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Frontiers in Education 01 frontiersin.org
Using Minecraft to cultivate
student interest in STEM
ChristineLourrineS.Tablatin
1,2*, JonathanD.L.Casano
1 and
MariaMercedesT.Rodrigo
1
1 Ateneo Laboratory for the Learning Sciences, Department of Information Systems and Computer
Science, Ateneo de Manila University, Quezon City, Philippines, 2 Information Technology Department,
Pangasinan State University, Urdaneta City, Philippines
Due to the popularity and flexibility of Minecraft, educators have used this game
to develop instructional materials and activities to cultivate student interests in
science, technology, engineering and mathematics (STEM). One example of such
an initiative is the What-If Hypothetical Implementations in Minecraft (WHIMC)
project of the University of Illinois Urbana Champaign. The study reported in
this paper describes a WHIMC deployment in the Philippines and the eects
this deployment had on student STEM interest. The study used quantitative and
qualitative methods to determine the eect of WHIMC on the STEM interest
of Filipino students. We performed quantitative analysis of the pre- and post-
STEM Interest Questionnaire (SIQ) ratings and Game Experience Questionnaire
(GEQ) ratings of the high- and low-performers to determine the eect of using
WHIMC in the students’ STEM interest and the dierence between the game
experience of high- and low-performers, respectively. Qualitative analysis of the
answers to the open-ended questions about the attributes of the module was
also conducted to determine the relationship between the module attributes
and student performance. The analysis of the aggregated SIQ ratings before
and after using the WHIMC-based modules revealed only a minimal eect on
the STEM interests of the students. However, there was a significant eect in the
Choice Actions construct, which implies that students recognize the importance
of studying hard if they want to pursue STEM-related careers. Further, the
analysis of the overall GEQ of high-performers and low-performers also revealed
no significant dierence. Although no significant dierence was observed in
the overall GEQ, high-performers had significantly higher GEQ ratings in the
Immersion dimension. This result suggested that high-performers had a more
positive, engaging, and enjoyable learning experience. Moreover, the findings
on the favorite module attributes suggested that students perform better in the
out-of-game assessments when they like all the module attributes. This implies
that students must beengaged in the game and learning task aside from being
interested in the learning topic to have better assessment scores. The study also
showed that open-ended learning environments coupled with tasks that demand
exploration, observation, and higher-ordered thinking are demanding even on
high-performers.
KEYWORDS
Minecraft, WHIMC world, STEM interest, digital game-based learning, educational
games
OPEN ACCESS
EDITED BY
Wang-Kin Chiu,
The Hong Kong Polytechnic University,
China
REVIEWED BY
Vanessa Camilleri,
University of Malta,
Malta
Mawardi – Mawardi,
Padang State University,
Indonesia
*CORRESPONDENCE
Christine Lourrine S. Tablatin
tablatinchristine@gmail.com
SPECIALTY SECTION
This article was submitted to
STEM Education,
a section of the journal
Frontiers in Education
RECEIVED 20 December 2022
ACCEPTED 27 February 2023
PUBLISHED
CITATION
Tablatin CLS, Casano JDL and
Rodrigo MMT (2023) Using Minecraft to
cultivate student interest in STEM.
Front. Educ. 8:1127984.
doi: 10.3389/feduc.2023.1127984
COPYRIGHT
© 2023 Tablatin, Casano and Rodrigo. This is
an open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic practice.
No use, distribution or reproduction is
permitted which does not comply with these
terms.
TYPE Original Research
PUBLISHED
DOI 10.3389/feduc.2023.1127984
16 March 2023
16 March 2023
Tablatin et al. 10.3389/feduc.2023.1127984
Frontiers in Education 02 frontiersin.org
1. Introduction
Learners oen nd STEM dicult because it requires complex
thinking, repeated practice, and self-discipline (Bertozzi, 2014).
According to the PISA National Report on the Philippines, compared
to the OECD average of 489in Math and 489in Science, Filipino
students scored a low 353 and 357, respectively. Only 1 out of 5
attained the minimum prociency level in math (Education GPS,
OECD, February 2023). ese results are corroborated by students’
performance in the National Achievement Test, where only 25%
demonstrated mastery levels in math and only 5% of test takers
demonstrated mastery levels in science. us, addressing STEM
interest and achievement in the Philippines is an acute need.
Improving students’ self-ecacy through learning experiences is
essential to cultivating students’ interest and enthusiasm in STEM
careers (Mohtar etal., 2019). One of the innovative ways to provide an
engaging learning environment that keeps students interested and
enthusiastic about STEM subjects is the use of games in learning.
