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Effectiveness of PhET Simulations and YouTube Videos to Improve the

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Effectiveness of PhET Simulations and YouTube

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Rwandan Secondary Schools

Kizito Ndihokubwayo , Jean Uwamahoro & Irénée Ndayambaje

To cite this article: Kizito Ndihokubwayo , Jean Uwamahoro & Irénée Ndayambaje (2020)
Effectiveness of PhET Simulations and YouTube Videos to Improve the Learning of Optics
in Rwandan Secondary Schools, African Journal of Research in Mathematics, Science and
Technology Education, 24:2, 253-265, DOI: 10.1080/18117295.2020.1818042

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African Journal of Research in Mathematics, Science and Technology Education, 2020
Vol. 24, No. 2, 253–265, https://doi.org/10.1080/18117295.2020.1818042
© 2020 Southern African Association for Research in Mathematics, Science and Technology
Education (SAARMSTE)

Effectiveness of PhET Simulations and YouTube Videos to

Improve the Learning of Optics in Rwandan Secondary
Schools
a*
Kizito Ndihokubwayo , Jean Uwamahoroa, and Irénée Ndayambajeb

a
University of Rwanda College of Education (URCE), African Center of Excellence for Innovative
Teaching and Learning Mathematics and Science (ACEITLMS), Kayonza, Rwanda
b
Rwanda Education Board, Kigali, Rwanda
*Corresponding author. African Center of Excellence for Innovative Teaching and Learning
Mathematics and Science (ACEITLMS), University of Rwanda College of Education (URCE),
Kayonza, Rwanda. PO Box 55, Rwamagana
Email: ndihokubwayokizito@gmail.com

Effective teaching of physics requires the use of well-designed and diversified instructional tools such as
multimedia throughout the teaching and learning process. The main objective of this study was to
investigate the effectiveness of Physics Educational Technology (PhET) simulations and YouTube
videos to improve the learning of optics in Rwandan secondary schools. The study was framed by the
cognitive theory of multimedia learning. A total of 136 senior-4 physics students from six schools were
divided among three groups and taught with the usual teaching methods only, the usual teaching
methods supported by PhET simulations or YouTube videos, respectively. Student achievement was
measured by administering the Geometric Optics Conceptual Understanding Test to each group of
students before and after teaching. The groups of students who were taught using PhET simulations and
YouTube videos achieved significantly more gains on the posttest compared with the students who
experienced neither. PhET simulations and YouTube videos saw average normalised learning gains of
12 and 11%, respectively, while students who experienced solely the usual teaching methods got a gain
of only 2%. These results show that the use of PhET simulations without student manipulation (as
applied in this study) is equally effective as the use of YouTube videos. The researchers recommend that
teachers incorporate these instructional tools as a way of effectively teaching and learning optics.

Keywords: YouTube videos; PhET simulations; geometric optics; instructional tools; multimedia

Introduction

Physics has always been considered as an abstract discipline and isolated from learners’ everyday life
experiences (Ramma et al., 2018). Therefore, teachers need to introduce various engaging instruc-
tional tools to improve students’ understanding of such abstract physics topics. Teachers who want
to help students develop physics competencies may select technology as a tool to improve their
instruction and motivate their learners to grasp a fuller understanding of the concepts (Ndayambaje
& Ngendahayo, 2014; Stott & Case, 2014). For example, multimedia tools—categorised into audio,
visual and audio-visual—can be used to supplement verbal explanations of concepts in order to
clarify and concretise the lesson for students (Sorden, 2012). Despite these alternative approaches,
many teachers choose to teach the subject without the use of multimedia tools (Jones & Cuthrell,
2011; Sokoloff et al., 2007). Whereas a lecture alone leads to rote learning (Jones & Cuthrell,
2011), multimedia-based learning increases students’ participation and engagement, which can posi-
tively impact their learning and attitude (Mayer, 2014; Stott & Case, 2014). The concern about access
254 Kizito Ndihokubwayo et al.

