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https://doi.org/10.1177/14697874211069524
Active Learning in Higher Education
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DOI: 10.1177/14697874211069524
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Fostering university students’
learning performance using the
one-take video approach
Julian Börger
University of Cologne, Germany
Markus Spilles
Bergische Universität Wuppertal, Germany
Johanna Krull
University of Cologne, Germany
Tobias Hagen
University of Cologne, Germany
Thomas Hennemann
University of Cologne, Germany
Abstract
Various studies have shown that video-based learning by explaining to a fictitious audience can be an effective
learning strategy for promoting multiple knowledge domains such as memory, comprehension and knowledge
transfer. However, field studies testing the effectiveness of this learning strategy in an applied setting are
rare. The present study examines the effectiveness of the one-take video (OTV) approach on undergraduate
students’ learning performance. The OTV method involves users recording short oral presentations without
any editing and with the support of handwritten visualisations (video-based learning by explaining). To test the
learning outcomes, 218 undergraduate teaching students for special educational needs were randomly assigned
to two test groups (OTV and explaining in writing). After that, they completed three study tasks throughout the
semester, each followed by immediate and delayed knowledge tests. The results for the OTV group show that
students achieved significantly better results in the immediate memory test the more handwritten visualisations
they used, but not the more often they repeated the video recordings. Analyses of variance revealed that the
OTV group outperformed the writing group in terms of memory performance in the immediate test but not in
the delayed posttest. The OTV group also significantly outperformed the writing group in both posttests in the
transfer domain. No significant differences were found in the comprehension test scores.
Keywords
field study, higher education, learning by explaining on video, one-take video approach
Corresponding author:
Julian Börger, University of Cologne, Klosterstraße 79c, Cologne 50931, Germany.
Email: julian.boerger@uni-koeln.de
1069524ALH0010.1177/14697874211069524Active Learning in Higher EducationBörger etal.
research-article2022
Article
2 Active Learning in Higher Education 00(0)
Introduction
An important task of educational institutions is to train students to become learning experts (Bjork
and Yan, 2014). According to Richey and Nokes-Malach (2015), learning experts can recall more
knowledge over a longer period (memory performance) and apply previously acquired knowledge
more flexibly to new areas (transfer performance) than learning novices. Although a variety of
effective learning strategies that can help learners become learning experts have been identified
(e.g. spacing: Cepeda et al., 2006; testing: Roediger and Karpicke, 2006; self-explaining: Wylie
and Chi, 2014), such methods are neither frequently used by students nor implemented by teachers
in higher education settings. For example, a survey by Hartwig and Dunlosky (2012) revealed that
a large number of students tend to use the learning strategy of cramming. Cramming often leads to
good results in exams but does not support long-term retention (Bjork and Yan, 2014). In addition,
Karpicke et al. (2009) found that college students prefer to reread content rather than recall knowl-
edge to learn, which is a more effective learning strategy for long-term retention (Roediger and
Karpicke, 2006). On the side of higher education institutions, still much teaching is covered by
lectures that evoke passive learning (e.g. listening to a lecturer; Roberts, 2019).
To support students in becoming learning experts, higher education teaching should provide
active learning methods that include effective learning strategies. Active learning should be made
visible through overtly active behaviours that are always related to the learning material and result
in a measurable output (Chi and Wylie, 2014). One such method may be the one-take video (OTV)
approach, which is evaluated in this study. This method aims to provide effective cognitive learn-
ing and the practical training of presentation skills simultaneously.
Besides, the approach can be implemented very easily as a teaching method in academic teach-
ing. This is important as a variety of barriers often impedes a sustainable adaptation of innovative
learning methods in higher education teaching. Implementation is often accompanied by a signifi-
cantly increased workload for academic staff (Gregory and Lodge, 2015). In addition, technologi-
cal requirements and financial resources are often lacking (Liu et al., 2020; Reid, 2014). These
barriers also seem to hinder the use of self-produced videos in academic teacher training (Christ
et al., 2017) even though it can be an effective instructional strategy (e.g. Hoogerheide et al., 2016
for effects on learning; Cavanagh et al., 2014 for effects on presentation skills). The OTV approach
as a video production technique avoids these implementation pitfalls (Börger, Spilles et al., 2020)
but its effectiveness for learning has not yet been sufficiently evaluated.
