=Paper=
{{Paper
|id=Vol-3024/paper3
|storemode=property
|title=Smart gamification: Exploring the application of gamification in smart learning environments
|pdfUrl=https://ceur-ws.org/Vol-3024/paper3.pdf
|volume=Vol-3024
|authors=Alejandro Ortega-Arranz,Eduardo Gómez-Sánchez,Miguel L. Bote-Lorenzo,Sergio Serrano-Iglesias,Juan I. Asensio-Pérez,Alejandra Martínez-Monés
}}
==Smart gamification: Exploring the application of gamification in smart learning environments==
https://ceur-ws.org/Vol-3024/paper3.pdf
Smart gamification: Exploring the application of
gamification in smart learning environments
Alejandro Ortega-Arranz, Eduardo Gómez-Sánchez, Miguel L. Bote-Lorenzo,
Sergio Serrano-Iglesias, Juan I. Asensio-Pérez and Alejandra Martínez-Monés
GSIC-EMIC Research Group, Universidad de Valladolid, Valladolid, Spain
Abstract
Smart Learning Environments are conceived as environments able to understand the student needs and
context, and to propose adapted informal learning activities that might involve physical and virtual
elements. However, SLEs can potentially fail in engaging students to perform such generated tasks as they
are not part of the learning design and they might not be assessed by the teacher. Therefore, considering
the positive effects observed in other educational environments, gamification is proposed to increase
the student engagement and participation in such informal activities. Nevertheless, the gamification of
activities that are not part of the learning design and which are generated on-the-fly becomes a difficult
task as the gamification design needs to be created on the run without the intervention of the teacher. To
make this process meaningful, the cornerstone is the adequate use of Learning Analytics and Learning
Design information. This work-in-progress paper introduces a technological architecture supporting
this type of gamification, and Smart GamiTool, a prototype of a smart learning environment supporting
the orchestration and enactment of smart gamification.
Keywords
Smart Gamification, Smart Learning Environment, Learning Analytics, GamiTool, Teachers
1. Introduction
Smart Learning Environments (SLEs) are educational environments able to understand the
student needs and context, and to automatically adapt their learning experience [1, 2]. For
instance, Hwang [3] proposes the use of SLEs to generate automatic informal tasks (e.g., the
creation of a concept map), associated to real entities near the position of the learners (e.g., trees),
and which are related with the contents learned in the formal context (e.g., school). Therefore, in
this example, the course content (i.e., theory about trees) and the students’ physical location are
the input variables that personalize the suggestion of informal tasks. However, SLEs involving
physical and virtual spaces can potentially fail in engaging students to perform such informal
tasks as they require physical activity, are not part of the formal learning design and they
will not be assessed by the teacher [4]. Given this situation, gamification is proposed to help
overcome such issue (i.e., to increase student engagement) based on the results observed in
other educational and non-educational environments across physical and virtual spaces [5].
LAS4SLE @ EC-TEL 2021: Learning Analytics for Smart Learning Environments, September 21, 2021, Bolzano, Italy
" alex@gsic.uva.es (A. Ortega-Arranz)
0000-0002-8167-7157 (A. Ortega-Arranz)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Gamification is usually defined as the use of game design elements (e.g., rewards) and struc-
tures (e.g., stages with increasing difficulty) in non-game contexts (e.g., education) [6]. Neverthe-
less, it seems difficult to gamify activities that are not part of the learning design as configured
by the teacher, and which are generated on-the-fly by SLEs. Considering the previous example,
many gamification design questions arise such as how to automatically gamify a task that is
not known beforehand? how to promote the visits to unknown entities with gamification? or, how
many points should be given for successfully performing such task? Therefore, we propose to
denote as Smart Gamification those gamifications that need to understand the context (e.g.,
informal tasks proposed, desirable student actions within the informal tasks) to automatically
create “meaningful gamification designs” on-the-fly able to promote the purposes for which the
gamification is used (e.g., increase student engagement). To this end, Learning Analytics (LA)
play a fundamental role in smart gamifications, providing gamification systems with information
to understand the context, and therefore, as an additional input variable to create such mean-
ingful designs [7]. Accordingly, SLEs and gamification systems need to define pre-established
contracts considering the supported computer-interpretable LA information. Therefore, the
underlying RQ guiding this work is How can topic-independent smart learning environments be
gamified using LA?