Digital game-based learning (DGBL) has become a growing
educational trend in the classroom as an engaging teaching approach
for improving student motivation and learning (Ennis, 2018; Leong
etal., 2018; Hussein etal., 2019; Shang etal., 2019). Games provide
more amusement, enjoyment, and aesthetic appeal (Alawajee, 2021).
ey can also encourage the player to learn, oer multisensory
environments, and improve the capacity of a player to think and create
meaning (Iliya and Jabbar, 2015). e use of digital games can help
students gain a more concrete understanding of abstract, theoretical
topics while interacting with the learning material (Nkadimeng and
Ankiewicz, 2022). Games have been advantageous for learning in
dierent domains, including more authentic learning and increased
student engagement because of their degree of interactivity and
immersion (Alonso-Fernandez etal., 2019). Since STEM subjects are
complex and challenging to learn, games can bea great way to
introduce learners to scientic concepts. Several studies have
demonstrated the benecial impacts of games on science education.
A study on DGBL for elementary science education revealed increased
student engagement, domain knowledge, and problem-solving skills
(Lester etal., 2014). Students who played the personalized DGBL
application about plants gained a signicant increase in learning
achievements and motivation (Hwang et al., 2012). In addition,
students who learned about migratory bird identication with the
DGBL environment have signicantly outperformed their peers in the
acquisition of learning and motivation (Chu and Chang, 2014). e
game Sorceress of Seasons was utilized to teach fundamental
programming concepts. is resulted in increased positive attitudes
toward programming, with female students reporting larger increases
in computer science interest than males. e study suggests that games
may be successful in increasing interest in STEM (Bonner and
Dorneich, 2016). Further, the simultaneous presence of learning
experiences and player self-determination while playing a STEM
digital game might foster STEM interest (Ishak et al., 2022). e
positive eects of digital games on student achievement, skills
acquisition, motivation, and engagement have inuenced educators,
game developers, funding organizations, and researchers to use games
across many platforms to teach STEM subjects (Bertozzi, 2014).
Minecra is one of the game platforms used to teach and
encourage interest in STEM. Minecra is a sandbox-style video game
released in 2009 by Mojang and the most widely played game in the
world, with more than 180 million copies sold to date (Bitner, 2021).
Due to its popularity and exibility, educators utilize this game
platform to develop instructional materials and activities to cultivate
student interest in STEM. Pusey and Pusey (2015) used MinecraEdu
as an instructional tool to teach Earth Science topics to Grade 8
students in 2 schools in Australia. Along with the traditional teaching
methods such as worksheets, slideshows, videos, and hands-on
activities, the MinecraEdu lessons were utilized once a week
throughout the 5 to 6-week Earth Science program. Students who
participated in the program expressed increased enthusiasm about
attending science class because they liked the interactive learning,
teamwork, and enjoyable coursework. is result showed that aer the
use of MinecraEdu lessons, student interest in science has increased.
Nkadimeng and Ankiewicz (2022) also reported a similar nding
about using MinecraEdu for a series of ve 1 h lessons in atomic
structure in a South-African junior high school. Further, learning with
MinecraEdu makes abstract concepts easier to understand, promotes
critical thinking, and is conducive to collaboration and motivation.
Another study prepared four dierent STEM activities and asked
6th-grade science classes to use Minecra Educational Edition for
4 hours per week. e researchers collected data on STEM interests
using the STEM Career Interest Survey and Scientic Creativity Scale.
Both scientic creativity and STEM interest levels statistically
increased (Saricam and Yildirim, 2021). ese results imply that
MinecraEdu might besuitable as a learning tool for Science and
Chemistry subjects. Furthermore, there is evidence from prior studies
that games have a positive eect on STEM interests. However, there is
a lack of longitudinal studies. Indeed, papers such as those of Gao etal.
(2020) call for longitudinal studies to determine game-based learning’s
far-reaching eects.
What-If Hypothetical Implementations in Minecra (WHIMC;
https://whimcproject.web.illinois.edu/) also aims to engage, excite,
and generate interest in learning science. WHIMC is a set of Minecra
worlds teachers can utilize as supplementary activities in teaching
STEM. It includes a Rocket Launch Facility, the Lunar Base LeGuin,
and a Space Station as shown in Figure1. It also includes exoplanets
and dierent versions of Earths, e.g., Earth with no moon, Earth with
a colder sun. WHIMC immerses learners in simulated environments
wherein they can move around these dierent worlds and make
observations while exploring them (Yi and Lane, 2019; Manahan and
Rodrigo, 2022; What-If Hypothetical Implementations in Minecra
(WHIMC), n.d.).
WHIMC has been the platform for several studies. One such
study conducted during a summer camp examined campers’
actions by giving them a quick 10 min presentation on
hypothetical earth scenarios before allowing them to explore
worlds in Minecraft. It revealed that sandbox games can spark
interest in STEM subjects among underrepresented adolescents
and that engagements with natural phenomena are possible in an
open digital environment (Yi etal., 2020). Another study (Yi
et al., 2021) examined interest triggers within Minecraft and
found that personal relevance relates to a desire to reengage in
camp content and with the design and structure of the
intervention. Further study on STEM interest triggers within
Minecraft in a hybrid summer camp found that various in-game
and contextual aspects of the learning experiences, such as
instructional conversation, novelty, ownership, and challenge,
triggered the learners’ STEM interests (Lane et al., 2022).