The OTV approach
The OTV approach involves learners recording short oral presentations on video using a smart-
phone or webcam. The content should be prepared and presented as if it was being taught to one’s
own peer group. Four rules must be followed.
Rule 1: Each video must be recorded without editing or interruption.
Rule 2: The person giving the lecture must be visible in the video.
Rule 3: The person giving the lecture must use handwritten notes or illustrations.
Rule 4: The video length should not exceed 5 minutes.
These rules result from the desire to combine practicability with empirically confirmed learning
mechanisms.
Börger etal. 3
Practicability
Recording a video in one take (rule 1) does not require any editing skills or programmes, which
reduces the requirements to accessing a smartphone or webcam. In the United Kingdom, 96% of
adults aged 16–24 own a smartphone (Ofcom, 2020). In the survey conducted by Börger, Spilles
and colleagues (2020), university students in Germany reported high accessibility to the OTV
approach. The absence of technical barriers indicates that the method does not require additional
financial resources.
Underlying learning mechanisms
Quality of learning increases the more actively learners engage with learning materials (Chi and
Wylie, 2014). Therefore, the OTV approach combines different learning mechanisms to encourage
learners to actively manipulate learning content.
Learning mechanism 1: Learning by explaining to fictitious others. Generating explanations can be an
effective learning strategy, be it through self-explanation (Wylie and Chi, 2014), peer tutoring
(Roscoe and Chi, 2008), or explaining to a fictitious audience (Hoogerheide et al., 2016; Hooger-
heide, Renkl et al.,2019; Jacob et al., 2020; Lachner et al., 2018).
According to the select/organise/integrate model of generative learning (Mayer, 2014), learning
by explaining requires three cognitive processes for all these cases. Learners have to select the
central information within the learning object, organise it into a mental model and integrate it into
knowledge from long-term memory to provide a coherent explanation. This initiates a continuous
internal review of the learning process with regard to the understanding, comprehension and coher-
ence of knowledge components (Fiorella and Mayer, 2016). While in peer tutoring tutors’ develop-
ment and validation of knowledge benefit from tutee questions (Roscoe and Chi, 2008), learning
by explaining to fictitious others can be supported by a teaching expectancy (Hoogerheide et al.,
2014, 2016).
Learning mechanism 2: Teaching expectancy. Roscoe and Chi (2008) suggest that learning by teach-
ing without adequate preparation of the content can lead to cognitive overload, which may result
in learners tending towards a knowledge-telling bias. This describes a superficial reproduction of
information without generative learning taking place (Roscoe and Chi, 2008). Therefore, rule 1
aims to encourage learners to prepare for the content broadly.
When expecting teaching performance, learners’ efforts to prepare content and understand a
subject can be promoted. The meta-analysis of 28 studies conducted by Kobayashi (2019) found
that the teaching expectancy groups outperformed the control groups, with small to medium effects
on surface and deep learning.
However, the long-term retrieval of knowledge seems to be promoted only when the content is
actively taught (Fiorella and Mayer, 2013, 2014; Hoogerheide et al., 2014). In this context,
Hoogerheide and colleagues (2016 and Hoogerheide, Renkl et al. 2019) found that teaching on
video increases arousal, thereby improving problem-solving skills.
Learning mechanism 3: Social presence and arousal in video production. Knowledge of a potential
audience can lead to a sense of increased social presence (Gunawardena, 1995). This stimulates the
adoption of the perspective of potential addressees, which in turn sets in motion cognitive and
motivational processes that improve one’s understanding and quality of preparation (Hoogerheide
et al., 2016). In this context, video production seems to be accompanied by a heightened state of
4 Active Learning in Higher Education 00(0)
arousal, which can support cognitive learning processes (e.g. the performance of working memory;
Arnsten, 2009) to a moderate extent (Hoogerheide et al., 2016; Hoogerheide, Renkl et al., 2019).
Therefore, rule 2 should help increase presenters’ feelings of social presence.
Learning mechanism 4: Practice and testing. Practice describes the repetitive learning of content
without the use of other strategies to support the learning process (Richey and Nokes-Malach,
2015). It can enhance comprehension (Anderson et al., 1997; Taatgen and Lee, 2003) and develop
connected knowledge (Anderson et al., 1997; Singley and Anderson, 1985). The more often repeti-
tion takes place in learning, the better the recall of the content from memory (Anderson and Leb-
iere, 1998). Rule 1 should encourage learners to restart their video recordings whenever they
discover errors within their presentation, fostering practice with each new video. At the same time,
the continuous recall of one’s own knowledge (testing effect) improves learners’ memory capacity
(Rowland, 2014). Koh et al. (2018) found that learning by teaching is most beneficial for fostering
comprehension when learners have to recall knowledge within teaching performance without
using notes.
