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International Journal of Science and Mathematics Education
https://doi.org/10.1007/s10763-022-10351-w
Exploring the Relationship Between Surface
Features and Explaining Quality of YouTube
Explanatory Videos
Philipp Bitzenbauer1·Sebastian H ¨
ofler1·Joaquin M. Veith2·
Bianca Winkler1·Tim Zenger1·Christoph Kulgemeyer3
Received: 30 July 2022 / Accepted: 19 December 2022
©The Author(s) 2023
Abstract
Physics education research on explanatory videos has experienced a boost in recent
years. Due to the vast number of explanatory videos available online, e.g. on
YouTube, finding videos of high explaining quality is a challenging task for learners,
teachers, and lecturers alike. Prior research on the explaining quality of explanatory
videos on classical mechanics topics has uncovered that the surface features provided
by YouTube (e.g. number of views or likes) do not seem to be suitable indicators of
the videos’ explaining quality. Instead, the number of content-related comments was
found to be statistically significantly correlated with the explaining quality. To date,
these findings have only been observed in the context of explanatory videos on clas-
sical mechanics topics. The question arises whether similar correlations between the
explaining quality and YouTube surface features can be found for videos on topics
that are difficult to access visually and verbally, for example from quantum physics.
Therefore, we conducted an exploratory study analyzing the explaining quality of
N=60 YouTube videos on quantum entanglement and tunnelling. To this end,
we made use of a category-based measure of explanatory videos’ explaining quality
from the literature. We report correlations between the videos’ explaining quality and
the surface features provided by YouTube. On the one hand, our results substantiate
earlier findings for mechanics topics. On other hand, partial correlations shed new
Philipp Bitzenbauer
philipp.bitzenbauer@fau.de
1Friedrich-Alexander-Universit¨
at Erlangen-N¨urnberg, Professur f¨ur Didaktik der Physik,
Staudtstr. 7, 91058 Erlangen, Germany
2Institute of Mathematics and Applied Computer Science, Stiftung University Hildesheim,
Samelsonplatz 1, 31141 Hildesheim, Germany
3Institute of Science Education, Physics Education Group, University of Bremen,
Otto-Hahn-Allee 1, 28359 Bremen, Germany
P. Bitzenbauer et al.
light on the relationship between YouTube’s surface features and explaining quality
of explanatory videos.
Keywords Explaining quality ·Explanatory videos ·Entanglement ·Tunnelling ·
Quantum physics ·Surface features
Introduction
Quantum mechanics is a topical theme of physics in general (cf. Ac´
ın et al., 2018),
and of physics education research in particular (cf. Bitzenbauer, 2021b). With today’s
technological advancements, students may not only come into contact with quan-
tum physics in formal learning settings, e.g. in undergraduate university courses
(cf. Galvez et al., 2005; Marshman & Singh, 2019; Passante, Emigh, & Shaffer,
2015; Pearson & Jackson, 2010; Singh, 2001; Zhu & Singh, 2012a; Zhu & Singh,
2012b), but also in the informal context: For example, interested learners can access
the quantum world via multiple digital resources, such as smart-phone/tablet apps
(e.g. Oss & Rosi, 2015), AR/VR applications (e.g. Dorland et al., 2019; Suprapto,
Nandyansah, & Mubarok, 2020), games (e.g. Chiofalo, Foti, Michelini, Santi, &
Stefanel, 2022;Seskiretal.,2022), or explanatory videos (e.g. Bitzenbauer, 2021a).
Explanatory videos are brief videos — typically up to 10 min maximum — aimed
at introducing and explaining a certain topic of interest (cf. Wolf & Kratzer, 2015).
They have increasingly been discussed in science education research in recent years
(e.g. Kulgemeyer & Wittwer, 2022; Pekdag & Le Marechal, 2010; Schroeder &
Traxler, 2017), both in the context of formal and informal learning environments,
in particular on YouTube (e.g. Beautemps & Bresges, 2021; Kulgemeyer & Peters,
2016; Pattier, 2021). In the literature, factors that seem to be conducive to the suc-
cess and popularity of explanatory YouTube videos on scientific topics have been
revealed (Beautemps & Bresges, 2021; Welbourne & Grant, 2016), e.g. regarding the
structure of a video (Beautemps & Bresges, 2021). While it is desirable to reach as
many people as possible, the main goal associated with the development of explana-
tory videos, of course, is to support student learning. Besides ensuring the success
of the video, creators thus have to increase the quality of the explanations offered in
their explanatory videos.
From the physics education research perspective, it is crucial to assist learners,
teachers, and university lecturers in selecting videos with high explanation quality
from the plethora of (online) resources. In the case of YouTube explanatory videos,
their popularity is publicly shown by means of different surface features, such as the
number of views, the ratings of the video (e.g. the number of likes), or via the com-
ment section. However, it remains open as to whether or not these surface features
indeed correlate with the explanatory video’s explaining quality, and hence, may
serve as some kind of quality indicator in this respect. In other words: Can teachers
and students rely on them?
Exploring the Relationship Between Surface Features and Explaining...
This question has already been posed by Kulgemeyer and Peters (2016). The
authors presented a measure of explaining quality to investigate the abovementioned
question in the context of YouTube explanatory videos on two topics from classical
mechanics, namely Newton’s third law of motion and Kepler’s laws (Kulgemeyer &
Peters, 2016). In their exploratory study, the number of content-related comments
given by users below a specific video turned out to be the only variable that was sta-
tistically significantly correlated with the explaining quality of explanatory videos —
neither the number of views nor the number of likes or dislikes showed correlations
to explaining quality that were statistically significant (Kulgemeyer & Peters, 2016).
Kulgemeyer and Peters (2016) see the need for further studies on the relationship
between surface features provided by YouTube and explaining quality, in particu-
lar, regarding other topics. They developed a hypothesis on this relationship that
requires further evidence, and furthermore, suggest that YouTube metrics are influ-
enced not only by the explaining quality, but also by the medial design of the videos.
