveys a cake baking process We arrived at three core overlay types: text, image, and user interface (UI). UI was used if the video contained any content that showed the TikTok user interface. For example, if a creator was explaining how to do a dance and showed the TikTok UI as part of the explanation itself, this was noted. Image overlays included the use of emojis. Text codes included more straightforward text-over-video treatments as well as special text features, such as closed captioning.
Dimension: Overlay Function
Along with overlay content, we also noted overlay functionality. We described overlay functionality as the ways in which the overlays were helping the viewer to understand the content. We arrived at several codes, including advertisement or attribution text, captions, illustration, prompt or setup, and title text. Advertisement or attribution text was simply text that gave another creator credit or advertised the creator’s other social media channels. The illustrative text was text that provided additional context to the voiceover or visuals. Generally, this was text that was thought to carry more weight and do more work than title text. Prompt or setup text was noted as being distinct from other types; this was text that only appeared at the beginning of the video and was meant to orient the viewer to the subject matter. In contrast, title text was understood to be orientation text that occurred throughout the video, as a way to guide the viewer along a journey. Lastly, captions were noted as being distinct; this code was interpreted as text that followed the audio narration quite closely.
Dimension: Audio Content
We noted any audio content in each video and found codes for voiceovers, outside content, songs, soundscapes, and ambient sound. Voiceovers included audio content such as voice narration by the creator. We interpreted any sounds that were not created or selected via the TikTok app as outside (sound) content. For example, a sound effect created outside TikTok and added to a video would be coded as outside sound content. Songs can be selected from within the app, and we noted where it was apparent these songs could be linked back to the TikTok music library. Soundscapes are similar to songs in that they can also be searched for and selected from within the TikTok app; however, these soundscapes do not contain lyrics and, for the most part, would not be classified as songs one would hear on the radio. These soundscapes can be thought of almost as “bed music” or music that can exist underneath voiceover-type content. Lastly, we also coded for ambient sound, or sound that is not specific to any person or song, but simply the sound of the activities going on in the video. For example, a TikTok showing a person sanding a piece of wood with no other audio would be considered ambient sound.
Dimension: Tempo
A primary concern in short-format video is the very nature of its format: there is limited time to instruct. In its current state, TikTok allows some creators to post videos up to 10 minutes in length, with 15-second and 60-second video options as suggestions in the user interface. While some may find short-format to be a limitation, others may find a creative challenge. How does one instruct when there is limited time to do so? We found that tempo was important to instructional TikToks but in a different sense than ten Hove and van der Meij (2015) outlined in their work. In long-format frameworks, tempo can best be described as the real-time pace of the video in terms of how much information (e.g., narrative speed) is delivered per a certain time frame. In TikTok, however, tempo is more of a stylistic choice and interacts with level of detail: how much information is the creator fitting into a video and what is the balance between visual and audible information. We found that videos could have fast, intermediate, and slow tempos. One video with a high tempo included a DIY repurposing of an Ikea side table. The creator walks us through its transformation from a Scandinavian-inspired, minimal table to a wicker table, fit for a seaside bungalow. However, the tempo is quite fast. Different snapshots of the process are shown quickly throughout the video, although there is a good amount of detail in each. The narration seems rushed and slightly sped up with few natural pauses. We are left feeling informed, yet in need of a pause to take a breath. Other videos can feel rushed in different ways. We encountered a few examples of video that had been sped up to remain under the one-minute mark. Some videos have the opposite effect, potentially moving at too slow of a pace. A hallmark of this type of video is one that contains unusually long narration pauses throughout, which adds to the feeling that the video could have been shorter. A closer look at the interaction between level of detail and tempo in short-form video may be warranted.