Learning mechanism 5: Handwritten visualisations. Creating a visual representation of a text (learning
by drawing) can help learners foster their comprehension and transfer skills (Fiorella and Zhang,
2018). As with learning by explaining, it can be theoretically grounded in generative learning
(Mayer, 2014). To prepare coherent visualisations, learners need to select, organise and integrate
knowledge components. Drawings also allow for a continuous, self-regulated review of the steps
of the select/organise/integrate model. While creating the drawing, learners have the opportunity to
check it for coherence with the source text and adjust it as necessary (Van Meter and Firetto, 2013).
Fiorella and Kuhlmann (2020) found that drawings enhance the benefits of learning by teaching.
Their study showed that explaining in combination with using drawings outperforms the draw-
only and explain-only conditions in a retention, transfer and drawing questions test, with medium
to large effect sizes.
Rule 3 considers these findings. Although it is possible to create digital visualisations (e.g. on a
pad), the requirement explicitly demands handwritten visualisation, as handwriting seems to have
inherent cognitive processes that support learning further than digital notetaking (Mueller and
Oppenheimer, 2014).
Additional assumptions
Giving an oral presentation on video and reflecting on it subsequently can improve learners’ pres-
entation skills (rule 2; Börger, Krull et al., 2020; Cavanagh et al., 2014). The time limit (rule 4)
aims to help learners focus on the essential content of one topic when planning their presentation
(Koedinger et al., 2012).
The OTV approach as active learning
In their Interactive/Constructive/Active/Passive framework (ICAP), Chi and Wylie (2014) differ-
entiate three forms of active learning in ascending quality alongside passive learning: Active, con-
structive and interactive learning. Active learning can be described as an overtly motoric or physical
manipulation of learning material (e.g. using gestures while presenting). In constructive learning,
content is generated actively by the learner in addition to the source material (e.g. explanations or
visualisations). Interactive learning takes place when constructive learning is reviewed and vali-
dated together with a peer (e.g. peer-tutoring).
Börger etal. 5
Based on the previously presented learning mechanisms, the OTV approach can be defined as
an active learning method within this framework. While the verbal presentation of the content on
video achieves the quality level of active learning, the generation of explanations and visualisa-
tions can even be classified as constructive learning. While active learning should increase the
storage and retrieval of knowledge, constructive learning aims primarily at transferring knowledge
(Chi and Wylie, 2014).
The present study
While the effects of learning by explaining on video have so far been assessed almost exclusively
under laboratory conditions (Fiorella and Mayer, 2013, 2014; Hoogerheide et al., 2014, 2016, ;
Hoogerheide, Renkl et al., 2019; Lachner et al., 2018), field studies are lacking (with the exception
of Hoogerheide, Visee et al., 2019). Research on the OTV approach that has focused primarily on
the promotion of the presentation skills of undergraduate students (Börger, Krull et al., 2020)) and
teachers (McCammon and Parker, 2014a, 2014b) supports positive effects. Although an initial pilot
study of the promotion of memory performance (Börger, Spilles et al., 2020) did demonstrate sig-
nificant differences between the test groups (explaining in writing vs OTV vs OTV and feedback),
these differences no longer existed in a pairwise posthoc analysis. Furthermore, the results were
limited by the study design used, as only the results of the final exam were compared and no pretest
data were collected. For this reason, the aim of the present study is to further investigate the effec-
tiveness of the OTV approach as a variant of learning by explaining on video in an applied setting.
Two research questions are investigated.
Performance criteria (research question 1)
First, this study aims to examine which implementation factors most influence the effectiveness of
the OTV approach in terms of improving memory performance. Based on the research findings
above, in addition to prior knowledge, we expect both the number of video repetitions (learning
mechanism 4) and the handwritten visualisations used (learning mechanism 5) to affect memory
performance significantly. These two learning mechanisms are focused on because they can be
filled in by participants in the OTV approach. The generation of explanations (learning mechanism
1) and teaching expectancy (learning mechanism 2) are considered to be immanent to the method
for all participants and are therefore not investigated. Social presence and arousal (learning mecha-
nism 3) are not considered because of the high technical effort required to measure these variables
(e.g. using electrodermal activity wristbands: Hoogerheide, Renkl et al., 2019).