Against this backdrop, videos on topics from quantum physics seem to add a valu-
able perspective here since the media design is even more crucial than in mechanics
in order to make the invisible accessible to students: Quantum physics differs fun-
damentally from classical mechanics, especially since its concepts are not directly
visible with the naked eye. Thus, explanations of quantum physics topics arguably
require specifically varied explanations. As a result, the question arises as to whether
or not the metrics of YouTube explanatory videos about quantum concepts show sim-
ilar correlations to an established measure of explaining quality as has previously
been revealed by Kulgemeyer and Peters (2016) for explanatory videos on classical
mechanics topics. This is where this research project comes in: We investigate the
explaining quality of YouTube explanatory videos on two genuine quantum physics
topics without classical analogies, namely quantum entanglement and quantum tun-
nelling. To this end, the research methods used by Kulgemeyer and Peters (2016)
were leveraged into our study. The objective of the research project presented in this
article is to expand on results of Kulgemeyer and Peters (2016) by exploring corre-
lations between the YouTube surface metrics (e.g. likes, dislikes, views, number of
days since release, number of relevant comments) of explanatory videos on these two
quantum topics and the explaining quality of these videos.
Research Questions
The present study addresses the following research questions:
1. How is the explaining quality of YouTube explanatory videos on quantum entan-
glement and quantum tunnelling correlated with the videos’ metrics such as the
number of views, the number of likes, or the number of dislikes?
2. How is the number of content-related comments correlated with the explaining
quality of YouTube explanatory videos on quantum entanglement and quantum
tunnelling?
P. Bitzenbauer et al.
Research Background
Explaining Physics
Instructional explanations are “designed with the specific purpose of teaching a stu-
dent or group of students” (Leinhardt & Steele, 2005, p. 90). Hence, instructional
explanations need to be distinguished from scientific explanations (Treagust & Har-
rison, 1999). Wittwer and Renkl (2008) uncovered four factors that lead to effective
instructional explanations; for example, they should...
1. ...“be adapted to the learner’s knowledge prerequisites” (Wittwer & Renkl, 2008,
p. 51),
2. ...“focus on concepts and principles” (Wittwer & Renkl, 2008, p. 53),
3. ...“should be integrated into the learners’ ongoing cognitive activities” (Wittwer
& Renkl, 2008, p. 55), and
4. ...“should not replace learners’ knowledge-construction activities” (Wittwer &
Renkl, 2008, p. 56)
These factors have been expanded to a total of nine factors in a 2019 review
addressing instructional explanations in science teaching (Kulgemeyer, 2019, p. 90).
An important criterion for effective instructional explanations is the adaption to
the explainee because this criterion mirrors that explaining is to be regarded a
constructivist process (Kulgemeyer & Peters, 2016).
The constructivist nature of explanations is reflected in the communication model
for explaining physics presented by Kulgemeyer and Schecker (2013). This model
consists of four pillars, namely the explainer, the explanation itself, the explainee,
and the explainee’s feedback. The fact that a good explanation requires
1. Constant evaluation of the explainee’s feedback, and
2. Prompt adaptation of the explanation based on that feedback,
is at the heart of this model (Kulgemeyer & Schecker, 2013). According to the com-
munication model for explaining physics, “the explainer can vary the explanation
on four levels based on this feedback, ranging from the language code, the graphic
representation form and the mathematic code, to using examples and analogies”
(Kulgemeyer & Peters, 2016,p.3).
Design Principles for Explanatory Videos
The Cognitive load theory (e.g. Sweller, 1988;1994; Sweller, van Merrienboer, &
Paas, 1998) assumes a limited capacity of working memory caused by a cognitive
load on learners in learning environments, which — in its modern view (cf. Sweller,
van Merrienboer, & Paas, 2019) — is composed by
•Intrinsic cognitive load which is dependent on the concrete learning task, the
students’ prior knowledge, or the teaching materials used, and
•Extraneous cognitive load stemming from irrelevant cognitive processes that tie
up working memory capacities and thus hinder the learning process
Exploring the Relationship Between Surface Features and Explaining...
According to Sweller et al. (2019), the Cognitive load theory “provides evidence-
informed principles that can be applied to the design of instructional messages or
relatively short instructional units, such as lessons, written materials consisting of
text and pictures, and educational multimedia” (p. 274).
The Cognitive Theory of Multimedia Learning (cf. Mayer, 1999) builds upon the
abovementioned Cognitive load theory. This theory is based on three fundamen-
tal assumptions that, taken together, describe how auditory-verbal or visual-imagery
information is processed toward long-term memory:
•The dual-channel assumption describes that “humans possess separate channels
for processing visual and auditory information” (Mayer, 2009, p. 63)
•The limited-capacity assumption describes that each of the abovementioned
channels can only process a limited amount of “chunks” (Mayer, 2009, p. 67) of
information simultaneously
•The active-processing assumption describes that students’ active engangement is
necessary for students constructing knowledge (Mayer, 2009)
Both the Cognitive load theory and the Cognitive Theory of Multimedia Learn-
ing have been the basis for prior research on explanatory videos aimed at fostering
student learning (cf. Kruger & Doherty, 2016; Noor, Aini, & Hamizan, 2014). In
addition, different studies have derived design principles that may influence the effec-
tiveness of explanatory videos against the backdrop of the abovementioned theories
(e.g. Brame, 2016;Kay,2014; Muller, 2008). For example, it has been indicated
that the integration of interactive elements into explanatory videos (Delen, Liew, &
Willson, 2014) or the use of a 1st-person perspective in explanatory videos (Fiorella,
van Gog, Hoogerheide, & Mayer, 2017) might have a positive impact on students’
performance. Findeisen, Horn and Seifried (2019) reviewed and systematized stud-
ies dealing with potential effects of explanatory videos’ design principles on student
learning, and derived guidelines for the development of explanatory videos based on
the overall picture emerging from current empirical findings.
Explaining Quality of Explanatory Videos
In the previous sections, we reviewed both the current state of research on explaining
physics and on design criteria for the development of explanatory videos. In this
section, both perspectives are merged in order to shed more light on the state of
research on the explanatory quality of explanatory videos.
