International Journal of Engineering & Technology 1
A Framework of Drone-based Learning (Dronagogy) for Higher
Education in the Fourth Industrial Revolution
Helmi Norman 1*, Norazah Nordin 1, Mohamed Amin Embi 1, Hafiz Zaini 1, Mohamed Ally 2
1 Faculty of Education, Universiti Kebangsaan Malaysia Bangi, Selangor, Malaysia
2 Center for Distance Education, Athabasca University, Alberta, Canada
*Corresponding author E-mail: firstname.lastname@example.org
The fourth industrial revolution is impacting the world in three megatrends which include physical, digital, and biological trends. Drone
technology is gaining more interest from all sectors including the education sector. Drones is one of the technologies in the physical world
that has the potential for redesigning education in the fourth industrial revolution. Yet, as the technology is newly made for the public, its’
affordances in educational environments are still not fully understood. Hence, the study investigates dronagogy for higher education and
develops a framework for dronagogy a learning strategy. The study applies a case study using small autonomous drone integration in using
problem-based learning and MOOCs using the pedagogy-space-technology framework. Learning analytics are used for assessment of
learning in terms of active learning time while dronagogy was applied as learning tasks. The findings revealed that that dronagogy could
be used as a learning strategy in different learning contexts and dronagogy could be used to guide integration of drone-based learning in
higher educational settings for the fourth industrial revolution.
Keywords: Drone-based learning, fourth industrial revolution, pedagogy-space-technology framework, 4IR learning strategy, higher education
In the fourth industrial revolution (4IR), the blurring of physical,
digital, and biological worlds is affecting the educational landscape.
Technological advancements in 4IR such as drones in the physical
world, Internet-of-Things (IoT) in the digital world, and synthetic
biology in biological world is offering educational affordances that
have been never possible . As learners today are digital natives,
blending teaching and learning with technology is important to
engage them in learning. Yet, merely using technology without
well-designed pedagogy may lead to disruption of learning rather
than engagement . Design of the “right” blended between
pedagogy, space and technology is crucial is ensuring both
instructors and learners are empowered during teaching and learning
One of the emerging technologies of 4IR is drones. Drones could be
considered relatively new technologies as they have used in the past
for military purposes. The emerging aspect of drones are they are
available in the current public market, as drones’ usage have shifted
from military purposes (e.g. for intelligence) to agricultural,
passenger and delivery drones . For agriculture, drones have
been utilized to monitor tree plantations. In a study by , drones
were used to gain information on geometric features of agricultural
trees for optimization of crop management operations. The drones
assisted farmers in terms of three-dimensional (3-D) features such
as canopy area, tree height and crown volume that were important
information for plantation status. With regards to delivery drones,
Dubai created a “buzz” by the launching of the world’s first “drone
taxi” for passenger transport.  reported that the drone can
autonomously take passengers and transport two passengers other
locations via use of mobile apps [2, 13]. As for delivery drones,
several companies such as Amazon are using drones for delivery
services. In late 2016, Amazon launched the “Prime Air” service
that offers transportation of small goods and products via drones
within a maximum air time of 30-minutes (Amazon, 2017). This
spurred a discussion of on customer-drone relationship in which
service-delivery drones with regards to consumer-brand
relationships were studied .
Albeit emerging usage of drones in various sectors, the usage of
drones in education is still new. Previous studies have shown
educational affordances of drones in fields of geology journalism
education , model-based learning , and environmental
chemistry . In environmental chemistry,  used drones for
environmental sampling experiments. The drones were used to find
suitable sampling sites in which they could collect samples for their
experiments. In addition, drones assisted students in risk assessment
– whether the sites where suitable for land exploration and the
degree of safety at the potential sampling site. In model-based
learning,  modeled activities and features of the drones to teach
about situational analysis, in which students analyze situations and
scenarios (in this case, setting up and flying the drones) and map
them to produce mental models. In relation,  describe the
potential of drones to be applied in geology and journalism
education. The former explained that drones could be potentially
aerial surveys, field mapping, and monitoring (i.e. dangerous or
hard-to-reach locations such as volcanoes and overhanging rocks
outcrops). The latter highlighted that drones could be integrated in
journalism as newsgathering tools.