To the best of our knowledge, there exist gamification systems able to adapt different game
elements according to the learners’ engagement and preferences [8]. However, the gamified
activities are designed by the teachers and/or the researchers (i.e., known beforehand), and
although the game elements are different for the different student profiles, the gamified activities
are similar to all students. Additionally, there exist several SLEs with gamification capabilities
(e.g., programming [9], e-health [10], mathematics [11]). Nevertheless, these SLEs are topic-
specific, and have the gamification rules hard-coded, thus skipping the on-the-fly gamification
of SLE-generated activities. The work-in-progress presented in this paper describes a scenario
highlighting the usefulness of the proposal (Section 2); presents a technological architecture
that supports the gamification of SLEs using LA (Section 3); and, outlines a set of conclusions
and ideas for future research (Section 4).
2. Smart Gamification: A Scenario
Taking back the scenario given by Hwang about SLEs for learning about trees [3], in this section
we describe how students might react to the gamification of such scenario:
Rose and Anna are two secondary school students using a SLE that proposes informal
activities related to nearby physical entities that have a connection with the contents taught
in the classroom (e.g., trees, historical buildings). In particular, this month, Rose and Anna
learn about trees, including the different types of trees according to their leaves (i.e., evergreen
and deciduous trees). Informal activities (e.g., creating a concept map, answering a quiz or
watching a video related to the nearby entity) are generated weekly by the SLE considering the
student knowledge about the topic (obtained through the results of the weekly quizzes) and
their physical position. In this case, the difficulty of the physically-located informal activities is
proportional to the student knowledge level on this topic.
In order to complete the informal activities, students have to get physically close to the
proposed entities and complete the associated task, as suggested by the SLE. Although the
activities are visible in the SLE app from any student location, they are only unlocked once
students are close to the entities. In case the student location is out of the boundaries of any
related entity, the SLE will propose the completion of the tasks without the need of being close
to any entity1 .
This week, Rose is struggling with the features of deciduous trees, and was suggested with
an informal activity involving 5 different trees near her location. At the beginning, Rose is not
engaged to perform the proposed informal activity because they will not be assessed by the
teacher. Nevertheless, she likes the possibility of earning rewards, including being ranked in a
leaderboard with other friends, customizing her avatar in the SLE and earning virtual cash that
can be redeemed for course privileges (e.g., deadline extensions, unlocking contents). Therefore,
motivated by the possibility of earning such rewards, she decides to visit the physically-located
entities and complete the proposed tasks. Additionally,since students can earn +20 redeemable
points when performing the activities with other students, Rose called and convinced her friend
Anna to do the activities together. At this point, for each reached entity they will earn 50
redeemable points, additional to the extra points earned by successfully completing each of the
associated tasks (e.g., successfully answering quizzes, watching 100% of videos). At the end of
the activity, Rose and Anna have a total number of 500 redeemable points that they could use
to buy a new avatar and an additional attempt for the next course quiz.
In this scenario, we have visualized how the gamification might affect the behavior of certain
students (e.g., fostering students to get close to the entities and to complete the informal task
with other students). However, which LA were needed to automatically perform the gamification
of such activities during course run-time? The next section presents an eventual architecture
that would support the implementation of this type of gamification.