Kulgemeyer (2020) presented a framework for effective explanation videos. This
framework is
•...consistent with guidelines on the quality of explanatory videos published
elsewhere in the literature (e.g. Brame, 2016; Findeisen et al., 2019), and
•...acknowledges research on multimedia learning (Kulgemeyer, 2020),
while building upon state-of-the-art research on instructional explanations (e.g. Gee-
lan, 2012, Wittwer & Renkl, 2008). In this framework, seven factors comprising a
total of 14 features are described to have an impact on the effectiveness of explana-
tory videos (Kulgemeyer, 2020, p. 2450). Examples are the use of summaries (factor:
P. Bitzenbauer et al.
structure of the video), the use of an appropriate language-level (factor: tools for
adaption), the avoidance of digressions (factor: minimal explanation), or the adap-
tion to prior knowledge, misconceptions, and interest (factor: adaption). An overview
of the whole framework for effective explanation videos is presented in Kulgemeyer
(2020, p. 2450).
The abovementioned framework has been tested empirically in order to clarify
as to whether or not an explanatory video developed with respect to the framework
leads to higher student achievement compared to a video that has not strictly been
developed according to the framework (Kulgemeyer, 2020). The results of this study
revealed that students learning with an explanation video adhering strongly to this
framework showed significantly more declarative knowledge in a post-test than stu-
dents learning with a video that has not strictly been developed according to the
framework (d=0.42). However, no statistically significant difference in the post-test
scores regarding conceptual knowledge was observed.
Evaluation of Explanatory Videos’ Explaining Quality
An online test which allows for the assessment of physics explanatory skills has been
published by Bartels and Kulgemeyer (2019). This test has been developed both for
its usage in teacher education and for self-assessment.
Moreover, based on the communication model for explaining physics (Kulge-
meyer & Schecker, 2013), Kulgemeyer and Tomczyszyn (2015, p. 121) developed
a process-oriented and category-based measure for the assessment of explanation
skills. Kulgemeyer and Peters (2016) adopted this category-based measure for the
evaluation of explanatory videos’ explaining quality. The category system to eval-
uate explanatory videos’ explaining quality (cf. Appendix) consists of seven main
categories (content, structure, use of language, contexts and examples, mathemat-
ics, interrogation, non-verbal elements) comprising a total of 31 different categories.
Each of these categories is either assigned to a certain explanatory video (= 1 point)
or not (= 0 points). Four out of the 31 subcategories ((1) scientific mistake, (2) ignor-
ing students’ comment, (3) leaving new technical term uncommented, (4) without
context) are related to a decrease of explaining quality, and hence, a negative point
(= −1 point) is allocated to the video for their occurrence.
Within the scope of evaluating the explaining quality of explanatory videos (i.e. in
the course of categorization), each category is considered uniformly and there is no
counting of a successive occurrence of the same category, “since repetitions of the
same wording or the repeated use of a similar explaining aid without any variation
are not considered a rich and varied explanation” (Kulgemeyer & Peters, 2016,p.6).
By summing up the points received on the basis of the categories assigned, a specific
number of “category points” (Kulgemeyer & Peters, 2016, p. 6), referred to as CP,
can be calculated for a given explanatory video (Kulgemeyer & Peters, 2016,p.6):
CP =X++X−,
where X+denotes the number of positive categories assigned to a video, and X−
stands for the number of all negative categories assigned to a video. The category
Exploring the Relationship Between Surface Features and Explaining...
points (with the upper limit of 28 CP) serve as a measure of an explanatory video’s
explaining quality as has been shown by Kulgemeyer and Peters (2016).
It is important to note that the category points assigned to a specific explanatory
video may neither judge the video’s overall quality (e.g. a video’s technical design is
not taken into account) nor do the CP help finding the best explanation of a specific
topic under investigation among multiple explanatory videos. Instead, the rationale
underlying this measure is “to distinguish between rich and varied explanations on
the one hand and those with fewer variations on the other” because “those with fewer
variations in their explanations may be less suitable for a wider range of viewers as
some learners’ needs may not be considered” (Kulgemeyer & Peters, 2016,p.9).
Methods
In this section, we outline the methodology applied in our exploratory study to
approach a clarification of the research questions. We aim at expanding on the
study of Kulgemeyer and Peters (2016) according to which none of the correlations
between the surface features provided for YouTube explanatory videos and their
explaining quality was statistically significant, except from the number of content-
related comments. In a further study, Kocyigit and Akaltun (2019) even conclude
that the “number of views, likes, dislikes, and comments per day is not a predictor of
high-quality videos on YouTube” (p. 1267).
Sample
Content Domain
We decided to analyze YouTube explanatory videos on two topics: (a) quantum
entanglement and (b) quantum tunnelling. We analyzed videos addressing these top-
ics because neither quantum entanglement nor quantum tunnelling has any classical
analogy, and the quantum physics formalism does not enable a space-time descrip-
tion of these concepts (cf. Ubben & Bitzenbauer, 2022). In this way, our study allows
best to contrast the previous findings of Kulgemeyer and Peters (2016) who analyzed
explanatory videos on topics of classical mechanics.
Inclusion-Exclusion Criteria and Search Procedure
Following Kulgemeyer and Peters (2016), we found the videos to be included
in our sample via YouTube’s search engine applying the search strings “quan-
tum entanglement” and “quantum tunnelling”, respectively. We used the following
inclusion-exclusion criteria for selecting videos appropriate for data analysis:
•The video is published in the English language
•The video exclusively covers one of the two topics quantum entanglement or
quantum tunnelling, respectively. Videos that covered both topics were excluded
P. Bitzenbauer et al.
Furthermore, videos addressing one of the topics under investigation plus at least
one further (different) topic were excluded.
•Video-recorded lectures (or excerpts thereof) were excluded, since recorded lec-
tures “do not share the explainers’ intentional core of publishing a concise
explanatory video” (Kulgemeyer & Peters, 2016,p.4)
•The video has a maximum duration of 10 min
•The video is scientifically sound (cf. Kulgemeyer & Peters, 2016)
The latter criterion was important because it only makes sense to compare “the
explaining quality of scientifically correct explanations” (Kulgemeyer & Peters,
2016, p.5). Applying the abovementioned search strings, we found more than
100,000 videos on both topics. A title-caption screening of the search results led to
the exclusion of the majority of these videos since they did not fulfill the inclusion
criteria (in this stage most often due to a duration above 10 min, the coverage of top-
ics beyond the ones under investigation, or representing recorded lectures). In a next
step, we reviewed about 200 videos on each of the topics quantum entanglement or
tunnelling in detail. Again, we excluded the videos that did not fulfill the inclusion
criteria (in this stage most often due to serious scientific errors). Lastly, for our final
sample, we (a) settled on videos with comparable run-times of around 5 min as has
been done in the prior study conducted by Kulgemeyer and Peters (2016), and (b)
aimed for a sample size comparable to the one of the earlier study in the classical
mechanics context (Kulgemeyer & Peters, 2016). The final sample consists of 60
YouTube explanatory videos that were included for data analysis, 30 of which address
the topic of quantum entanglement, and 30 of which focus on quantum tunnelling.