International Journal of Engineering & Technology
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Despite all these potential and educational affordances, there are
still limited frameworks and models to guide integration of drone-
based learning in higher educational settings . Previous research
related to framework or models of drones include studies by ,
 and . In the study of , a framework for collaborative
learning was produced by using drones as the subject matter. Here,
students were required to design and manufacture drones, and this
assisted them in production of drone conceptions. As for  study,
a project-based learning toolkit was developed for automation and
robotics engineering, where a series of activities were designed in
development of an aerial robotic system (i.e. drone). With regards
to  study, they studied on frameworks for internal and external
auditing in which they proposed a framework for prototype
inventory counts. Although these studies proposed drone-based
frameworks, the studies used drones as educational outputs rather
than offering frameworks that assist educationists in designing
learning environments by integration of drones. Moreover, there are
also limited frameworks that link drones to the aspects of pedagogy,
space, and technology. As such, in resolving the issues and filling
in the gaps, this study investigates the educational affordance of
drones (i.e. consumer quadcopters) and develops a framework of
drone-based learning for 4IR higher education. The study also links
the framework with the design of pedagogy, space and technology
2. The Dronagogy Framework
The proposed framework for dronagogy is adapted from the works
of  and .  proposed a pedagogy-space-technology
framework for design and evaluation of learning places. In the
framework, all three aspects (pedagogy, space and technology)
influenced each other in a reciprocal manner, in which an intended
pedagogy could influence arrangement of space, while a space could
equally influence what people do in it and influence teaching and
learning patterns. Similarly, a learning space could influence
opportunities and constraints on a type of technology, while a
particular technology could influence how a learning space is
utilized by educators and learners. Thus, the study adapts the
pedagogy-space-framework and links the framework for framing
drone-based learning. The proposed framework for drone-based
learning in 4IR Education is illustrated in Figure 1.
Fig. 1: Framework of drone-based learning for 4IR higher education
By definition, drones are unmanned aerial devices (UAVs) and are
aircrafts that are controlled by human pilots which are not onboard.
Drones range from quadcopters, helicopter drones, RTF drones,
delivery drones, photography drones and racing drones [5, 9]. This
study focuses on small autonomous drones, specifically,
quadcopters (or rotorcrafts) that are available in the market for the
public. In development of the framework, drone-based learning is
linked to the perspectives of pedagogy, space, and technology by
. In a review on small autonomous drones,  explains that
drones have three-levels of autonomy, which are sensory-motor
autonomy, reactive autonomy, and cognitive autonomy. Autonomy
of drones can be related to robot autonomy, where autonomy is
based on their abilities to carry out tasks without human
interventions based on aspects such as current state and sensing. At
first level autonomy (sensory-motor autonomy), drones can perform
high-level human commands (e.g. move to a global positioning
system or fly at a given altitude). At the next level autonomy
(reactive autonomy), drones are capable of avoiding obstacles, take
off, land, coordinate with other moving objects, and maintain a
predefined distance from the ground. In the highest autonomy level
(cognitive autonomy), drones can carry out simultaneous
localization and mapping, recognize objects and humans, plan and
learn . Based on the three levels of drone control autonomy, the
educational affordances of small autonomous drones can be
categorized as follows: (i) active tracking-based video shooting and
monitoring; (iii) gesture-based video shooting and monitoring; and
(iii) controller-based video shooting and monitoring.
Active tracking-based video shooting is related to video shooting
that is performed by the drone on an intended fixated object or area.