3. Smart Gamification: Components and LA
In order to understand the data exchange needed for smart gamifications, we have synthesized
SLEs and gamification systems into their main components. On the one hand, SLEs are usually
based on three components [4] (see white boxes in Fig. 1): (i) Sense: collects information about
students’ context (answers in weekly quizzes); (ii) Analyze: generates higher-level indicators and
profiles learners based on such indicators (determines the level of knowledge for the different topics);
and (iii) React: provides personalized tasks based on students’ profile (physically-located quizzes,
concept maps and videos). On the other hand, gamification systems can be also decomposed
into three similar components (see gray boxes in Fig. 1). Borrowing the names from SLEs: (a)
Sense: monitors student actions within gamified activities (answers to physically-located quizzes,
concept maps and videos); (b) Analyze: compares student actions with predefined gamification
conditions (100% score in quizzes, 100% video visualization); and (c) React: issues the rewards to
those students satisfying the gamification conditions (virtual cash).
The main feature of smart gamification derives from the fact that gamification conditions
and rewards are generated by the system, while in traditional gamifications, teachers or pro-
1
It should be noted that these rules are not part of the learning design. Instead, they constitute the “smartness”
of the SLE and therefore, other rules could be applied.
(6) Monitored gamified actions
(2) Gamification preferences
(1) LD information
GAMIFICATION MANAGER
SENSE SENSE
Teacher Teacher
SLE MANAGER
(5) Gamification generation
and adaptation
ANALYZE ANALYZE
REACT REACT
(4) Information about
informal tasks
(3) Informal tasks
generated on-the-fly
(7) Rewards and other game elements
Figure 1: High-level architecture and data-flow for smart gamifications.
grammers are responsible of configuring the actions to be monitored, their thresholds (i.e.,
gamification conditions), and the reactions (i.e., gamification rewards). In Figure 1, the Gam-
ification Manager will be the architectural element in charge of reading the different input
variables that automatically will create and update the gamification design. Therefore, smart
gamifications should at least contain the aforementioned functionality (e.g., sense, analyze,
react) either in an integrated environment where the “smart” and “gamification” components
are shared for both purposes (integrated gamified SLE) or in decoupled systems with different
components (SLE + gamification system).
In the scenario before, the teacher of the course configured the formal learning design of the
course, including the quizzes and their topics, that later were used by the SLE to understand
the knowledge level of the students (see (1) in Fig.1). Additionally, the teacher expressed in
the gamification system that he wants to foster collaboration between students with the smart
gamification (2). During course enactment, Anna and Rose were monitored and evaluated by the
SLE-Sense and the SLE-Analyze components respectively to understand their level of knowledge
regarding the different topics of the course. As a basic example, this level of knowledge could
be interpreted considering the average score obtained in the same-topic quizzes during the last
week (low ≤ 40% < medium ≤ 80% < high).
Since Rose obtained a score lower than 40% for the deciduous trees topic, she was provided
with informal activities as considered by the SLE-React component (3): complete quizzes, concept
maps and videos being next to 5 deciduous trees near her location. At this moment, the proposal
of informal activities needs to be reported to the Gamification Manager in order to understand
which actions are susceptible to be gamified (4). This interaction should include:
1. Any SLE configuration that may be useful to tune the gamification design. In the previous
scenario, this information could regard the maximum number of informal activities that
can be suggested to one student in such week (i.e., for low knowledge students, 5 activities).
This information grants that all the students will have access to the same rewards although
the conditions to earn them may vary according to their level of knowledge.
2. The number and type of generated tasks per student (in the scenario before, Rose had 5
physically-located activities: 2 answers to questionnaires, 2 video watches, and 1 concept
map creation). This information enables the system to know the gamification conditions
that can be applied to the proposed informal activities. Thus, apart from actions common
to all types of activities (e.g., open the activity, complete the activity, complete the activity
before a specific date), the gamification system can personalize the gamification according
to the activity types (e.g., achieving high score for quiz-based activities, watching more
than 50% of a video).
3. The location of the informal activities. This information allows the gamification system
know where the gamification system must monitor the student actions. It should include
the tool where the reaction is performed (e.g., H5P), the identifier of the resource (e.g.,
video ID: 345) and the credentials to query their analytics (e.g., teachers’ token).