to technological equipment is less of a concern in the Rwandan context because the Rwandan Ministry
of Education has a policy of distributing more than 100 positivo laptops to each school and gradually
providing internet access (Ministry of Education, 2016).
In the study by Mayer and Moreno (1998), it was found that during computer-based multimedia
lessons, students are more motivated to learn and understand difficult topics better. This finding
shows the potential for technology-related multimedia tools to support the usual and traditional teach-
ing approaches. Among the many possible multimedia instructional tools, YouTube videos and
Physics Educational Technology (PhET) interactive simulations are the focus of the present study.
Kunnath and Kriek (2018) have found that students performed well in the photoelectric effect when
exposed to computer-simulated instruction. PhET simulations help both teachers and learners to visu-
alise something that is otherwise not visible. For instance, a study by Perkins et al. (2006) showed that
PhET simulations were useful for helping students to see the motion of a string when learning the wave
concept. Mckagan et al. (2008) showed that PhET simulations affected the performance and attitude of
students after learning quantum mechanics. Computer simulations were also found to be appropriate
to enable students to understand the key concepts in electromagnetism (Kotoka & Kriek, 2014). Com-
puter simulations are tools enabling students to view science concepts in a virtual environment. They
show what would be similarly visualised in a conventional laboratory. Therefore a good simulation can
be effective at promoting a rapid understanding of difficult topics (Wieman et al., 2008). Simulations
have the potential to overcome a non-scientific conception and allow the development of explicit
and shared understanding (Wieman et al., 2010). Similarly, simulations replace experiments that
are hard to control, dangerous or impossible to conduct in laboratory settings—such as working
with nervous systems, lightning or dynamite (Ndihokubwayo, 2017), and exploring the force of
gravity or nuclear fission (Bell & Smetana, 2008). All of these concepts can be meaningfully explained
by simulating them on a computer (Bhukuvhani et al., 2010).
YouTube videos have improved the performance of students in a broad range of subjects com-
pared with teaching through lecture methods alone. For instance, YouTube videos were found to
be an innovative teaching tool to diminish the dullness of physics among learners and lead them
to view it as an interesting subject (Gustafsson, 2013). In a study of guided inquiry laboratory teach-
ing with embedded videos for learning optics and light, Afriani and Agustin (2019) found that students
were motivated to learn and achieved understanding after being exposed to instructional videos.
Additional to conceptual understanding, YouTube videos increase motivation for learning and
improve recall. From this point of view, teachers have started to adopt these instructional tools
(Lincoln, 2017), which provide them with more varied teaching strategies and content knowledge.
YouTube videos can be used in many forms. Teachers may use them as standalone course
content, as a supplement to other course activities (Jones & Cuthrell, 2011), as a support for home-
work to provide just-in-time teaching, or as a reference during research. The primary role of using a
YouTube video in teaching and learning is to direct students’ attention, initiate discussion and con-
textualise ideas. Such videos have been shown to impact students’ engagement and promote mean-
ingful learning (Bohloko et al., 2019).

Conceptual Framework
The present study builds on the cognitive theory of multimedia learning (Mayer & Moreno, 1998). This
theory proposes multimedia instructional practices as remedies for poor learning. It suggests that tea-
chers use cognitive practices and employ more effective cognitive strategies to help learners learn in
proficient ways (Sorden, 2012). In order to promote conceptual understanding, teachers should not
only rely on verbal explanation, but should also represent a concept using both verbal and pictorial
explanations (Mayer & Moreno, 1998): ‘Multimedia learning happens when we build mental represen-
tations from words and pictures’ (Sorden, 2012, p. 155). In such multimedia learning, there are three
main stages that a learner should follow to gain cognitive achievement. The first stage is the verbal and
visual information selection, the second is the creation and organisation of that information in the mind,
and the third is the connection of events and communication of that information via verbal or visual
modes (Mayer & Moreno, 1998; Sorden, 2012).
African Journal of Research in Mathematics, Science and Technology Education 255

A multimedia learning environment such as in the use of a moving picture has been predicted by
many researchers from the time of the famous inventor of the light bulb, Thomas Edison, to surpass
the traditional way of teaching, such as using a textbook (Mayer & Moreno, 1998). Multimedia con-
verts a source from a text in a textbook to an image, animation or video in a computer-based
environment (Mayer, 2014). In addition, during electronic tutoring (Stott & Case, 2014), electronic
tools have been viable for guiding conceptual change among students. Both conventional labora-
tory and virtual laboratories such as PhET simulations and YouTube educational videos support
cognitive features. Specifically, YouTube videos serve as free learning opportunities for learners,
possess the capability of visual representations and are accessible any time (Richtberg &
Girwidz, 2019).