Learning outcomes (research question 2)
Second, the OTV approach is compared with explaining in writing (as a widely used assignment
format) in terms of fostering the ability to recall facts, comprehension and near declarative knowl-
edge transfer (Schunk, 2012). In this context, the study by Hoogerheide et al. (2016) supported that
learning by explaining on video (but not learning by explaining in writing) is more beneficial in
promoting comprehension than restudying. The direct comparison of the video and writing groups,
however, did not reveal any significant differences in the knowledge domains of comprehension
and transfer in direct and delayed testing. The participants in both groups only had 6 minutes to
construct their explanations and the video group did not use handwritten visualisations. In another
study, Lachner et al. (2018) found no differences in a direct test with regard to conceptual knowl-
edge between explaining in writing and explaining orally, while the transfer scores were signifi-
cantly higher with a medium effect in the oral explanation group. The authors attributed these
6 Active Learning in Higher Education 00(0)
differences to the fact that learners more often engage in elaborative processes (e.g. use examples
and analogies) when generating oral explanations, thus increasing the ability to transfer knowl-
edge. The time to prepare the content was 7 minutes, similar to the study by Hoogerheide et al.
(2016). Again, no handwritten visualisations were used in the oral explanation group. Additionally,
explaining orally seems to outperform a written explanation for fostering comprehension with a
small effect when the texts used are complex (Jacob et al., 2020). The authors did not include the
use of visualisations.
Based on these study results, we expect a stronger increase in memory performance, compre-
hension and knowledge transfer through the use of the OTV approach than when using a written
explanation because the former should have inherent active learning mechanisms that the latter
lacks (see Table 1). However, this assumption presupposes that the method is implemented in
accordance with didactic instructions.
Data and methodology
Participants and study design
Participants were 218 undergraduate teaching students for special educational needs at the
University of Cologne in Germany (sex: 11.93% male, 0.92% diverse; age: M = 22.00 years,
SD = 2.91; semester: M = 2.98, SD = 0.70, meaning that students have been studying their degree
programme for an average of 1.5 years). Participants who reported their sex as diverse did not clas-
sify themselves as either female or male. All students studied the same class in 2020 in eight dif-
ferent courses taught by four lecturers.
Participants were randomly assigned to one of two test conditions, regardless of course or instruc-
tor affiliation: OTV (n = 105) and writing (n = 113). As a result, each course contained a random
number of participants from both test conditions. The memory performance, knowledge transfer and
comprehension (see below) of these groups were compared in a pre/post-follow-up design.
Materials
Owing to the coronavirus pandemic, students received all texts digitally via the learning manage-
ment system of the university, and tests were conducted using LimeSurvey (Limesurvey GmbH).
Learning texts. The learning texts were scientific publications in the German language used in the
institute’s teacher education programme. All publications deal with specific scientific models from
the field of developmental psychology. Texts about scientific models were focused on because they
stimulate the generation of explanations (Richey and Nokes-Malach, 2015): (1) Kanfer’s (Kanfer
and Saslow, 1969) stimulus/organism/reaction/contingency/consequence (SORKC) model
(Tuschen-Caffier and von Gemmeren, 2009), (2) a transactional development (TD) model for dis-
social behaviour (Beelmann and Raabe, 2007) and (3) Lemerise and Arsenio’s (2000) integrated
Table 1. Learning mechanisms.
Learning mechanism OTV Written Expected impact of the OTV approach
Teaching expectancy ✓Comprehension, transfer
Social presence/arousal ✓Memory, transfer
Practice and testing ✓Memory
Handwritten visualisation ✓(✓)* Comprehension, transfer
*Visualisations can be used here as well.
Börger etal. 7
model of social information processing (SIP; Hennemann et al., 2016). This content was selected
because it was an integral part of the learning class.
Memory performance tests. Memory performance was assessed through eight open-ended ques-
tions focusing on facts about each model (example item: ‘Name and describe each component of
the SORKC model’). The correct answer received one point. Students could achieve a maximum
of 15 points (for SORKC), 8 points (for SIP) and 27 points (for TD). For each model, the test results
were divided by the potential maximum number of points (equal weighting) and the mean score
was calculated for the memory domain (range: 0–1).