Dimensions for Assessing Short-Form Instructional Video
Our goal was to develop a framework for assessing instructional content for short-format videos, particularly on TikTok. Keeping in mind the frameworks and dimensions that have been developed for long-format media, such as tutorials and instructional videos for YouTube, we assessed TikToks for the unique dimensions and affordances that supported instruction in small durations. In this section, we outline our framework for assessing short-format video content, which marries the relevant dimensions of long-format frameworks with our new observations from the TikTok dataset. Table 6 provides a summary of our framework
An Assessment Rubric for Short-Form Video
We combined the dimensions derived from our study of instructional TikToks with the previous literature on frameworks for assessing instructional content.
Generally, dimensions regarding visuals and audio were directly translatable to a framework for short-form content. We ported over dimensions such as verbal and sound and visuals from ten Hove and van de Meij (2015), and sounds, text, and moving/still images (modes) from Morain and Swarts (2012). Audio and visual content were highly nuanced on the TikTok platform. For that reason, aspects of these two elements are both broken out separately (audio meme/content & overlay function/overlay content) as well as integrated within other dimensions, such as instruction type. They are inseparable. Pflugfelder’s (2013) language, or how language directs viewers to perform specific tasks, is present in narration and through the use of text overlays. Ploetzner and Lowe (2012) articulate the idea of scaffolding, visual or auditory cues to guide the viewer, which underlies the very concept of an instructional video, no matter the length of the format. The two dimensions, audio and visual, are inseparable.
Our dimension of production complexity had roots in previous work. Carliner (2000) articulated viewability, or the ability of the video to be tolerable. Pflugfelder (2013) outlined production as a dimension, which suggested that a good instructional video was easy to watch through its strategic zooms and pans as well as easy to hear through audio editing. All of these are elements of our dimension of production complexity, which takes previous concepts a step further. One can now assume video will be easy to watch and hear, due to the affordances available for editing on TikTok and other similar applications as well as with advances in mobile device technology; viewability and production are givens and, without these basic components, would not be good candidates for the platform. Complexity arises when numerous affordances and features are taken advantage of, which is what production complexity describes: When the basic features are met, what other elements are incorporated that help a user understand the instruction? In more complex short-form videos, we see more intricate narratives, more content ported in from other sources, multiple audio sources, and generally more assistive elements.
Subject complexity is relevant for the TikTok platform as it attracts a wide range of topics and audiences. While we do not make judgments about the subject complexity on its own, it is important to note how a high subject complexity might interact with other dimensions in the rubric. For example, what would a low subject complexity and high production complexity instructional TikTok look like? Would this type of instruction be effective or engaging on a platform such as TikTok?
Much of the rhetorical work from Farkas, Morain, and Swarts (2012), and Ploetzner and Lowe (2012) remained in the rubric in spirit through instruction type and level of detail. These dimensions aim to capture the how’s and rhetorical modes of instructional work. They also point to a very interesting concept on a platform like TikTok—how does one convey information in a short period of time? Creators have devised clever ways to do this. On one hand, information can be densely populated in a short-form video through narration over text overlays, which is still relatively common for TikTok. On the other hand, a video can be more notional, walking someone through a vague but still perhaps reproducible DIY project. Related to the idea of rhetorical work in the context of short-form video is the dimension of tempo. Both Carliner (2000) and ten Hove and van der Meij (2015) describe timing and tempo as dimensions to assess instructional content. With TikTok and other short-form platforms, timing is of primary concern: There is less time to get an idea across. For this reason, we felt that tempo should be a dimension of assessment in short-form content.
Lastly, entertainment value, while not formally present in our framework, is essential to the culture of content on the TikTok platform. Both Carliner (2000) and Pflugfelder (2013) maintained that engagement was critical to creating good instructional content. While we make no claims about the goodness of the content surveyed, the entertainment value is certainly a primary concern for creators on a platform that encourages vibrant content.
Shifting relevancy of dimensions?
In building our short-form dimensional rubric, we wanted to emphasize the elements and dimensions that remained relevant from previous work on long-form videos. However, there were a number of dimensions that were noticeably absent from our short-form rubric.