Rationale of the Study

The literature has shown the potential of using simulations and videos to improve students’ learning of
various science concepts. However, little has been done on optics specifically. The present study
notes the gap in previous studies that have focused on the impact of the use of PhET simulations
or the use of YouTube videos, but very rarely looked at the impact of both tools for the same
context and for the same curriculum. Therefore, this study seeks to answer the following research
question: how effective are YouTube and PhET instructional tools for learning geometric optics
among Rwandan secondary schools?
We have focused on the achievement of conceptual understanding as a criterion for judging the
effectiveness of the use of the three teaching strategies. Therefore, we hypothesise that:
(1) There is no statistically significant difference in conceptual understanding of geometric optics
between students taught using YouTube videos and those taught using the teacher’s usual
teaching strategies.
(2) There is no statistically significant difference in conceptual understanding of geometric optics
between students taught using PhET simulations and those taught with the usual teaching
strategies.
The present study adds to the current literature about the effect of these instructional tools in learning
optics. Since there is a scarcity of fully equipped conventional science laboratories, the findings may
indicate effective alternative teaching strategies to deliver physics lessons on geometric optics.

Research Methodology

Sample and Sampling Methods

We selected our sample of six schools, i.e. two schools from each of three districts, one district from
Kigali city and two from the Eastern province of Rwanda. While the districts were selected randomly,
the schools were selected based on the inclusion of physics in their subject combinations. One class
from each participating school constituted our sample, that is, six classes of senior-four (S4) physics
students. Apart from their location (rural or urban), the selected schools have other characteristics
that differentiate them. Some are boarding while others are day schools. To minimise the effect of
these characteristics, we paired one day and one boarding school before randomly assigning each
pair of schools to an intervention. This matching made neary equal sizes of the three groups (n =
45, 45, and 46 students) as the size of the class in boarding schools was close to 30, while class
size in the day schools was close to 15 students.

Research Paradigm and Design

Our research employed a quantitative research paradigm and a quasi-experimental design where a
pretest–posttest for control and experimental group (Fraenkel et al., 2012) design helped us to
assess the students’ achievement after PhET simulation and YouTube video teaching interventions.
Each group received a different intervention over three months. In usual classes, teachers lectured
256 Kizito Ndihokubwayo et al.

using textbooks, blackboard and chalk. Some practical demonstrations were conducted using draw-
ings and real objects from the laboratory. Students listened to the teacher and were assigned to
small groups to discuss the observations. After the group work, students presented their findings
and opinions in plenary, where the teacher synthesised the findings and allowed the students to ask
questions. In addition to these practices in the usual classes, in PhET classes the teacher manipulated
PhET simulations using a projector. During the simulation, the teacher asked questions, and students
answered using what they already knew and what they saw on the screen. In addition to the practices
in usual classes, in YouTube classes the teacher showed YouTube videos using a projector or laptops
distributed to a small group of students. Similar to the strategy in the PhET group, in the YouTube group
the teacher stopped the video to ask some questions.
Since all three groups were taught the same content by different means, Table 1 shows the topics
covered by teachers in each group in line with the prescribed curriculum (Rwanda Education Board,
2015). While mirror concepts are taught in previous grades, the rest of the topics constitute the
content of Grade S4. Whereas we had enough YouTube content to cover all eight topics, we did
not find PhET simulations related specifically to defects and correction of lenses, application of
lenses or optical instruments. However, some PhET simulations are capable of covering a broad
range of concepts. For instance, the ‘geometric optics’ simulation explains any kind of image for-
mation as it has the option of changing diameter, refractive index and curvature of the radius of
the lens. Similarly, the ‘bending of light’ simulation explains the reflection and refraction phenomena,
and the manipulator is able to create any shape of glass such as cylinder, prism and other shapes.
This simulation also measures different variables, such as intensity, speed, time and angle. In
addition, the manipulator can adjust the medium, such as air, water and glass, by changing the
index of refraction.
Before implementing the YouTube and PhET simulation instructional tools, we trained participating
teachers for what they would use. It needs emphasising that, in this study, the teachers manipulated
these instructional tools rather than the learners.

Ethical Clearance
Our first steps in this study were to contact the district education office and school administration to
seek a research permit. We explained the purpose of the study to teachers and students, emphasised
that participation is voluntary and requested them to sign the informed consent forms. There was no
participation incentive apart from pens provided to students during the posttest.