This is performed by using geolocations and video imagery tracking
. Using the active tracking feature, drones video shoot on a
fixated target and follow the movement of the target without the
interventions of humans using controllers. For instance, drones can
be used to video shoot a student conducting fieldwork without the
student having to control the drone. Gesture-based video shooting
involves human operators using hand gestures to command and
control drones as well as give directions of movements. This is done
via machine vision techniques using locally on-board video cameras
on drones. When a hand gesture indicating an intended direction of
drones are given, the drone estimates the angle and distance by the
estimated hand direction and face score system . Controller-
based video shooting is typically type of video shooting that can be
performed by drones. The controller is usually connected via radio
or Wi-Fi signals and in some cases connected to mobile phone or
tablet PCs for visualization of during video shooting.
The space aspect is defined by  as physical learning spaces or
places. In relation,  elaborated on the physical learning space
concept, explaining that learning spaces are on the continuum of two
ends of unstructured and structured physical learning spaces.
Structured physical learning spaces are spaces that are designed for
teaching and learning, such as collaborative teaching and learning
spaces. Unstructured physical learning spaces are informal social
learning spaces such as “eddy spaces” which are small spaces for
learning [24, 21]. This can be further extended to virtual learning
spaces, where they can also be categorized as structured and
unstructured virtual learning spaces. Here, the structured virtual
learning spaces refer to formal virtual learning environments such
as massive open online courses (MOOCs) or learning management
systems while unstructured ones refer to informal virtual learning
environments such as social media and .
With regards to drones and learning spaces, drones offer educational
affordances in both physical and virtual learning spaces. From
physical learning spaces, drones can be designed to be used for
structured and unstructured learning environments. In structured
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learning spaces, drones could be used for outdoor lab experiments
and fieldwork. As for unstructured ones, drone features such as
active tracking-based video shooting could be used in recording
group discussions in indoor or outdoor learning . With regards to
structured and unstructured virtual spaces, video shots by drones
could be shared in formal and informal spaces such MOOCs and
In terms of the pedagogical aspect, drones could be used with
application of various learning theories and learning strategies. As
drones offer interesting educational affordances that could be
utilized in different learning contexts, appropriate teaching and
learning strategies and theories have to be selected in order to
maximize the potential of drones.  explains that drones have
three-levels of autonomy, which are sensory-motor autonomy,
reactive autonomy, and cognitive autonomy. Here, depending on the
learning aims, an educator would have to first understand the
educational affordance of a drone type (e.g. small autonomous
drone) according to the levels of autonomy. This would enable
educators to design their pedagogy to suit the educational affordance
of drones or utilize drones to suit their pedagogy.
The case study was conducted in an educational technology course
at Universiti Kebangsaan Malaysia in a period of five months, from
February to June 2018. The course is a postgraduate course that
provided exposure on instructional design as well as learning
material and task development for blended learning. The course was
conducted in blended learning format. The platform used for the
virtual learning space was a MOOC on the openlearning.com
platform. The MOOC was a self-paced MOOC opened to the public,
in other words, anyone, not necessarily a student could enroll in the
course. The total number of students currently enrolled for the
course is over 650 students.
The pedagogical design applied in the case study was problem-based
learning as discussed by . They posit that problem-based
learning is defined by three main aspects, which are: the problem,
the work process, and the solution. The problem is related to any
issues or problems that were intended to be solved while the work
process involves processes that were carried out to solve the
problem. The solution is the solution designed and developed based
on the work process that was conducted. As discussed by ,
defining the three aspects are important in problem-based learning
– as to who defines the problem, who organizes and controls the
work process, and who owns the solution – between both the
educator and learner. In the case study, an overall task was given to
learners, where the students were assigned to produce videos related
to a given theme. The problem and the work process were own by
learners in which learners were responsible to define their research
problems, project management and teamwork processes.