Description of the Sample
The mean duration of the selected videos is m=4.97 min with a standard deviation
of SD =2.43 min. The explanatory videos on quantum entanglement (m=4.74 min,
SD =2.38 min) were of similar length as those on quantum tunnelling (m=5.20
min, SD =2.48 min). Moreover, the videos in our sample are of similar length as the
ones included in the prior study (cf. Kulgemeyer & Peters, 2016).
Data Collection
The explanatory videos included in the final selection have been analyzed in August
and September 2021. For the exploration of our research questions, the data col-
lection comprised three aspects: In a first step, we collected each video’s surface
features, i.e. the number of likes and dislikes, the number of views,andthepublica-
tion date to calculate the videos’ time online (in days). Additionally, we recorded the
number of subscribers to the channels by which the videos were published. The aver-
age view duration has been a further surface feature which was included in the study
of Kulgemeyer and Peters (2016 p. 5) on explanatory videos on classical mechanics
topics. However, at the time of conducting our data collection, this feature was not
publicly accessible anymore and hence, it is not included in our analysis. In addi-
tion, the dislike statistic is not publicly available anymore since the end of 2021 —
Exploring the Relationship Between Surface Features and Explaining...
since our data collection was conducted in August and September 2021; however, we
kept the number of dislikes for each video in our dataset and also included it in the
data analysis. This allows for a more comprehensive comparison to the earlier results
published by Kulgemeyer and Peters (2016) and may help to better understand the
interaction of users with explanatory videos. For a description of all the abovemen-
tioned YouTube metrics, we refer the reader to the YouTube Analytics and Reporting
APIs (2022).
In a second step, we categorized the comments given below the videos in order
to receive the number of relevant comments for each video. We provide a proper
description of (a) the term relevant comment and (b) the categorization procedure
in the data analysis section. We explored relevant comments because they “provide
by far the most intense communication channel between explainer and addressee”
(Kulgemeyer & Peters, 2016,p.5).
Lastly, following the data collection method from Kulgemeyer and Peters (2016),
we used the category system described above (cf. Appendix) to assess the explaining
quality of the explanatory videos included in our sample. The coding was performed
by two independent raters. The inter-rater reliability expressed via Cohen’s kappa
can be regarded substantial (κ=0.79) according to Cohen (1988). Against this
backdrop, the category system used in this study allows for an objective assessment
of the explaining quality of explanatory videos. Furthermore, the reliability of the
measure has been found to be satisfactory (Cronbach’s α=0.58; in the earlier
study by Kulgemeyer and Peters (2016), a comparable value of α=0.69 has been
reported). Moreover, the category system used for this study allows for a valid mea-
sure of explanatory videos’ explaining quality as has been justified by Kulgemeyer
and Peters (2016).
As a last step of data collection, we calculated the category points CP for each
explanatory video included in our sample. These category points were then further
processed to data analysis.
Data Analysis Carried Out the Answer Research Question 1
We report descriptive statistics (range, median Mdn, mean m, standard deviation
SD) regarding the category points of the explanatory videos on quantum entangle-
ment and quantum tunnelling, respectively. In addition, we introduce a new metric
to investigate this research question: We assumed that the interaction with a specific
explanatory video, i.e. giving a like or a dislike to a video, requires the user to be cog-
nitively activated to some extent. We therefore introduced the variable interactions
calculated via
interactions =likes +dislikes,
to explore the relationship between explaining quality and the number of interactions.
This variable will provide further insights into how users interact with explanatory
videos depending on their explaining quality.
P. Bitzenbauer et al.
We conducted a correlation analysis in order to explore relationships between the
videos’ explaining quality (in category points CP) on the one hand, and the surface
features on the other hand. We report Pearson’s correlation coefficient rbecause the
data are of metric scale. We interpret correlation coefficients according to Cohen
(1988): weak correlation for 0.1 <|r|<0.3, moderate correlation for 0.3 ≤|r|<
0.5, and strong correlation for |r|≥0.5. In addition, we report partial correlations to
verify that observed relationships are no artefact caused by
•The videos’ time online, i.e. the time that has passed between the publication of
a video and the data collection, and
•The number of subscribers to the channels by which the videos were published
The latter control variable seems particularly important due to the fact that the
YouTube algorithms promote videos published by popular channels which in turn
leads to high numbers of views for these videos. This might influence the results, and
hence, deserves special attention.
Data Analysis Carried Out the Answer Research Question 2
The comments below each video included in our sample have been categorized. For
the categorization, we used the category system presented by Kulgemeyer and Peters
(2016) which consists of four categories:
1. Comment on content: “further question or comment on notations” (Kulgemeyer
& Peters, 2016,p.8)
2. Comment on explanation: “constructive criticisms and inquiries for more
videos” (Kulgemeyer & Peters, 2016 p. 8).
3. Comment on explainer’s style: “comments on the style including a reason”
(Kulgemeyer & Peters, 2016,p.8)
4. Comment on use: description of “the viewer’s use of the video, e.g. revising,
preparing a talk or learning for a test” (Kulgemeyer & Peters, 2016,p.8)
All comments that could be assigned to at least one of these categories were con-
sidered as relevant comments. Comments that could not be assigned to any of
these categories, conversely, were excluded from further analysis because they were
not related specifically to the content presented in the respective video or to the
explanation offered within. For the further analysis, we refrained from a deeper
differentiation between the different categories as has been done by Kulgemeyer
and Peters (2016) because research question 2 only addresses relevant comments in
general.