Comprehension tests. To assess knowledge comprehension (ability of sense-making where learners
try to understand or reason; Koedinger et al., 2012), participants were asked to rate five content-
related statements about the TD model as true or false (example item: ‘If there are at least as many
protective factors as risk factors, negative development is impossible’). Students then explained their
choices. For the correct rating of the statement and correct explanation of their choice, students
received one point each (maximum of eight points). Test results were divided by eight (range: 0–1).
For content-specific reasons, a comprehension test was conducted only for the knowledge area of TD.
Transfer tests. To assess transfer knowledge (ability to apply knowledge in new contexts; Nokes-
Malach and Mestre, 2013), participants were asked to apply the SORKC model to explain internal-
ising problem behaviour (the self-learning task focused on explaining externalising problem
behaviour). Following Schunk (2012), this task can be described as near knowledge transfer, where
the original and transfer contexts are similar. Since this content context did not exist at the time of
the pretest, transfer knowledge could only be measured in the immediate and delayed tests. Describ-
ing the theoretical mechanisms of the models that could be logically linked to internalising prob-
lem behaviour received one point for each aspect. A maximum of five points could then be received.
The test results were divided by five (range: 0–1). The TD model cannot be used to elicit transfer
knowledge in this sense because it can only explicitly be applied to externalising problem behav-
iour. The questions on the SIP model were excluded because of validity limitations.
Social validity. All participants were asked to indicate how much mental effort (Paas et al., 2003)
and how much time they invested in answering each task. Mental effort was assessed on a five-
point Likert scale ranging from 1 (very low effort) to 5 (very high effort). In the analysis, the mean
scores across all the tasks were used.
Participants in the OTV group were asked to indicate the invested time for each video prepara-
tion (preparation time) as well as each video production (production time) on a six-point scale from
30 minutes to 3 hours. The means of preparation time and production time throughout all the videos
were summed up to show the time on the task. For the writing group, the time on the task was
assessed by a single item, with the mean score of all the tasks again calculated.
Additionally, participants rated how much fun they had using their learning method, as hedonic
motivation influences the actual use of technology (Venkatesh et al., 2012). Similar to mental
effort, fun was assessed on a five-point Likert scale from 1 (very little fun) to 5 (very much fun) for
each task and then averaged.
Implementation. To monitor the implementation of the OTV, participants in the OTV group were
asked to answer a checklist that included the following points after each self-study task: (1) How
often did you restart/have you repeated the video recording? (2) Did you use handwritten visualisa-
tion in your video? For repetition, the mean scores were calculated. For visualisations, a maximum
of three points were scored (one point for each video).
8 Active Learning in Higher Education 00(0)
Procedure
The semester and study were organised online because of the coronavirus pandemic. On the first day
of the online lectures, students received an e-mail with an invitation for the pretest on all three models
(SORKC, SKI, TD) that had to be completed within 1 week. In the second week, students received
their first self-study task (SORKC model) that had to be completed within 12 days. Subsequently, the
results were provided to lecturers. Participants in the OTV group were asked to produce their videos
according to the rules described above. Participants in the writing group produced a text of three to
four computer-written pages. All students were instructed to prepare the self-study task as if they
were explaining it to a fellow student. They were also told that the lecturers of their seminar would
review the written texts or videos to raise their feelings of social presence (Gunawardena, 1995) and
arousal (Hoogerheide et al., 2016). One day after handing in their self-study task, students completed
the posttest on the SORKC model. Only after that did lecturers thematise the model within their
courses. This procedure was implemented for all the self-study assignments.
The delayed test was equivalent to the pretest (except for additional questions on transfer
knowledge) and was conducted in the last week of lectures, 8 weeks after the deadline for the last
self-study assignment. After that, the test results were rated by four project staff members using an
answer sheet.
By completing the self-study tasks (SORKC, TD, SIP) as OTV or written text, students received
their credit points for the course. Participants who were assigned to the video group and did not
want to choose this option were able to change groups but were then not included in the analysis.
However, this only happened once in total. Furthermore, neither the video and text results, nor the
test scores did affect the awarding of the credit points. After completion of the study, students still
had several weeks to prepare for the written exam in the module.
Results
Preliminary analysis
To assess the interrater reliability among the evaluating project staff, 50 random test results for
each posttest (SORKC, TD, SIP) were rated by each rater. Results were then analysed using a two-
way mixed effect, absolute agreement, single rater measures intraclass correlation coefficient
(McGraw and Wong, 1996). Table 2 shows that the rater agreement averaged from good to excel-
lent. Therefore, individual ratings are included in the analysis.