Data Collection Tools

To measure student understanding, we designed and implemented the Geometric Optics Conceptual
Understanding Test (GOCUT). This 40-minute test contains 25 multiple-choice questions that reveal
the conceptual understanding of geometric optics at the level of S4 students who are taking a
physics course. The 25-item GOCUT was used to evaluate students’ achievement in the light unit in
S4 physics in the Rwandan physics curriculum. This test has been adapted from several studies
and inventories (Chu et al., 2009; Schlichting, 2006; Sokoloff, 2006), as well as Rwandan secondary
school physics textbooks. After reducing the initial pool of 70 potential items to 44 by evaluating each
question’s content alignment, piloting was done with 46 students to evaluate face validity and reliability;
we achieved a medium Pearson product–moment correlation coefficient over time of r = 0.69. After
removing questions that were confusing, too easy and too challenging, 25 items remained in the
final version of the instrument (see the sample questions in Box 1). For the whole GOCUT test of all
25 items, please see the supplementary materials. This test evaluates a range of topics related to geo-
metric optics, including laws of reflection and refraction, mirrors and lenses, and optical instruments.
Among the 25 questions, 24 are of a multiple-choice format, while one is a performance prompt that
requires drawing. All multiple-choice questions have four answer choices, with one correct answer
and three distractors.
Table 1. Rwanda physics curriculum content delivered by the instructional interventions

African Journal of Research in Mathematics, Science and Technology Education

Serial
number Topics included Usual teaching methods YouTube videos PhET simulations
0 Mirrors Revision by chalk–chalkboard, group work Concave and Convex Mirrors by Bending-light_en (https://phet.
and discussion Manocha Academy at https://youtu.be/ colorado.edu/en/simulation/
oDNqfxRYQY0 legacy/bending-light)
Applications of Mirrors in Daily Life (F4 C5 Color-vision_en (https://phet.
L109 V22) by Lyco Physics at https:// colorado.edu/en/simulation/
youtu.be/2fqFAQwpjYg legacy/color-vision)
1 Characteristics and types Textbooks, chalk–chalkboard, group work Convex and concave Lenses – Physics – Geometric-optics_en (https://phet.
of lenses and presentation, experiment on types of Eureka.in by Maria Teixeira at https:// colorado.edu/en/simulation/
lenses by drawing a lens on white paper youtu.be/4zuB_dSJn1Y legacy/geometric-optics)
2 Refraction of light and ray Textbooks, chalk–chalkboard, experiments Geometric Optics – Crash Course Bending-light_en (https://phet.
drawing on the characteristics of the image formed Physics #38 by CrashCourse at https:// colorado.edu/en/simulation/
by a lens and the critical angle of a glass youtu.be/Oh4m8Ees-3Q legacy/bending-light)
Geometric-optics_en (https://phet.
colorado.edu/en/simulation/
legacy/geometric-optics)
3 Focal length and lens Textbooks, chalk–chalkboard, word problem Geometric Optics – Crash Course Geometric-optics_en (https://phet.
formulae solving, experiment to determine the focal Physics #38 by CrashCourse at https:// colorado.edu/en/simulation/
length youtu.be/Oh4m8Ees-3Q legacy/geometric-optics)
4 Defects and correction of Textbooks, chalk–chalkboard, group work Why is my image upside down? By —
lenses and presentation Astronomy and Nature TV at https://
youtu.be/VtAwdO0Dhnc
5 Prism and total internal Textbooks, chalk–chalkboard and Total Internal Reflection Demo- Optical Bending-light_en (https://phet.
reflection conventional laboratory Fibers by Physics Demos at https:// colorado.edu/en/simulation/
youtu.be/Lic3Gcs_bKo legacy/bending-light)
6 Application of combined Textbooks, chalk–chalkboard, group work Applications of Lenses – in Optical —
lenses and presentation Devices (F4 C5 L101 V14) by Lyco
Physics at https://youtu.be/
Cn6uDeh0Sto
7 Optical instruments Textbooks, chalk–chalkboard, group work How does a camera work? By Branch —
(microscope, telescope, and presentation, word problem solving Education at https://youtu.be/
and camera) B7Dopv6kzJA
Working of Compound Microscope
(animation) by Rupesh Singh at https://
youtu.be/cmzWDkOYTjM
Refracting vs. Reflecting Telescopes by
Randy Dobson at https://youtu.be/
HAW_6ukFO9w

257
258 Kizito Ndihokubwayo et al.