Every time that the gamification system receives new information from the SLE, the gam-
ification manager should create or update the gamification design for the informal activities
triggered. As an example applicable to the previous scenario, and considering the gamification
preferences of the teacher, the gamification system will issue as maximum as 600 redeemable
points (RPs). In the case of Rose (i.e., a student with 5 triggered activities), she will get a maxi-
mum number of 100 RPs per activity (+20 RPs if the activity is performed together with another
student). Getting into more detail, 50% of points will be issued when reaching the physical
entity and the remaining 50% if the student successfully completes the different activities (i.e.,
100% score in quizzes, 100% video visualization, concept map with at least 10 elements) (5)2 .
Once the gamification design is created (or updated), this information is transferred to the
other gamification components to: start monitoring the triggered activities (6); analyze if the
conditions are satisfied; and, reward the students (7). In the case of Rose, she got 500 RPs as
the sum of 3 activities completed successfully: (100+20) * 3 = 360 RPs, and 2 activities that were
visited but unsuccessfully completed: (50+20) * 2 = 140 RPs.
In order to demonstrate the feasibility of this proposal, we have developed a prototype of
an integrated gamified SLE: Smart GamiTool (see Figure 2). Smart GamiTool is based on the
original gamification tool GamiTool3 , incorporating basic smart functionality as described in the
scenario, proposing personalized reactions that involve physically-located entities (annotated
in Open Street Map4 ), videos from YouTube5 , multiple-choice questions from Casual Learn6 and
course privileges (e.g., unlocking content). At design time, Smart GamiTool enables teachers to
configure certain gamification features (e.g., game elements) that will be later considered as
input variables for the creation of the gamification design during course enactment.
2
It should be noted that as different rules could be applied to obtain the knowledge level of the students and to
trigger the reactions, different rules could be also applied to generate and update the gamification designs.
3
GamiTool: https://www.gsic.uva.es/gamitool/, last access: September, 2021.
4
Open Street Map: https://www.openstreetmap.org/, last access: September, 2021.
5
YouTube: https://www.youtube.com/, last access: September, 2021.
6
Casual Learn: https://casuallearnapp.gsic.uva.es/, last access: September, 2021.
Figure 2: Screenshots of Smart GamiTool in Canvas LMS: (a) student interface with gamified and
physically-located activities about deciduous trees, and (b) student interface with the same activities in
the virtual space (students without nearby entities).
4. Discussion and Next Steps
This paper proposes the use of multiple data (e.g., teacher inputs, LA generated by SLEs) to
inform the design of smart gamifications that can be generated in smart learning environments
involving physical and virtual spaces. Our initial hypothesis is that smart gamifications can
foster the participation and completion of informal activities generated by SLEs without the need
of teachers or programmers configuring the on-the-fly gamification designs. Thus, we presented
a high-level architecture and its data flow to support this type of gamification. Additionally,
we started the development of a system implementing such architecture and data flow: Smart
GamiTool.
In the short term, we aim to finish the development of a functional prototype, and evaluate the
generated gamification designs for different inputs (e.g. activity types, topics, game elements).
Then, we plan to use the prototype in real scenarios with secondary school students from where
we could get an idea of the temporal and cognitive implications for teachers, and its effectiveness
to foster student participation in informal activities generated by the integrated SLE. In the
medium term, we will consider the integration of more complex rules to increase the “smartness”
of the integrated SLE, including the gamification manager. This could involve the integration of
Smart GamiTool with external SLEs such as SCARLETT [12], and the consideration of other
variables affecting to the design of effective gamifications such as the student preferences [8].
It seems interesting to highlight that the addition of gamification strategies in smart environ-
ments might brings some pedagogical concerns that will need attention. For instance, there
could be students failing on purpose the activities configured as part of the formal design to
trigger easier informal activities (proposed by the SLE). Therefore, students could get easily
more rewards than those students being proposed with more difficult activities. This would be
another interesting point to explore in the foreseen empirical studies.
Acknowledgments
This research was funded by the Spanish State Research Agency (AEI) and the European Regional
Development Fund (TIN2017-85179-C3-2-R, PID2020-112584RB-C32); and by the European
Regional Development Fund and the Regional Government of Castilla y León (VA257P18).
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