Educational videos have been playing a major and growing role in learning processes of pupils, especially since the global COVID-19 pandemic changed school-based learning to a certain degree (e.g. mpfs 2018; mpfs 2020). Although there is already some research from the educational sciences on educational videos (e.g. regarding their purpose or quality criteria), these studies usually pay little attention to the channels themselves or the people responsible for them (Dorgerloh 2020: 138–139). With this paper we aim to fill in these gaps and present an analytical instrument to help educators assess these aspects more closely by using a focussed yet explorative approach and following these research questions: Who is responsible for educational videos that are used in (German) school contexts? To what extent do the underlying educational video channels differ from each other?

To answer these questions, we focussed on channels from the (currently) main online video platform: YouTube (mpfs 2018: 50). There, we concentrated on German-language educational videos. Both restrictions facilitated our multi-step process in which we first developed a theory-based analytical instrument, the AEY (Analysis Grid for Educational Video Channels on YouTube), that allows for a criteria-based, comparative analysis of educational channels by following similar developments in Siegel/Heiland (2019: 55–56), Fey (2015: 67–73) and Welbourne/Grant (2016: 709–711). We selected 11 such channels using a poll-based and criteria-based sampling method in which we included educators from different areas of the German educational landscape. The sample was then rated with regard to the AEY’s 31 analysis questions which are grouped in the following 5 categories:

1. Channel performance: 6 questions, e.g. How many videos are uploaded on this channel?
2. Channel structure and didactic contextualisation: 12 questions, e.g. Which school subjects are covered in the videos of this channel?
3. Channel responsibility: 4 questions, e.g. Is there information on the persons responsible for the channel?
4. Economic model: 4 questions, e.g. Is there advertising on the channel or its videos? If so, in what form?
5. Quality check: 5 questions, e.g. Do those responsible for the channel indicate that they check the quality of their content?

Results reveal substantial differences between the examined channels with regard to their performance, monetisation, transparency, staff, target groups and quality management. Advertisement, for example, is used to a varying degree and ranges from affiliate links in the video descriptions to in-video adverts but also includes additional fee-based video or learning contents. Some advertising is directed to children and teenagers and might be categorised as a form of anxiety-provoking manipulation.

Conclusively, both the findings of our study as well as its limitations and strengths are discussed and suggestions for future research are put forward. For instance, a larger number of educational video channels could be investigated as well as the above mentioned restrictions regarding video platform and language could be lifted to increase the generalisability of the findings and provide additional insights.

A multi-step process was applied to obtain a heterogeneous sample of channels that takes into account the plurality of providers. In a first step and in the absence of an overview of the market for educational video channels there was a necessity for (a) identifying channels on YouTube which (b) provide educational videos for a predominantly German-speaking target group and (c) whose content is primarily relevant for pupils and teachers. In the absence of an instrument that allows a comparative analysis of YouTube educational video channels, a new instrument was developed based on the Authors’ (2019) questionnaire for the analysis of online platforms (2019: 55–56) and the Augsburg analysis and evaluation grid for analog and digital educational media (AAER, Fey 2015: 67–73) as well as the Welbourne/Grant procedure (2016: 709–711). The newly developed Analysis Grid for Educational Video Channels on YouTube (AEY) was then used both for content analysis and cross-channel as well as comparative evaluation. It is available under a CC-BY-SA 4.0 license. In a second step, criteria were used to select YouTube educational video channels which (a) are operated by different providers (e.g., private individuals, companies), (b) serve different subjects (e.g., politics, mathematics) and (c) differ in their channel performance (e.g., views, subscribers). The data collection was carried out on 13/08/2020. For this purpose, the data was collected non-reactively on the YouTube educational video channels (Lütters 2004: 95–96). Following Fey (2015: 61–62), the data was analysed descriptively in terms of content and evaluated according to defined criteria. In contrast to Fey (2015), for the present study mainly low-inferential questions were used in the different categories and thus “the degree of conclusion by the observer is kept as low as possible” (Lotz/Gabriel/ Lipowsky 2013: 359). Accordingly, it is possible that teachers use the grid in the school context even without specific training. Only the evaluations for the 5 categories are more highly inferential, but still based on the decisions previously made with low inference. The data were collected from at least 2 persons and documented separately. To eliminate discrepancies in the data collected, these were discussed in the group of authors and adjusted after a consensual decision (Bortz/Döring 2002: 319).

The analysed educational video channels and their content are characterised by a great heterogeneity. On the one hand, this diversified media content complies with a diverse pupil body and their learning needs; on the other hand, this can present challenges for users. Didactic commentaries on the videos could help users to better classify the content, but such comments are missing. In addition to the educational videos on YouTube, some channels provide other teaching and/or learning materials such as worksheets, exercises and apps. While this is most welcome, paywalls often limit users from accessing them freely. Future research should focus on these materials. The findings of the presented educational video channel analysis provide several more starting points for follow-up studies in international contexts and encourage reflection on what constitutes the trustworthiness and quality of educational video channels. The Analysis Grid for Educational Video Channels on YouTube (AEY), which was developed within the framework of this study, can be used for the criteria-based selection, analysis and evaluation of YouTube educational video channels and can serve as a decision-making aid for (prospective and practising) teachers. Furthermore, it can be used as an instrument of reflection and deepened sensitisation in university teacher education. In addition to dealing with the channels, teachers should always deal with the individual educational video and do so from a critical, pedagogical, scientific and/or didactic perspective. To do this, (future) teachers need comprehensive media education and professional educational media skills, which they can (further) develop by teaching and learning with and through educational videos in university teacher education and in the second and third phases of teacher education (Authors 2021; Matthes et al. 2017: 165–168).