Table 2. Intraclass coefficients.
Task Intraclass correlation (95% confidence interval) Interpretation (Koo and Li, 2016)
SORKC memory 0.90 (0.85–0.94)** Good to excellent
SORKC transfer 0.80 (0.71–0.87)** Good
TD memory 0.91 (0.87–0.95)** Good to excellent
TD comprehension 0.83 (0.75–0.89)** Good
SIP memory 0.90 (0.82–0.95)** Good to excellent
SIP transfer 0.81 (0.71–0.88)** Good
SORKC: stimulus/organism/reaction/contingency/consequence; TD: transactional development; SIP: social information
processing.
**p < 0.01
Börger etal. 9
Performance criteria (research question 1)
To analyse the performance criteria of the OTV method, the impacts of video repetitions (average
across all the tasks) and the use of visualisations (sum of all the tasks) on immediate memory per-
formance (average across all the tasks) were evaluated for the OTV group (n = 105). Prior memory
performance and preparation time (average across all the tasks) were also included in the model as
control variables. We did not control for variation between sex since the percentage of men in the
OTV group was low (11%). All variables were z-standardised.
The regression model (Table 3) shows a moderate (R2 = 0.16; adjusted R2 = 0.13) variance clari-
fication: F(4, 100) = 4.79, p < 0.01. Prior knowledge had a significant impact on immediate mem-
ory performance (slight effect), whereas preparation time had no significant influence. The impact
of video repetition was not significant, whereas the use of visualisations had a slight and significant
effect, meaning that the more often students used visualisations in their OTV, the better they per-
formed in the immediate memory tasks.
Learning outcomes (research question 2)
Building on the results of the first research question and because the OTV method specifies the use
of handwritten visualisations, 47 participants in the OTV group were excluded from the analyses
because they reported not using handwritten visualisations in all of their videos (implementation).
Another 16 participants in the writing condition and six in the OTV condition were identified as
outliers because of their pretest scores and were also excluded. Furthermore, four participants in
the writing condition and two participants in the OTV condition were excluded from the analyses
because they did not answer the delayed test.
The distributions of lecturers (χ2 = 2.11, p = 0.55) and sex (χ2 = 1.12, p = 0.57) of students did not
differ significantly between the test groups. Additionally, t-tests showed no differences in the age of
students (t(140) = 0.73, p = 0.27), number of semesters students studied (t(134.58) = 5.52, p = 0.07), or
pretest scores (memory: t(67.84) = 12.24, p = 0.12; comprehension: t(74,24) = 6.89, p = 0.38).
To test for differences in participants’ memory and comprehension performance develop-
ment, mixed ANOVAs were conducted using the within-subject factor time (pre vs immedi-
ate as well as pre vs delayed test scores) and the between-subject factor test group (OTV
approach vs writing). To answer the research questions, only the interaction effects
(time × group) were reported. Because no pretest on the transfer knowledge assessment was
conducted, two one-way ANOVAs were carried out for the immediate and delayed posttests.
Since there was a significant difference between the groups in terms of the time on the task
(see Table 5), this potential influencing factor was included as a covariate in all the analyses.
Table 4 shows the mean test scores and standard deviations for each knowledge component
for both test groups.
Table 3. Impact of video repetition and visualisation use on immediate memory performance (OTV
group).
βSE t p R2
Intercept 0.00 0.09 0.00 n.s.
Prior knowledge 0.20 0.09 2.12 <0.05 0.06
Preparation time 0.18 0.10 1.94 <0.10 0.11
Repetition 0.17 0.10 1.23 n.s. 0.12
Visualisation 0.22 0.10 2.31 <0.05 0.16
10 Active Learning in Higher Education 00(0)
For memory, the ANOVA showed a significant interaction between time and the test group in
favour of the OTV group with a small effect for pre versus immediate posttest scores: F(1,
139) = 4,62, p = 0.03, η2 = 0.03, but not for pre versus delayed test scores, F(1, 139) = 0.21, p = 0.65,
η2 = 0.00). In terms of comprehension, there was no significant interaction between time and the
test group for either period (pre vs immediate: F(1, 139) = 3,18, p = 0.08, η2 = 0.02; pre vs delayed:
F(1, 139) = 2.73, p = 0.10, η2 = 0.02). For transfer, the one-way ANOVAs revealed that the OTV
group outperformed the writing group with a small effect in the immediate test performance (F(1,
139) = 4.70, p = 0.032, η2 = 0.03) and a medium effect in the delayed test performance
(F(1,139) = 14.05, p < 0.01, η2 = 0.09).