For example, video resolution was considered an important assessment criterion in ten Hove and van der Meij’s work as late as 2015. With ubiquitous mobile devices that can capture video in 8K at 24fps in 2023 (Cyrus, 2023), the resolution is no longer a primary concern for content created ad hoc via a mobile device. Another dimension that did not appear in the final rubric was the notion of error representation. Particularly in short formats, error representation or recovery may not “make the cut” of content that makes it into the final video. For some content, errors could prove to be humorous, provide entertainment value, and even bolster engagement and sales (Barta et al., 2023); however, we did not find any examples of this in our dataset. One example of this type of sub-genre is exemplified by the TikTok user @sophiena_official. The creator often features videos that humorously struggle with assembling a cooked meal, yet the underlying instruction is coherent enough to pass the instructions on to the viewer. The notion of error representation goes hand-in-hand with that of self-efficacy and accuracy, which also did not find their way into the short-form rubric. While there are many professionals on the TikTok platform who show and even instruct their craft, self-efficacy was not a factor found in the videos we reviewed. Accuracy also cannot be known in many cases, particularly for content that highlights DIY or craft practices. Confidence and completeness, related to self-efficacy and accuracy, are also not knowable through our dataset.
Some dimensions point toward highly specific types of content that were not well-represented in our dataset. For example, guided exploration, which is a dimension that alludes to having a product to explore, was not captured in our dataset. One area where this dimension might be relevant is in so-called “unboxing” videos where users open or unbox a product and explore its features. However, this type of content does not qualify as doing the work of explaining or instructing on a concept. We found the same to be true for action, from Pflugfelder’s (2013) framework. The idea of action was about shifting the focus of the viewer onto the things a user could do with a product. Again, this was not the orientation of many of the TikToks that were captured in this dataset. In fact, the dataset represented a range that was wider reaching. For example, videos under the #DIY hashtag would not focus so much on what one could do with a newly built piece of furniture for your living room, but rather the process it took to make the piece. Task orientation was similar in terms of applicability to the dataset. While few TikToks focused more on tasks rather than features, the lack of formal step-by-step instruction within our dataset made task orientation less relevant to the framework.
Lastly, there are long-format dimensions that are relatively standard practice on TikTok, similar platforms, or with video in general. For example, user control over media is an expectation that many users would have of most video-based platforms. On TikTok, it is not only possible to pause media to catch up to the real-life delays of, for example, assembling a salad, but a user can opt to download the video if the creator allows it. The accessibility dimension from Carliner (2000), focuses on the parts of the subject that are pertinent to the instruction is key, is more or less a norm on the TikTok platform. There is limited time and screen space to attract attention elsewhere. Related to the concern of what instruction is in-focus in the video is correspondence, or how well the video matched up to directions, which can also be considered a standard practice on TikTok. A video without synced communication would be confusing to follow. Similarly, the dimension of configuration, which Ploetzner and Lowe (2012) describe as the ways in which instructional content can be made available to a user, was found to be not applicable as TikTok only allows content to be delivered in one way: via short, pausable videos.
Discussion
In our analysis of long-format dimensions of assessment for instructional video and short-format instructional TikToks, we found that there were a surprising amount of similarities between long and short formats. This is evidenced by the number of dimensions that were able to translate from previous frameworks. In this section, we will discuss some of the challenges, successes, and limitations of developing the short-form framework.
TikTok Norms, Notional Material, and the Definition of Instruction
It is worth noting that even in previous literature to outline general frameworks for assessing long-form instructional content, the frameworks themselves were not always comparable or applicable to a wide set of media. For example, Carliner’s (2000) model of information design, while applicable to YouTube tutorials as well as TikTok content, was designed during a time of rapid change in information design and functioned as a guideline for workplace communication. Morain and Swarts’ (2012) work is focused on YouTube tutorials, where there is a strict definition of tutorial. Previous literature points toward long, formal, step-by-step instructional content being a sort of north star—something to emulate. On TikTok, and with a short-form framework for assessment, there can exist a broader definition of instruction. Instruction can be step-by-step, formal, or informal, use audio cues set to a popular song, or rely heavily on text captions. It can also be more notional; it can give the viewer a vague idea of a long process or show a series of complex processes in quick succession. What does and does not resonate with the TikTok audience regarding instruction has a lot to do with the platform culture, which can best be described as informal.