Box 1. Sample questions in GOCUT

1. The figure below shows the observers A and B looking into the object O. What is the correct situation?

A B

O
X Y
A. Y is correct because bundles of light are coming from the observer B’s eyes and so he/she is able to
observe the object O.
B. X is correct because the beam of light is shown emanating from the object O and being
received by the eye of the observer A.
C. Both X and Y are correct because light rays are from any side of the object and observers.
D. None of them are correct.
2. The prism is able to split a beam of white light into a spectrum of seven visible light colours (red, orange,
yellow, green, blue, indigo and violet). What light colour is bent the most?
A. The violet light colour.
B. The green light colour.
C. The yellow light colour.
D. The red light colour.
3. Why is the image upside down in optical instruments? This is because of the lenses used in these optical
instruments have a:
A. Spherical surface, hence making light propagating in straight lines cross each other.
B. Spherical surface, hence making rays of light propagate in a parallel direction.
C. Flat surface, hence making rays of light propagate in a parallel direction.
D. Flat surface, hence making rays of light propagate in a perpendicular direction.

Data Analysis
In the present study, we did not focus on issues or pitfalls in answering each item. Instead, we have
focused on the overall students’ achievement after the three interventions. We have excluded the stu-
dents who missed either pre- or posttest from the analysis. We analysed the data from GOCUT scores
using descriptive statistics (minimum, maximum, mean and standard deviations) and inferential stat-
istics (t-test for means, Cohen’s D effect size, average normalised learning gains and p-values)
using Microsoft Excel and Statistical Package for Social Sciences. We compared the means of normal-
ised learning gains of the given interventions. According to Hake (1998), this is calculated by taking the
ratio of mean difference of post- and pretest over the mean difference of the highest expected score
(100%) and pretest. We marked each item on the GOCUT by awarding one point when the student
had successfully chosen the correct answer (or correctly drew on the drawing question). Thus, the
maximum possible score is 25.

Findings

The overall comparison results among the groups are presented in Table 2. It shows that all interven-
tions improved student achievement, but usual teaching improved achievement only marginally, i.e.
from a mean score of 8.42 (33.69%) to 8.73 (34.93%). In contrast, the PhET and YouTube interven-
tions provided more considerable improvement in the student achievement of a mean score of 9.09
(36.36%) to 10.93 (43.73%) and from 9.20 (36.78%) to 11.17 (43.83%), respectively. In line with the
cognitive theory of multimedia learning, the two interventions added value to the existing usual teach-
ing. It is striking that the range of student achievement following the usual teaching and PhET approach
African Journal of Research in Mathematics, Science and Technology Education 259

Table 2. Overall student performance for the three intervention groups

Student performance
Average number of correct
s/n Intervention N Test answers (25 questions) % STD Minimum Maximum
1 PhET 45 Pre 9.09 36.36 13.12 3 15
Post 10.93 43.73 11.03 5 17
2 YouTube 46 Pre 9.20 36.78 10.03 3 15
Post 11.17 43.83 11.34 4 17
3 Usual teaching 45 Pre 8.42 33.69 10.93 3 14
methods Post 8.73 34.93 8.19 5 13

decreased with smaller standard deviations in the posttest. In contrast, the range widened for YouTube
intervention. While the highest standard deviation was 13.12 at the pretest (found in the PhET group),
the lowest standard deviation was 8.19 at the posttest (found in the usual teaching group). The lowest
score was 3 out of 25 at the pretest for each group. The highest score earned at the posttest was 13 out
of 25 from the usual teaching group, but the maximum score was 17 out of 25 in both the PhET and
YouTube groups.
Figure 1 shows the trending mean scores and normalised learning gains for each group from pre-
and posttest according to intervention given. A mean difference of 7.38, 7.04, and 1.24 was obtained
for PhET Simulations, YouTube videos and usual teaching, respectively. Those differences equate to
learning gains in geometric optics of 12, 11 and 2% for students in the PhET simulation, YouTube video
and usual teaching classes, respectively.
In order to determine the significance of the impact of each intervention, Table 3 shows Cohen’s D
effect size, average normalised gains (<g>), t-test, p-value and the level of statistical significance for
the treatment effect. According to Cohen (1988), when D is below 0.2, we should conclude that the
effect size is small, when it is above 0.5, we should conclude that it is medium, and when it is
above 0.8, we should conclude that the effect size is large. The groups of students taught using
PhET simulations and YouTube videos achieved a highly statistically significant difference from pre-
to posttest. This difference was not observed for the group taught using the usual teaching strategies.
In other words, the PhET and YouTube interventions showed higher learning gains compared with the