The categorization of the all comments underneath N=60 explanatory videos
included in our sample led to a total of 1452 relevant comments. The number of
relevant comments for each video was included in our data set as a metric variable
and was used for correlation analysis. Again, we additionally calculated partial cor-
relations to verify that observed relationships are no artefact caused by the videos’
time online, or the number of subscribers to the channels by which the videos were
published.
Exploring the Relationship Between Surface Features and Explaining...
Table 1 Descriptive statistics on the measure of explaining quality of the videos included in our sample
(expressed in category points CP)
Range Mdn mSD
Explaining quality total samplea2–18 11.00 11.02 3.28
Explaining quality quantum entanglementb2–18 11.00 11.03 3.62
Explaining quality quantum tunnellingc4–18 11.50 11.00 2.96
aN=60. bN=30. cN=30
Results
Descriptives
The median value of the explanatory videos’ explaining quality (measured in CP)
was Mdn =11 CP for the total sample, ranging from 2 CP (assigned to one video of
the sample) to 18 CP (assigned to two videos of the sample). In Table 1, descriptive
statistics on the category points assigned to the videos comprised in our sample are
reported separately for the two subject areas under investigation, namely quantum
entanglement and quantum tunnelling, respectively.
Correlation Analysis
The correlation analysis results are summarized in Table 2. Within the total sample,
we find statistically significant correlations between the videos’ explaining quality
and the number of views (r=0.27, p<0.05) as well as the number of likes
(r=0.37, p<0.01). The highest correlation is uncovered between the videos’
explaining quality and the number of relevant comments (r=0.46, p<0.01),
whereas the correlation between the videos’ explaining quality and their time online
does not differ from 0 with statistical significance.
A striking observation is the positive correlation between the number of dislikes
and the measure of explaining quality, both in the total sample (r=0.32, p<0.05)
and the two sub-samples including videos on quantum entanglement (r=0.37, n.s.)
and quantum tunnelling (r=0.30, n.s.).
Furthermore, the total number of user interactions is correlated significantly with
the videos’ explaining quality — no matter of whether these interactions result in a
like or a dislike in the end (r=0.39, p<0.01, 95% CI [0.14;0.59]). It is necessary
to control the correlations presented in Table 2for the videos’ time online (in days),
and the number of subscribers to the channels by which the videos were published
in order to explore this in more detail. Therefore, we report partial correlations in the
next subsection.
Partial Correlations
In this subsection, we report partial correlations which refer to the entire sample. This
means that we do not distinguish between the sub-samples here for the sake of clarity.
P. Bitzenbauer et al.
Table 2 Pearson’s correlation coefficient rbetween the measure of explaining quality (in CP) and the
surface features (incl. number of relevant comments) for the total sample, the videos on quantum entan-
glement, and the ones on quantum tunnelling, respectively. For all correlations, we report 95% confidence
intervals (95%-CI)
Surface feature r95%-CI
Explaining quality Time online (days) −0.14 [−0.38;0.12]
Tot al sa mp l eaVi ews 0 .2 7∗[0.02;0.49]
Likes 0.37∗∗ [0.13;0.57]
Dislikes 0.32∗[0.06;0.53]
Relevant comments 0.46∗∗ [0.24;0.64]
Explaining quality Time online (days) 0.00 [−0.36;0.36]
Quantum entanglementbViews 0. 32 [−0.05;0.61]
Likes 0.42∗[0.08;0.68]
Dislikes 0.37 [0.00;0.65]
Relevant comments 0.59∗∗ [0.29;0.79]
Explaining quality Time online (days) −0.28 [−0.58;0.09]
Quantum tunnellingcViews 0. 23 [−0.15;0.54]
Likes 0.30 [−0.07;0.60]
Dislikes 0.30 [−0.07;0.60]
Relevant comments 0.31(∗)[−0.06;0.60]
Statistical significance of the correlations is denoted by an asterisk: (∗)p<.10. ∗p<.05. ∗∗p<.01.
aN=60. bN=30. cN=30
Controlling the correlations between our explanatory videos’ explaining quality
(measured in CPs) and the YouTube surface features for the videos’ times online (cf.
Tabl e 3), we observe that all metrics correlate significantly with the explaining qual-
ity, ranging from highly significant (relevant comments) to significant (views and
dislikes). These partial correlations uncover similar relationships between YouTube’s
surface metrics and the videos’ explaining quality as the ones presented earlier
(cf. Table 2).
Table 3 Partial correlations (controlled for the time online) between the measure of explaining quality (in
CP) and YouTube’s surface metrics as well as the number of interactions
Controlled for: time online (days) Variable r
Explaining quality Views 0.33∗
Tot al sa mp l e aLikes 0.40∗∗
Dislikes 0.33∗
Relevant comments 0.55∗∗∗
Interactions 0.41∗∗
Statistical significance of the correlations is denoted by an asterisk: ∗p<.05. ∗∗p<.01. ∗∗∗ p<.001.
aN=60
Exploring the Relationship Between Surface Features and Explaining...
Table 4 Partial correlations (controlled for the number of subscribers) between the measure of explaining
quality (in CP) and YouTube’s surface metrics as well as the number of interactions
Controlled for: Number of subscribers Variable r
Explaining quality Views 0.23
Tot al sa mp l eaLikes 0.43∗∗
Dislikes 0.26
Relevant comments 0.47∗∗∗
Interactions 0.43∗∗
Statistical significance of the correlations is denoted by an asterisk: ∗p<.05.∗∗p<.01.∗∗∗ p<.001.
aN=60
In a next step, we controlled for the number of subscribers to the channels by
which the videos were published. The corresponding partial correlations are shown
in Table 4: Only three of the correlations remain statistically significant in this case,
namely the ones between the explanatory videos’ explaining quality and the num-
ber of likes (r=0.43, p<0.01), the number of relevant comments (r=0.47,
p<0.001), and the number of interactions (r=0.43, p<0.01). In contrast, both
the correlations of the videos’ explaining quality to the number of views, and the
number of dislikes are not statistically significant anymore. We will discuss these
observations in the “Discussion” section.
Discussion
In our exploratory study, we investigated as to how the explaining quality of YouTube
explanatory videos on genuine quantum topics such as quantum entanglement and
quantum tunnelling is correlated with the surface features provided by YouTube
alongside each online video. In this section, we discuss the results of our study with
regard to our research questions, and against the backdrop of prior research.