Meanwhile, the solution was co-owned between learners and
educators. With regards to the problem, the educator provided a
general rubric for video components, and a general task was given,
which was to create a video on the theme of “awareness on the future
learning”. The learners were empowered in finding their own topic
and research problem that was related to the theme. As for the work
process, learners were given total autonomy over management of
their learning. Drones were introduced to learners as a potential
learning tool and features that included active tracking-based video
shooting and monitoring, gesture-based video shooting and
monitoring, as well as controller-based video shooting and
monitoring. For the solution, learners were required to produce a
video that solved the problem identified with the use of drones. The
space aspect in this study was the course MOOC as the virtual
learning space and physical learning space that included computer
labs and fieldwork sites involving drones. The technology aspect
integrated were drones, specifically, small autonomous drones,
where the drones allowed for active tracking-based video shooting,
gesture-based video shooting, and controlled-based video shooting.
The brand used of drone used was the DJI Spark that is a mini drone
with a mechanical gimbal and camera allowing for intelligent flight
The pedagogical design was applied in three phases, which are the
problem phase, work phase, and the solution phase. IIn the problem
phase, learners defined their own research problem of their tasks
based on the research theme “awareness on the future of learning”
(pedagogical aspect). Materials were gathered by using document
analysis, in which documents relating to problems related to the
community were collected from sources such as newspapers, journal
articles, community-based websites and social media sites. Here, the
aim was to elicit a real-world problem based on what is happening
in the community. This was done via the online collaborative mind-
mapping where learners conducted brainstorming over the internet
in real-time by using online maps . The mind-maps produced
were shared in the virtual learning space, which was the course
MOOC (space aspect). An example of an online collaborative mind-
map produced by a group of learners in depicted in Figure 2. In this
phase, learners also familiarized themselves with drones, in terms of
management, safety issues, flight control features, and video
shooting techniques (technological aspect). This was important as
most of the learners were not familiar with handling and
management of drones.
Fig. 2: An example of an online collaborative mind-map created
by a group of learners in the course MOOC
With regards to the work phase, learners used drones to create their
learning products (technological aspect). Based on the research
problems elicited in the previous phase, the learners develop
solutions by producing learning products (pedagogical aspect).
Based on the problems, learners used the educational affordances of
drones which were active tracking-based video shooting, gesture-
based video shooting, and controlled-based video shooting. The
video shots were conducted in communities based on their research
problems (space aspect). With active tracking-based video shooting,
learners utilized the features in scenarios that required video
shooting on moving objects or focus areas. This feature allowing
continuous video shooting on an intended focus areas/object by
tracking its’ movements, as in Figure 4 and Figure 5. As the study
used the DJI Spark drone, four types of video shots were available
in the active tracking-based video shooting mode, which were: (i)
ascend drones with camera pointing downwards; (ii) fly backwards
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and upwards with drones locked on an intended focus area; (iii)
circle around an intended focus area; and (iv) flying upward with
drones circling around an intended focus area . Meanwhile,
gesture-based shooting was used for video-shooting without remote
controls and controller-based video shooting was used for aerial
shots that required high elevation levels.
Fig. 4: A mobile phone connected to the drone remote for viewing
and monitoring of the video shots captured by the drone
Fig. 5: An example of active tracking-based video shooting
conducted by a group of learners using drones and the different
types of video shots afforded by the drone
In the final phase (solution), learners performed video-editing and
shared their learning products (i.e. videos) on the course MOOC
(space aspect), as shown in Figure 6. The videos were developed in
solving research problems that were identified in the problem phase
(pedagogical aspect). Learners then peer-reviewed their work and
suggested feedback on refinements. These feedbacks were then
implemented to enhance the learning products.
Fig. 6: Learning products (videos) produced by learners using
drones shared on the course MOOC
Results and Discussion
The results are discussed in terms of the learning analytics with
regards to: (i) learner geographical locations; (ii) total active
students over time; and (iii) three-dimensional data of total active
time, total comments and total progress. The analytics were
generated by the MOOC and the Tableau Public software.
Learner Geographical Locations
The learner analytics showed the learner geographical locations are
shown in cartogram in Figure 7. The learners currently come from
30 countries which covers continents including North America,
South America, Europe, Africa, Asia and Australia.