Figure 1. Teaching interventions and outcome results

 260
Table 3. Statistical significance of differences before and after the interventions

Intervention Pretest Posttest Average STD D <g> t-Test (d.f.) p-Value Significant? Level
PhET simulations 36.36 43.73 12.07 0.61 0.11 0.000 (44) <0.001 Yes ***
YouTube Videos 36.78 43.83 10.68 0.65 0.11 0.000 (45) <0.001 Yes ***
Usual Teaching 33.69 34.93 9.56 0.13 0.01 0.23 (44) >0.05 No —

Hypothesis Usual/PhET Usual/YouTube Average STD D <g> t-Test (d.f.) p-Value Significant? Level
PhET simulations vs. usual teaching methods 34.31/40.04 9.19 0.62 0.08 0.002 (88) <0.01 Yes **
YouTube videos vs. usual teaching 34.31/40.30 8.11 0.73 0.09 0.000 (89) <0.001 Yes ***
Statistically significant difference: —, no significance; * significance; ** high significance; *** very high significance.

Kizito Ndihokubwayo et al.

African Journal of Research in Mathematics, Science and Technology Education 261

Figure 2. Student performance in pre- and posttest across all groups

usual teaching group. This effect size was medium (>0.5 and <0.8) in YouTube and PhET groups (0.65
and 0.61, respectively).
We used the t-test for two samples to address our hypotheses. We assumed equal variances for
PhET simulations compared with usual teaching and unequal variances for YouTube videos compared
with usual teaching. We found that the group of students taught using PhET simulations obtained a
highly statistically significant difference (p < 0.01) and an effect size of 0.62 compared with those
being taught using usual teaching strategies. Similarly, the group of students taught using YouTube
videos obtained a very highly statistically significant difference (p < 0.001) and an effect size of 0.73
compared with those being taught using usual teaching strategies. In other words, the PhET and
YouTube interventions have increased student learning gains in learning geometric optics, indicating
that teachers are able to teach with these tools effectively (see Table 3).
We also visualised the shift in results from pretest to posttest in a histogram (see Figure 2). It shows
that students generally have low performance on the test, as no teaching group reached a mean score
of 50% in any condition. However, the score distribution fits a normal distribution. The number of stu-
dents from the usual teaching group decreases while the number of students in the PhET and YouTube
groups increases. Only a few individual students (four students (8.88%) and two students (4.44%) in
pre- and posttest) from the usual teaching groups scored above 50% (range 50–59% score). In other
words, a small number of students succeeded in this test. Substantially more students earned above
50% in each of the other treatment conditions, which shows that more students succeeded with the
new teaching tools.

Discussion of Findings

PhET simulations and YouTube videos were shown in the present study to be far more effective in
improving the conceptual understanding of geometric optics than usual teaching. Therefore, we
reject the null hypotheses, which states that there would be no statistically significant difference
between groups of students taught using PhET simulations or YouTube videos vs. those using the
usual teaching strategies. YouTube videos and PhET simulation instructional tools received a
medium effect size while usual methods received a small effect size. Consecutively, students taught
using YouTube videos and PhET simulation instructional tools achieved a higher normalised learning
gain than students taught using usual methods. Several previous studies have found the positive
impact of PhET simulations (Bell & Smetana, 2008; Naranjo & Pardo, 2011; Wieman et al., 2008)
on students’ conceptual understanding. Other studies have reported the benefits of the use of
YouTube videos (Afriani & Agustin, 2019; Jones & Cuthrell, 2011; Lincoln, 2017) for improving
262 Kizito Ndihokubwayo et al.