Kulgemeyer and Peters (2016) analyzed the relationship between the explaining
quality of instructional videos on classical mechanics topics and YouTube’s surface
features. However, while a lot of scholars investigated the quality of instructional
videos in general, to the authors’ knowledge, no further studies exploring correla-
tions between explaining quality and surface features have been published in the
literature. Brame (2016) researched the literature on how to manage cognitive load
of educational videos and how to maximize student engagement with a video. Sim-
ilarly, Findeisen et al. (2019) review studies to investigate how didactical elements
of explanatory videos should be designed in order to best facilitate student learning.
On another note, Beautemps and Bresges (2021) conducted a questionnaire survey
to uncover key elements for a successful educational YouTube video from a viewers’
perspective. Nonetheless, an in-depth investigation of YouTube explanatory videos’
P. Bitzenbauer et al.
surface metrics and their relationship with the explaining quality has only been car-
ried out in the context of mechanics (Kulgemeyer & Peters, 2016) which we will
expand upon in the following.
Discussion of Research Question 1
While we observe slight differences between the videos on entanglement and those
on quantum tunnelling in terms of correlations between their explaining quality and
YouTube surface features (cf. Table 2), the global tendencies are similar for the videos
on both topics. In particular, for each metric, the correlations’ 95% confidence inter-
val in the entanglement group overlaps with the corresponding one in the tunnelling
group to such an extent that each correlation coefficient lies in the confidence interval
of the other one. Hence, the small deviations between the observed correlations for
videos on entanglement and quantum tunnelling might be drawn back to the choice
of a specific sample of N=60 YouTube videos out of the cornucopia of videos
in the depths of the internet for this study (cf. “Limitation” section). As such, we
refrain from providing in-depth explanations for this observation — further research
is needed to shed more light on this issue.
In total, our results compare well with the findings reported earlier for the mechan-
ics context (cf. Kulgemeyer & Peters, 2016): While the correlations presented in both
studies seem different at first glance (cf. Table 5), we note that most of the corre-
lations reported by Kulgemeyer and Peters (2016) fall within the 95% confidence
intervals of our correlation coefficients (or vice versa).
Table 5 Pearson’s correlation coefficient rbetween the measure of explaining quality (in CP) and the sur-
face features provided by YouTube. For the correlations calculated in our study, we report 95% confidence
intervals (95%-CI)
Surface feature r95%-CI
Explaining quality Time online (days) −0.14 [−0.38;0.12]
Videos on quantum topicsaViews 0.2 7∗[0.02;0.49]
Likes 0.37∗∗ [0.13;0.57]
Dislikes 0.32∗[0.06;0.53]
Relevant comments 0.46∗∗ [0.24;0.64]
Explaining quality Time online (days) −0.05 −
Videos on mechanics topicsbViews −0.26 −
(Kulgemeyer & Peters, 2016) Likes 0.21 −
Dislikes −0.09 −
Relevant comments 0.38∗∗ −
Statistical significance of the correlations is denoted by an asterisk: ∗p¡ .05. ∗∗p<.01. aN=60, this
study. bN=51 ( cf. Kulgemeyer & Peters, 2016, p.10)
Exploring the Relationship Between Surface Features and Explaining...
In addition, our results also shed new light on the underlying relationships: In
their 2016 article, Kulgemeyer and Peters (2016) found no statistically significant
correlation between the videos’ explaining quality and the number of likes although
the authors expected such a correlation due to the “illusion of understanding”: “Stu-
dents do not realize the possible inconsistencies in their understanding and feel as
if they have understood a topic” (Kulgemeyer & Peters, 2016, p.11). This assump-
tion is supported by empirical evidence from a recently published experimental study
by Kulgemeyer and Wittwer (2022). For the explanatory videos on quantum topics
included in our sample, we indeed uncovered a statistically significant correlation
between the number of likes and the videos’ explaining quality (r=0.37, p<0.01).
Moreover, we find the number of dislikes (r=0.32, p<0.05) and the number
of views (r=0.27, p<0.05) to have statistically significant correlations with the
explaining quality of the videos on quantum entanglement and tunnelling. In con-
trast, Kulgemeyer and Peters (2016) have not found the corresponding correlations
to be statistically significant for the videos on classical mechanics topics. The analy-
sis of partial correlations, though, puts these differences between the two studies into
perspective: We controlled the correlations between the videos’ explaining quality
and the surface features provided by YouTube for the number of subscribers to the
channels by which the videos were published. As a result, the correlation between
explaining quality and views (r=0.23) loses its statistical significance. To describe
this observation, we go along with Kulgemeyer and Peters (2016) who state that “the
number of views is more influenced by [...] the popularity of the YouTube channel
than the explaining quality” (p. 5). Accordingly, the correlation between explaining
quality and dislikes (r=0.26) loses its statistical significance, though remaining
moderate (cf. Table 4). One reason for this observation might be found in the way
that the videos’ explaining quality is operationalized in this study: The category sys-
tem used to assess a given video’s explaining quality does not comprise categories
related to a viewers’ interests, viewer’s conceptions, or viewer’s level of background
knowledge. Therefore, it seems possible that a viewer who (a) does not understand a
given video, (b) feels bored when watching the video, or (c) feels academically over-
whelmed might react with a “dislike” — independent from the explaining quality of
the video.
Lastly, we newly introduced the number of interactions, i.e. the sum of likes and
dislikes for a given YouTube explanatory video, into the analysis. The number of
interactions correlates statistically significantly with the explaining quality of the
explanatory videos on entanglement and tunnelling: r=0.39, p<0.01. The partial
correlation — when controlling for the number of subscribers of the channels by
which the videos are published — of r=0.43, p<0.01 was even higher.
Discussion of Research Question 2
The number of relevant comments turned out to be most strongly correlated with the
explaining quality of explanatory videos (r=0.46, p<0.01 for the total sample)
on quantum entanglement (r=0.59, p<0.01), and quantum tunnelling (r=0.31,
P. Bitzenbauer et al.
p<0.1). Similarly, Kulgemeyer and Peters (2016, p.10) report a correlation of r=
0.38 (p<0.01) between explaining quality and the number of relevant comments
for videos on Newton’s third law and Kepler’s laws, respectively.