Social validity
Table 5 shows means and standard deviations for perceived mental effort, fun and the time on the
task. The ANOVAs revealed no differences between the test groups in terms of perceived mental
effort (F(1, 140) = 0.00, p = 0.97, η2 = 0.00) and fun (F(1, 140) = 0.17, p = 0.69, η2 = 0.00), whereas
participants in the OTV group invested significantly more time working on tasks with a medium
effect (F(1, 140) = 18.49, p < 0.01, η2 = 0.12).
Implementation
Participants in the OTV group (n = 49) repeated the video recordings on average 2.5 times (M = 2.47,
SD = 1.43).
Discussion
This study investigated both, the factors influencing the learning effect through the OTV method
as well as the effectiveness of the OTV approach of video-based learning by explaining compared
with explaining in writing in terms of the knowledge components of memory, comprehension and
transfer in an applied setting.
Table 5. Means and standard deviations for social validity scores.
Group Mental effort Fun Time on the task
Writing (93) 3.19 (0.58) 2.82 (0.52) 2.17 (0.65)
OTV (49) 3.23 (0.57) 2.82 (0.67) 2.72 (0.86)
Scale ranges: Mental effort and Fun 1–5; Time on the task 0.5–3 hours.
Table 4. Means and standard deviations of the pretest and immediate and delayed posttest scores across
both groups.
Group Memory Comprehension Transfer
Pre Immediate Delayed Pre Immediate Delayed Immediate Delayed
Writing (93) 0.02 (0.04) 0.47 (0.18) 0.42 (0.22) 0.47 (0.18) 0.60 (0.18) 0.53 (0.16) 0.54 (0.38) 0.08 (0.24)
OTV (49) 0.03 (0.06) 0.56 (0.13) 0.44 (0.16) 0.43 (0.25) 0.62 (0.19) 0.55 (0.15) 0.70 (0.35) 0.28 (0.40)
Test score range for each task: 0.00–1.00.
Börger etal. 11
Performance criteria (research question 1)
The regression in the OTV group confirmed a significant albeit slight influence of the number of
handwritten visualisations used (as well as prior knowledge) on the memory performance scores.
This result is in line with Fiorella and Kuhlmann’s (2020) finding that using handwritten visuali-
sations affects knowledge retention even though their research question and study design dif-
fered from this one. It must be highlighted that only the number of handwritten visualisations
was used in the analysis. By contrast, there was no influence on the number of video repetitions,
perhaps because participants did not repeat the videos sufficiently (2.5 times on average) to
influence memory performance (Rowland, 2014). Furthermore, using handwritten visualisations
as well as other notetaking may also have weakened the testing effect of the video repetitions
(Koh et al., 2018).
Learning outcomes (research question 2)
Overall, both groups showed similar trends in test performance, insofar as performance in all
knowledge areas was the strongest in the direct test and decreased in the delayed test. In contrast
to our expectations and the findings of Jacob et al. (2020), no significant differences between the
test groups for the comprehension test scores were found. These results are surprising in that Jacob
et al. (2020) found that learning by explaining in the oral test group outperformed learning by
explaining in the writing test group without the use of handwritten visualisations, a learning mech-
anism included in the present study that is known to foster comprehension (Schmeck et al., 2014;
Van Meter and Garner, 2005). These unexpected results could be due to insufficient discrimination
between the items in the comprehension test because true/false questions can be answered cor-
rectly even without the corresponding knowledge, thereby jeopardising the reliability of the instru-
ment (Chandratilake et al., 2011). The comparatively high comprehension pretest results suggest a
bias.
For memory performance, the results show that participants in the OTV group benefited from
the direct test, whereas those in the writing group did not. This finding indicates that the OTV
approach is not more supportive of the long-term storage of factual knowledge than explaining in
writing. While the differences in favour of the OTV group in the immediate test could be attrib-
uted to the use of handwritten visualisations (Fiorella and Kuhlmann, 2020) and arousal (Arnsten,
2009) associated with video production (Hoogerheide et al., 2016; Hoogerheide, Renkl et al.,
2019), it remains unclear why the differences in memory performance dissipated in the delayed
comparison.