performance in physics. Our study is novel in that the effect of both teaching strategies can be com-
pared as the context and curriculum content are identical. It shows that the use of YouTube videos
and PhET simulations has an equally positive impact on student performance.
Routine laboratory demonstrations were conducted in all groups, including the group experiencing
the usual classes. The findings show that the usual class teaching resulted in improvement but not sig-
nificantly so. This suggests that routine laboratory demonstrations contribute little to the improved per-
formance in science in contrast to findings reported in Afriani and Agustin (2019) and Volkwyn et al.
(2008). Our study did not compare the impact of the use of small group experimental work with the
use of YouTube videos and PhET simulations. However, the literature (Ndihokubwayo, 2017)
shows that many Rwandan teachers are reluctant to use laboratory- or inquiry-based teaching. The
current study shows that investment in teacher training in using PhET and YouTube videos could
have a significant impact on student performance and may be cheaper than boosting resources and
training for increased laboratory work. Researching the learning effects of an experiment on optical
aberration, Naranjo and Pardo (2011) found a statistically significant difference between three
groups that were exposed to different kinds of experimentations. They found that the learning achieve-
ment gains with the use of hyper-realistic simulations were higher than those using the traditional sche-
matic PhET simulation or the traditional laboratory experiments. This suggests that the distinct positive
impact of the use of the traditional schematic PhET simulations, as in this study, may be strengthened if
teachers use hyper-realistic simulations.
Although the cognitive theory of multimedia learning requires active participation in processing the
given tools and information (Richtberg & Girwidz, 2019), in the PhET groups teachers manipulated
the simulation software. Research (Perkins et al., 2016) provides evidence that student manipulation
provides an even greater impact on their meaning making. Since the core objective of using simu-
lations is to engage students in active learning, Perkins et al. (2016) show that learners benefit from
simulations when they are asked to make predictions, work directly with the computers, engage in
sense-making, formulate their questions and build relationships among learned concepts. Thus,
with students handling the PhET, the impact on performance is likely to be even greater.

Conclusion, Recommendations, and Further Research

In this study, we tested the impact of YouTube videos and PhET simulations as instructional tools for
improving conceptual understanding of geometric optics. We found a statistically significant difference
for both PhET simulations and YouTube videos when compared with usual teaching. Therefore, we
conclude that PhET simulation and YouTube video instructional tools are highly beneficial for learning
physics in general, and optics in particular, for Rwandan secondary schools.
The cognitive theory of multimedia learning does not solely stop at student achievement but also
includes active participation. Bryan and Slough (2009) identified two essential steps for the effective
use of PhET simulations. The first is a demonstration of all simulation features by the teacher. The
second is the allocation of time for students to play around with the simulation. Therefore, teachers
should encourage learners to handle the computer, manipulate the simulations by themselves and
compare the observations with their predictions (Ndihokubwayo et al., 2020) recorded before simulations.
On the side of YouTube videos, teachers should critically search the platform with the right keyword,
and they should watch several videos before choosing one that is related to their lesson content. Stu-
dents should work to incorporate their explanations into their broader understanding in order to link
scientific theory with their observations. Teachers should let students, when online, select and
observe their own searched videos, as long as the teacher confirms the theme of the concept under
learning. This freedom will accelerate the students’ collaboration during peer discussion as all students
did not watch similar videos. However, when offline, the teacher should ask students to pause the video
and have time for reflection on predicted ideas and new observations, or even ask for clarification.
Further research should explore how students respond to the geometric optics test (GOCUT) or dif-
ficulties faced with the instrument, elaborate challenges in the use of these instructional tools for stu-
dents and reveal perceptions of teachers on why they do not exhaust the laboratory resources.
African Journal of Research in Mathematics, Science and Technology Education 263

Acknowledgements

The authors wish to thank the owners of YouTube channels we visited for their voluntary uploading of
the instructional videos for education purposes. We also thank the University of Colorado Boulder staff,
who developed and made free to use the PhET interactive simulations that we used in this study. The
authors acknowledge the members of the Center of Teaching Excellence and the Center for STEM
Learning at the University of Kansas for their research expertise provided to the first author during
his student exchange programme in September 2019. We especially value the proofreading of the
manuscript by Mr Michael Ralph and the guidelines on research design provided by Professor Fred
Lubben. The authors acknowledge the financial support from the African Centre of Excellence for Inno-
vative Teaching and Learning Mathematics and Science provided to carry out this research.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by African Centre of Excellence for Innovative Teaching and Learning Math-
ematics and Science (ACEITLMS)

ORCID

Kizito Ndihokubwayo http://orcid.org/0000-0002-2566-8045

Supplementary Material

Supplemental data for this article can be accessed at https://doi.org/10.1080/18117295.2020.1818042.

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