We controlled the correlations between the videos’ explaining quality and the
number of relevant comments for the videos’ time online (in days). As a result, the
partial correlation between explaining quality and number of relevant comments for
the total sample increased (r=0.55, p<0.001). This result is comparable to the
one reported for the mechanics context, where a partial correlation coefficient of
p=0.40, p<0.01 was found (Kulgemeyer & Peters, 2016).
The medium to high correlation between the explanatory videos’ explaining qual-
ity and the number of relevant comments might be justified via the users’ cognitive
activation: “Hence, videos that accumulate plenty of those relevant comments are
more successful in catching viewers’ attention as these videos might use either a
more stimulating explanation or the explanation delivered is considered as a starting
point for further learning progress” (Kulgemeyer & Peters, 2016, p. 12).
Conclusion
Our results support the findings presented earlier for YouTube explanatory videos on
mechanics (cf. Kulgemeyer & Peters, 2016), according to which
•There is a statistically significant correlation between explaining quality and the
number of content-related comments (r=0.46, p<0.001 in our study, cf.
Tabl e 2), and
•YouTube’s surface metrics (e.g. likes) should be considered with caution when
it comes to searching for high-quality videos since by calculating partial corre-
lations controlling for the number of subscribers to the channels by which the
videos were published, the correlations between the videos’ explaining quality
and the number of views as well as the number of dislikes lose their statisti-
cal significance (cf. Table 4). Hence, YouTube’s surface features might not be
fruitful indicators for the explaining quality of explanatory videos
However, focusing on YouTube explanatory videos addressing quantum entangle-
ment and tunnelling, our study contributes to extending previous results presented
by Kulgemeyer and Peters (2016) in two respects:
1. We find a statistically significant correlation between the number of likes
and the explaining quality of explanatory videos on quantum topics (r=
0.37, p<0.01, cf. Table 2). Although such a correlation has already been
assumed in the previous study (cf. Kulgemeyer & Peters, 2016), it could not
be found at that time in the context of explanatory videos on topics of classical
mechanics.
2. Our study hints that the number of interactions (e.g. the sum of likes and dislikes)
might be an indicator for videos of high explaining quality (r=0.39, p<0.01).
Exploring the Relationship Between Surface Features and Explaining...
We argue that this result fits well to the number of relevant comments being
statistically significantly correlated with the explaining quality of explanatory
videos (cf. Table 2).
Against the backdrop of the abovementioned observations, it seems crucial for
educators and students alike to become aware of the fact that a reliable judgement
of the explaining quality of a specific video solely based on YouTube’s surface fea-
tures might be insufficient. In particular, it is noteworthy that the category system (cf.
Tabl e 6) provided in this article was developed for the assessment of scientifically
sound explanatory videos’ explaining quality. Hence, when it comes to searching
high-quality videos for educational purposes, a videos’ scientific quality is to be
considered separately, e.g. taking into account the target group students’ prior knowl-
edge. Consequently, the use of the credit points CP assigned to the videos in the
course of this study may not be used without further reflection since this score is no
indicator of a video’s scientific quality.
Limitations
It is important to note that the results presented in this article should be interpreted
with caution for the following reasons:
1. We could only include a small number of N=60 videos in our sample due
to the huge amount of data and the great effort required for data analysis (e.g.
categorization of all comments underneath each video)
2. Classical correlations, as presented in this article, allow for the exploration
of relationships between variables, but not for the identification of causal
connections
3. The data analysis is largely based on the metrics provided by YouTube, which
are not fully transparent to users (cf. Kulgemeyer & Peters, 2016).
4. In this study, we only analyzed explanatory videos on the topics quantum entan-
glement and tunnelling, and hence, the correlations found are not generalizable
to different topics
Outlook
Despite the abovementioned limitations, our results may serve as a valuable starting
point for future research, in particular with respect to teaching and learning quan-
tum concepts: While in this study only scientifically sound explanatory videos have
been included for the analysis, the internet is crowded with scientifically misleading
or mystifying explanatory videos on quantum concepts, such as quantum entan-
glement and quantum tunnelling. Therefore, future educational research should (a)
explore widespread misconceptions in explanatory videos on quantum concepts, and
(b) make further efforts toward the derivation of evidence-based selection criteria that
support both students and teachers/lecturers in detecting high-quality content out of
the dark noise.
P. Bitzenbauer et al.
Appendix
Table 6 The category system to evaluate explanatory videos’ explaining quality (Kulgemeyer & Peters,
2016, p.7) including a description of each category’s usage in video coding. The subcategories that are
related to a decrease of explaining quality, and hence, lead to a negative point (=−1 CP), are marked
with ‘-’. The categories used in this context are also integrated in the framework of effective explanation
videos (cf. Kulgemeyer, 2020, p.2449). For an in-depth description of all the categories, we refer the reader
to Kulgemeyer (2020)
Main category Subcategories Usage
Content 1. Scientific Mistake (-) A negative CP is assigned to the video if
a physical concept is explained incorrectly,
a wrong definition is given, technical terms
are used incorrectly, or figures are inter-
preted incorrectly. Otherwise, 0 CPs are
assigned to the video.
2. Mistake corrected 1 CP is assigned to the video if a negative
CP was assigned to the video in subcate-
gory 1., and if the corresponding scientific
error is corrected subsequently in the video.
Otherwise, 0 CPs are assigned to the video.
Structure 3. Giving an outlook 1 CP is assigned to the video if, in the
sense of an advance organizer, a outlook is
given on (a) the physical content, or (b) the
structure of the video. Otherwise, 0 CPs are
assigned to the video.
4. Giving a review 1 CP is assigned to the video if it is empha-
sized explicitly that a concept that is being
used has already been introduced earlier in
the video. Otherwise, 0 CPs are assigned to
the video.
5. Giving a summary 1 CP is assigned to the video if a summary
or repetition is explicitly given for a con-
tent aspect that was presented earlier in the
video. Otherwise, 0 CPs are assigned to the
video.
6. Ignoring students’ comment (-) A negative CP is assigned to the video if (a)
a patronizing statement is made or if (b) pos-
sible learners’ comments are not taken into
account in the video. Otherwise, 0 CPs are
assigned to the video.