Fig. 7: A cartogram of learner geographical locations
Total Active Students Over Time
The total active students over time were also assessed via learning
analytics, which is displayed in Figure 8. Analytics from February
to June 2018 showed that there were two highest peak of active
learning time, which were 87 active students (April) and 65 active
students (June). These peaks were caused by the discussion of
dronagogy learning activities that was conducted. In the first high
peak, the learners discussed on drone video shooting activities,
where in the second peak, learners discussed about editing and
production of the drone video shots.
Fig. 8: A mobile phone connected to the drone remote for viewing
and monitoring of the video shots captured by the drone
Number of Comments and Likes Over Time
Learning analytics were also assessed with regards to number of
comments and likes over time (as in Figure 9 where the red line
represents number of likes while the blue one represents like over
time). Similar to the total active time diagram in Figure 8, there were
also two peaks for number of comment and also likes from February
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to June 2018. The analytics indicated that the two highest peak of
likes (red line) were 720 likes and 893 likes, while the highest peak
of comments were 203 comments and 237 comments. Interestingly,
this was inverse with the total active time, where the highest active
time (first peak) had a lower number of likes and comments while
the second highest active time (second peak) had a higher number
of likes and comments. This signifies that learners were more active
in communicating on the subject of drone video editing and
production rather than drone video shooting activities.
Fig. 9: Number of comments and likes over time
Three-Dimensional Data of Total Active Time, Total Comments,
and Total Progress
Three-dimensional data among total active time, total comments,
and total progress were also accessed, as shown in Figure 10. The
darker colors in the cartogram represents the higher progress over
time with highest number of comments, where the highest (in three-
dimensional data) was 1 day 16 hours with 100 percent progress and
80 total comments.
Fig. 10: Three-dimensional data of total active time, total comments
and total progress
4. Conclusion and Future Directions
The study has proposed a framework for drone-based learning for
higher education in the fourth industrial revolution, which consisted
of three main aspects of pedagogy, space and technology. A case
study was also discussed in applying the framework in a learning
situation, where small autonomous drones’ educational affordances
were integrated with regards to the technological aspect, while
problem-based learning, MOOCs and outdoor physical learning
spaces where used in terms of the pedagogical and space aspects.
The study also learning analytics with regards to: learner
geographical locations, total active students over time, and three-
dimensional data of total active time, total comments and total
progress. The findings indicated that learners were more active in
communication on the subject of drone video editing and production
rather than drone video shooting activities.
The limitations and future directions of the study are as follows.
First, with regards to drones, the study used small autonomous
drones for learning. Utilization of other types of drones, such as
helicopter drones, RTF drones, delivery drones, photography drones
and racing drones, could offer different educational affordances.
Second, the study was conducted with participants who were
postgraduates taking an educational technology course. Using
undergraduates and applying it to a different field other than social
science, for example engineering, could yield in different findings.
Third, with regards to the pedagogical aspect, problem-based
learning was integrated as the teaching and learning strategy.
Application of other learning strategies such as heutagogy or
challenge-based learning could be more suitable depending on the
learning contexts and could yield in other interesting educational
affordances of drones [27, 28, 29]. Finally, MOOCs were used as
virtual learning spaces for project discussion and management of
learning products. It would be interesting to investigate how other
learning environments such as mobile learning and ubiquitous
learning environments combined with other 4IR technologies such
as mobile augmented reality and interaction analysis tools such as
social network analysis could be used in drone-based learning [14,
22]. In sum, it is hoped that the study could be used for educators
and researchers interested in the field of drone-based learning.
The authors acknowledge the Ministry of Higher Education
Malaysia under the Fundamental Research Grant Scheme (FRGS)
grant number FRGS/1/2016/SSI09/UKM/02/2 and Universiti
Kebangsaan Malaysia under Dana Penyelidikan Strategik grant
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