As expected, the OTV group significantly outperformed the writing group in both the immedi-
ate and the delayed transfer tests. Here, it should be emphasised that the difference between the
groups increases in the delayed test (a medium effect in favour of the OTV group compared with a
small effect in the direct test). The outcomes of the direct test support the findings of Lachner et al.
(2018). Hoogerheide et al. (2016) could not find significant differences between the video explana-
tion and writing test groups with regard to transfer in the direct and delayed knowledge tests, but
participants in the video group also did not use handwritten visualisations, as in the current study.
The fact that the use of handwritten visualisations and the learning by drawing implied in them
enhances the ability to transfer knowledge in the context of learning by explaining has been shown
by several studies (Fiorella and Kuhlmann, 2020; Leopold and Leutner, 2012; Van Meter and
Garner, 2005). However, it must be reemphasised that only near declarative knowledge transfer
(Schunk, 2012) was examined here. Transfer as a cognitive ability takes a variety of forms with
diverse cognitive complexities (Nokes-Malach and Mestre, 2013).
12 Active Learning in Higher Education 00(0)
Social validity
The social validity results indicate that the knowledge test differences between the groups were not
due to the varying mental efforts invested, which underlies OTV efficiency. By contrast, the OTV
group invested significantly more time completing the self-study task. Therefore, it cannot be dis-
missed that the group differences in favour of the OTV group are not only due to the learning
mechanism involved, but also due to the time invested.
Both groups rated their method similarly in terms of fun. While it is positive that the OTV
approach is no less fun than explaining in writing, the aim must be to increase enjoyment, as it is a
relevant factor for motivation (Deci and Ryan, 1985). However, this is a difficult task because
video self-confrontation, which participants find uncomfortable (Börger, Spilles et al., 2020) is an
integral part of the method.
Limitations
This study’s results are limited by the fact that the knowledge tests used have not yet been vali-
dated. This is particularly important for comprehension tests, as described above. The extent to
which the results are generalisable is also affected by the fact that the overwhelming majority of
the study population is female. As this was a field study, unknown potential confounding variables
may have influenced the test results. For example, it is not possible to determine whether partici-
pants completed the knowledge tests without the use of additional resources or completed them
conscientiously. Furthermore, the study did not allow us to predict which mechanisms of action of
the OTV approach specifically led to better results, as the approach combines several learning
strategies that have not been analysed in isolation. It also remains to be noted at this point that only
results regarding cognitive learning were collected. The influence of ethnic, cultural, environmen-
tal and economic factors has not been considered in this study.
Outlook
Overall, the results of this study support the effectiveness and practicability of the OTV method in
an academic context, if implemented as intended. The high number of participants excluded from
the study due to the lack of implementation fidelity in research question 2 shows that better guid-
ance may be needed. With regard to the use of handwritten visualisations, Fiorella and Zhang
(2018) called for further research on accompanying guidance in the creation of drawings, as it is
unclear which form of support (e.g. drawing training, partially provided illustrations) best pro-
motes learning.
To improve the method and its implementation, future research should consider a qualitative
analysis of videos. Since neither the quality of the explanations nor the quality of the handwritten
visualisations were assessed in the present study, no conclusions can be made about their influence.
This analysis requires a specification of the quality criteria.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publi-
cation of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Börger etal. 13
ORCID iD
Julian Börger https://orcid.org/0000-0002-4025-158X
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Author biographies
Julian Börger is a PhD candidate at the University of Cologne, Germany. His current research includes the
effects of one-take videos on learning and presentation skills and e-learning management systems for
teachers.
Markus Spilles is a research assistant at the University of Wuppertal, Germany. His current research includes
the effects of teacher feedback on social integration, peer tutoring and interventions with students with behav-
ioural problems.
Johanna Krull is a research assistant at the Department of Special Education and rehabilitation at the University
of Cologne, Germany. Her focus of research and work include inclusive school environments, multi-tiered
systems of support as well as social interaction processes in school classes.
Tobias Hagen is postdoc at the University of Cologne, Germany. His current research includes school absen-
teeism and the evaluation of Multi-tiered Systems of Support.
Thomas Hennemann is a full professor of special education at the University of Cologne, Germany. His cur-
rent research includes school climate, social integration research, teacher qualification schemes, prevention of
emotional behavioural disorders.