Analysed Educational Channels K1 (2020): Die Merkhilfe: Nachhilfe & Allgemeinwissen. https://www.youtube.com/channel/UC_cCcxd8yUwIu1-rt5dpBdw (13.08.2020). K2 (2020): explainity ® Erklärvideos. https://www.youtube.com/c/explainity-erklaert/featured (13.08.2020). K3 (2020): FWU – Bildungsmedien. https://www.youtube.com/user/Bildungsmedien (13.08.2020). K4 (2020): Lehrerschmidt: Einfach Lernen! https://www.youtube.com/channel/UCy0FxMgGUlRnkxCoNZUNRQQ (13.08.2020). K5 (2020): Mathe - simpleclub. https://www.youtube.com/user/TheSimpleMaths (13.08.2020). K6 (2020): Mathe by Daniel Jung. https://www.youtube.com/user/beckuplearning (13.08.2020). K7 (2020): MrWissen2go. https://www.youtube.com/c/MrWissen2go/featured (13.08.2020). K8 (2020): musstewissen Deutsch. https://www.youtube.com/c/musstewissenDeutsch/featured (13.08.2020). K9 (2020): sofatutor. https://www.youtube.com/channel/UCE3vUBi72Mvy1TpPAPoq_9g (13.08.2020). K10 (2020): Sommers Weltliteratur to go. https://www.youtube.com/user/mwstubes (13.08.2020). K11 (2020): Terra X statt Schule. https://www.youtube.com/channel/UCP8e6wK18jJNdJpKkeQDlsA (13.08.2020). References Authors 2019 [anonymized] Authors 2021 [anonymized] Araújo, Camila Souza et al. (2017): Characterizing Videos, Audience and Advertising in Youtube Channels for Kids. In: Ciampaglia, Giovanni Luca/Mashhadi, Afra/Yasseri, Taha (Hrsg.): Social Informatics. 9th International Conference, SocInfo 2017 Oxford, UK, September 13–15, 2017. Proceedings, Part I. Cham: Springer, S. 341–359. Bortz, Jürgen;/Döring, Nicola (2002): Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler. 3., überarb. Aufl., Berlin: Springer. Brame, Cynthia J. (2016): Effective Educational Videos. In: CBE Life Sciences Education, 15, H. 4, S. 1–6. Burgess, Jean/Green, Joshua (2018): YouTube. Online Video and Partcipatory Culture. 2. Aufl., Cambridge: Polity Press. Dorgerloh, Stephan (2020): Wie Erklärvideos und Lehrfilme bereitstellen? In: Dorgerloh, Stephan; Wolf, Karsten D. (Hrsg.) (2020): Lehren und Lernen mit Tutorials und Erklärvideos. Weinheim: Beltz, S. 138–139. Duffett, Rodney (2020): The YouTube Marketing Communication Effect on Cognitive, Affective and Behavioural Attitudes among Generation Z Consumers. In: Sustainability 12, H. 12, S. 55–75. Fey, Carl-Christian (2015): Kostenfreie Online-Lehrmittel. Bad Heilbrunn: Klinkhardt. Google (2020b): Get an Overview of Channel Performance. https://support.google.com/youtube/answer/9314414?hl=en Kulgemeyer, Christoph (2018): A Framework of Effective Science Explanation Videos Informed by Criteria for Instructional Explanations. Research in Science Education 50 (2441–2462). DOI: 10.1007/S11165-018-9787-7 Matthes, Eva; Heiland, Thomas; Meyer, Anna-Maria; Neumann, Dominik (2017): Das Augsburger Projekt „Förderung der Lehrerprofessionalität im Umgang mit Heterogenität“. In: DDS 109 (2), S. 163–174. Medienpädagogischer Forschungsverbund Südwest (mpfs) (2018): KIM-Studie 2018. https://www.mpfs.de/fileadmin/files/Studien/KIM/2018/KIM-Studie_2018_web.pdf Medienpädagogischer Forschungsverbund Südwest (mpfs) (2020): JIMplus 2020 Corona-Zusatzuntersuchung. https://www.mpfs.de/fileadmin/files/Studien/JIM/JIMplus_2020/JIMplus_2020_Corona.pdf Lotz, Miriam; Gabriel, Katrin; Lipowsky, Frank (2013): Niedrig und hoch inferente Verfahren der Unterrichtsbeobachtung. In: Zeitschrift für Pädagogik 59 (3), S. 357–380. Lütters, Holger (2004): Nicht-reaktive Datenerhebung im Internet. In: Holger Lütters (Hrsg.): Online-Marktforschung. Wiesbaden: Deutscher Universitätsverlag, S. 95–114. Welbourne, Dustin. J./Grant, Will. J. (2016). Science Communication on YouTube. In: Public Understanding of Science 25, H. 6, S. 706–718.