Video Intent and Hashtags
Previous frameworks were quite clear regarding the intent of the media at the center of the investigation. With our dataset, the intent was less clear. For this study, we did not ask creators directly if they meant to create instructional content, or for whom the content was meant. Our next best tool for understanding intent is through the associated hashtags. We searched for hashtags that could reasonably result in instructional content, such as #DIY and #LearnOnTikTok. While many creators use these specific hashtags to make their content more findable, not all TikToks we collected fit the interpretation of the hashtags. A #LearnOnTikTok search could house videos entirely unrelated to the hashtag. This could be due to creators sometimes attaching irrelevant content to popular or trending hashtags. On the contrary, many creators who do create instructional content might not think of their content as being instructional at all. There might still be a portion of creators who mean to instruct but do not use the hashtags that one would expect or instruct hoping for that action to gain a following. Instructional content might be more ubiquitous than we think. One notable example of this type of content would be instruction related to physical products, specifically for the intention of generating demand and making sales. A creator might choose to instruct their audience on how to create abstract art using plaster on canvas but also sell the examples that are created. There is an opportunity to further explore the intersection of demonstration and TikTok video or TikTok Live for the purpose of marketing and sales, as some researchers have begun to do (Orlando & Fachira, 2023; Yang & Lee, 2022).
Part 1 or Part 2?
As TikTok now has a time limit of 10 minutes per short-form video, creators are running into the problem of how to tell a longer story on the platform while still maintaining the attention of their audience. One way to combat viewer fatigue and gain additional views is to break up an explanation or story into multiple parts. For example, a creator can title their first video “Part 1 of 2: Baking a Cake” and the second video “Part 2 of 2: Baking a Cake.” From a different perspective, breaking stories into multiple parts makes sense for viewership and follower growth. Creating multiple videos for a single story increases the amount of video real estate that can potentially find its way to someone’s “For You” page. In our 199 TikToks accepted into our final dataset, we observed a single video that was told in several parts. The video was a “Part 2” of an unknown number of parts. On its own, the video is still able to be described by our rubric; however, the choppiness of the story, quite literally not having a beginning or end, can have implications for the dimensions, tempo, level of detail, and subject complexity. For these dimensions, data might simply be incomplete or unknowable. This leads to the next logical question: Are TikToks “in parts” simply long-form content broken up for an audience that prefers much shorter, get-to-the-point videos? The fuzziness of the rubric, when applied to these videos, seems to suggest that multi-part TikToks are more like YouTube instructional content. Ideas are not shrunken down into bite-sized clips but are split to force-fit a long-form style of content to a platform where whole ideas and processes can be expressed in 60 seconds. We suggest that instructional TikToks which occur in parts might be better assessed through the lens of long-form dimensions and rubrics.
Metrics as a Way to Assess?
It is tempting to look at content in a social computing context and assess its worth using visible metrics such as the number of likes and comments. TikTok is algorithmically complex. Videos “go viral” or gain interest rapidly for many different reasons that are often unknown to the creator, much less the viewer. “Good” instructional videos that are tagged appropriately and are otherwise engaging might not see the viewership that corresponds with how well something is explained. We cannot know all the reasons some videos flop and others succeed. Social metrics such as likes, comments, bookmarks, and shares may not be a good way to assess content in this system.