7. Emphasizing important points 1 CP is assigned to the video if it is explicitly
emphasized that a certain aspect is impor-
tant, should be memorized or needs to be
understood. Otherwise, 0 CPs are assigned
to the video.
8. Open justification of the explaining
approach
1 CP is assigned to the video if the proce-
dure within the video is justified explicitly.
Otherwise, 0 CPs are assigned to the video.
Exploring the Relationship Between Surface Features and Explaining...
Table 6 (continued)
Main category Subcategories Usage
9. Addressing common
misconceptions
1 CP is assigned to the video if widespread
misconceptions on the topic under investi-
gation are explicitly addressed. Otherwise,
0 CPs are assigned to the video.
Use of language 10. Paraphrasing technical
terms
1 CP is assigned to the video if (a) the
use of technical terms is avoided by replac-
ing it with everyday terminology, or if
(b) a technical term is explained but not
explicitly mentioned. Otherwise, 0 CPs are
assigned to the video.
11. Comment techni-
cal term with everyday
language
1 CP is assigned to the video if (a) prop-
erties that define the technical term and
distinguish it from others are described
directly using everyday language, or if (b)
an everyday term with similar meaning
is named and described as an equivalent.
Otherwise, 0 CPs are assigned to the video.
12. Comment technical
term with other technical
terms
1 CP is assigned to the video if (a) prop-
erties that define the technical term and
distinguish it from others are described
directly using technical terms, or if (b)
another technical term of equal meaning is
presented. Otherwise, 0 CPs are assigned
to the video.
13. Leaving new technical
term uncommented (-)
A negative CP is assigned to the video
if a technical term is used without prior
description of its meaning. Otherwise, 0
CPs are assigned to the video.
Contexts
and
examples
14. Addressing explainee 1 CP is assigned to the video if the
explainee is explicitly involved in the
video, e.g. by explicitly stating that the
addressee performs the activity described
in the scenario or example. Otherwise, 0
CPs are assigned to the video.
15. Example close to
everyday life
1 CP is assigned to the video if observa-
tions or physical experiences from every-
day life are used to explain a content
aspect. Otherwise, 0 CPs are assigned to
the video.
16. Abstract example 1 CP is assigned to the video if abstract
examples are included in the video to
explain a content aspect, e.g. in the context
of a thought experiment. Otherwise, 0 CPs
are assigned to the video.
17. Without context (-) A negative CP is assigned to the video if
(a) the explanation of a content aspect is
detached from concrete contexts or if (b)
definitions are presented in a dumb man-
ner. Otherwise, 0 CPs are assigned to the
video.
P. Bitzenbauer et al.
Table 6 (continued)
Main category Subcategories Usage
18. Connecting at least two examples
by showing analogies
1 CP is assigned to the video if at least two
examples are linked by describing analo-
gies that exist between them. Otherwise, 0
CPs are assigned to the video.
19. Connecting example to explained
topic by showing analogies
1 CP is assigned to the video if the rela-
tions between a given example and the
subject of the video are described by analo-
gies. Otherwise, 0 CPs are assigned to the
video.
Mathematics 20. Providing numerical example for
formula
1 CP is assigned to the video if (a) a phys-
ical quantity is replaced by a value in a
formula, or if (b) a value is mentioned or
noted as an example of a physical quan-
tity in a formula. Otherwise, 0 CPs are
assigned to the video.
21. Using formula 1 CP is assigned to the video, if a formula
with at least one arithmetic operator is
explicitly mentioned or noted. Otherwise,
0 CPs are assigned to the video.
22. Describing relationships by use of
‘the more... the less/more’ relations
1 CP is assigned to the video if proportion-
alities are described using the “the more...
the less/more” relations. Otherwise, 0 CPs
are assigned to the video.
23. Using mathematical terms and
idealisations
1 CP is assigned to the video if mathe-
matical terms or or idealizations are used,
e.g. “perfect circle”. Otherwise, 0 CPs are
assigned to the video.
Interrogation 24. Asking further questions 1 CP is assigned to the video if questions
are raised (a) about prior knowledge and
experience, (b) to ensure understanding, or
(c) about how to use the video. Otherwise,
0 CPs are assigned to the video.
Non-verbal elements 25. Using realistic figures (such as
photos)
1 CP is assigned to the video if a content
aspect is explained on the basis of a real
figure, i.e. on the basis of an illustration
showing only real objects without physics
elements. Otherwise, 0 CPs are assigned to
the video.
26. Using analogical figures 1 CP is assigned to the video if a content
aspect is explained on the basis of an ana-
logical figure, i.e. an illustration that con-
tains elements of a real image and a logical
figure. Otherwise, 0 CPs are assigned to
the video.
27. Using logical figures (such as
diagrams)
1 CP is assigned to the video when a con-
tent aspect is explained using a logical
figure, i.e. an illustration that does not con-
tain any real elements. Otherwise, 0 CPs
are assigned to the video.
Exploring the Relationship Between Surface Features and Explaining...
Table 6 (continued)
Main category Subcategories Usage
28. Using experiments 1 CP is assigned to the video if physi-
cal experiments are covered in the videos.
Otherwise, 0 CPs are assigned to the video.
29. Connecting non-verbal elements 1 CP is assigned to the video (a) if the
video explicitly states how two non-verbal
elements are the same, or (b) if it alternates
between at least two non-verbal elements
to explain a content aspect. Otherwise, 0
CPs are assigned to the video.
30. Using writings 1 CP is assigned to the video if bul-
let points, sentences, formulas or formula
symbols are captured in written form.
Labels of illustrations are not included
here. Otherwise, 0 CPs are assigned to the
video.
31. Draw/amend figures 1 CP is assigned to the video if illus-
trations are drawn or modified in the
video to explain a content aspect.
Otherwise, 0 CPs are assigned to the
video.
Funding Open Access funding enabled and organized by Projekt DEAL.
Data Availability The data presented in this study are available on request from the corresponding author.
Declarations
Ethics Approval and Consent to Participate The study met the ethics requirements of the University of
Authors at the time the data was collected.
Competing Interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
licence, and indicate if changes were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.
0/.
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