Limitations
TikTok is not the only platform with short-form videos. Platforms such as Facebook, Instagram, Snapchat, and YouTube have short-form content, though the affordances available to the creator may be quite different. Our dataset is derived from TikTok content and, while many app features may be similar or even the same across multiple platforms, we cannot reasonably consider short-format video, or the assessment of it, to be the same across all platforms. Additionally, each platform has different norms and expectations for short-form videos. For example, TikTok culture has a penchant for memes (Lorenz, 2020). Many TikToks use a memetic structure to stay on top of trends and solicit engagement. There may be social or algorithmic value in utilizing a trending sound or video filter. We should consider that short-form videos may be limiting for certain types of instructional content. While some concepts can be explained completely in under 10 minutes, many more concepts cannot. For concepts where short-form video is prohibitive, we will likely not see representation for that concept on TikTok. As Pflugfelder outlined, the entertainment value is certainly a factor in keeping users engaged with instruction (Pflugfelder, 2013). Instructional videos that were perhaps far more technical or were otherwise less engaging for a TikTok audience, which greatly values entertainment, may be less likely to appear in the results of our initial hashtag searches. Lastly, we were constrained by not investigating the comments associated with each video. Comments may have allowed us to understand more about the intent of the video and whether or not any effort was being made to provide clarification or further instruction by the creator through the comment feature, similar to instructional discourse on YouTube.
Conclusion
In this article, we looked back at frameworks and dimensions that have helped technical communication researchers and educators assess the content, conceptualization, and potential effectiveness of long-format instructional videos. We carried over previous work on assessing long-format instructional videos to a relatively new format, short-format TikTok videos. To move toward a framework for assessing short-format videos, we chose TikTok hashtags and the associated videos that could reasonably describe instructional content and coded them according to their short-format dimensions. We then compared these new dimensions against those that have previously been applied to YouTube and other long-format tutorials. We found that many dimensions from YouTube carry over to short-form video, but there are affordances, social norms, and creative norms on TikTok that lend themselves well to a more notional and less step-by-step instructional design. We discuss the notional aspect of short-form instructional videos and the implications of the exclusion of our selected hashtags within video descriptions. Our findings show that short-form instructional content on TikTok in particular is worthy of further investigation.
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About the Authors
Julie A. Vera is a third-year PhD student in Human Centered Design and Engineering at the University of Washington in Seattle. She received a master’s degree in sociology from Brandeis University. Her research interests include short-format video communication, online community dynamics, and sensemaking in social computing environments. Prior to joining HCDE, she practiced user experience research and design in the field of human resources and employment. She can be reached at jvera@uw.edu.
Dr. David W. McDonald is a professor in the department of Human Centered Design & Engineering (HCDE) at the University of Washington. Dr. McDonald›s research focuses on the design and implementation of systems that support large-scale collaboration. He has published research on collaboration in distributed contributor systems, collaborative authoring, recommendation systems, online communities for health & wellness, and ubiquitous sensing for behavior change. His research interests span Social Computing, Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI). Dr. McDonald earned his PhD in Information and Computer Science at the University of California, Irvine. At UC Irvine, he was part of the Computing, Organizations, Policy and Society (CORPS) group. He worked at FX Palo Alto Laboratory in the Personal and Mobile technology group and at AT&T Labs in the Human Computer Interaction group. Dr. McDonald was faculty in the UW iSchool from 2002 to 2014. Dr. McDonald served as a Program Officer for the Human Centered Computing (HCC), Network Science and Engineering (NetSE), and Social Computational Systems (SoCS) programs at the National Science Foundation (NSF) in 2008–2010. Dr. McDonald was Department Chair for HCDE from 2014–2019. He can be reached at dwmc@uw.edu.
Dr. Mark Zachry directs the Communicative Practices in Virtual Workspaces Lab in the department of Human Centered Design & Engineering (HCDE) at the University of Washington. He is a professor in that department and adjunct professor in the department of English. In HCDE he serves as Co-Director of the master’s program and the User Centered Design Certificate. He is Director of the Individual Interdisciplinary PhD program for the UW Graduate School. His recent research investigations include studies of social behavior in online platforms, collaborative group interactions using augmented reality, and uses of generative AI in the UX practitioner community. He is an associate fellow in the Society for Technical Communication (STC) and Fellow in the Association of Teachers of Technical Writing (ATTW). He is a former editor-in-chief of Technical Communication Quarterly. He holds a PhD in Rhetoric and Professional Communication from Iowa State University. He can be reached at zachry@uw.edu.