Tablatin et al. 10.3389/feduc.2023.1127984
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Gadbury and Lane (2022) encouraged teenagers to participate in
five after-school sessions over the course of 5 weeks, during
which they used Minecraft to explore several versions of Earth.
The research investigates how different levels of STEM interest
affect in-game science tool usage and observations across the
hypothetical versions of Earth. The result revealed that
participants with moderate STEM interests had the highest
science tool usage, indicating high engagement and desire to
learn. In terms of observations, participants with high STEM
interests recorded high observations, suggesting confidence or
high prior knowledge. Studies on the use of WHIMC were also
conducted in the Philippines. The analysis of learner traversals
of Minecraft worlds conducted in a grade school found a negative
correlation between learner performance and overall distance
traveled. This finding implied that low performers wander early
in gameplay while high performers use a depth-first search
strategy when exploring an area and are goal-oriented (Esclamado
and Rodrigo, 2022a). The study of Casano and Rodrigo (2022a)
performed a comparative assessment of American and Filipino
learner traversals and in-game observations within Minecraft
against canonical answers from experts. The finding suggested
that high performers make more observations aligned with
canonical answers from experts than low performers. They also
found a difference in the in-game behavior of low performers.
Filipino students tend not to make in-game observations, while
American students actively make in-game observations. Another
study looked at the achievement, behaviors, and STEM interests
of frustrated and bored learners using WHIMC and found that
frustrated learners tend to disengage from the game and bored
learners tended to perform poorly on post-game assessments
(Esclamado and Rodrigo, 2022b). Further, the analysis of game
experience and STEM interest of primary school learners in the
Philippines reported that high and low performers had the same
level of game experience and that they like the game and learning-
related WHIMC features. However, the learning task integrated
into the WHIMC-based modules made learning difficult for the
high performers, and technical bugs made learning difficult for
the low performers. The finding on the STEM interest showed
that high performers had a higher degree of agreement with the
Stem Interest Questionnaire (SIQ) compared to the low
performers (Casano and Rodrigo, 2022b). This study aims to
continue the Philippine studies by promoting the use of WHIMC
as a learning tool in a Philippine middle school to cultivate
student STEM interests. Specifically, we seek answers to the
following research questions:
RQ1: What is the eect of using WHIMC on the STEM interests
of students?
RQ2: What is the dierence between the game experience of high-
and low-performers?
RQ3: What is the relationship between the module attributes and
student performance?
AB
C
FIGURE1
WHIMC worlds.
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2. Materials and methods
e study used quantitative and qualitative methods to determine
the eect of WHIMC on the STEM interests of Filipino students.
Weused an embedded design wherein wecollected quantitative data
from the SIQ and Game Experience Questionnaire (GEQ) survey
questionnaires and qualitative data from the open-ended questions
included in the GEQ questionnaire. Insights drawn from analyzing the
answers to the open-ended questions about the module attributes
might support the observations from the quantitative analysis of the
SIQ and GEQ ratings. erefore, we rst performed quantitative
analysis of the pre-SIQ and post- SIQ ratings and GEQ ratings of the
high- and low-performers to determine the eect of using WHIMC
in the students’ STEM interests and the dierence between the game
experience of high- and low-performers, respectively. We then
performed a qualitative analysis of the answers to the open-ended
questions on the attributes of the module to determine the relationship
between the module attributes and STEM interests. e research
protocol was reviewed and approved by the University Research Ethics
Committee of the Ateneo de Manila University.
2.1. Teacher-created learning modules
e research team established a formal partnership with the
University of Illinois Urbana-Champaign (UIUC) team to gain access
to WHIMC’s content, code, and conguration details. A parallel
server was then set up in the Philippines to run the experiments and
manage the tasks without constantly coordinating with the UIUC
team. Aer that, the research team established institutional
partnerships with elementary and middle schools in the Philippines.
Partner teachers were recruited, informed about the project goals, and
requested to design WHIMC-based learning modules and out-of-
game assessments. e research team gave the partner teachers 30 days
to explore the WHIMC worlds to familiarize themselves with the
game. e partner teachers then chose specic topics within their
respective academic curriculum levels where they thought a particular
WHIMC world would t. e partner teachers and the research team
reviewed the learning modules for quality, viability, and curriculum
alignment before using these modules in class. e research team then
provided documentation to assist the partner teachers in preparing for
the WHIMC module implementation. e project manager also gave
the partner teachers Minecra account credentials to beused by the
participating students in their class before the module implementation.
Only the partner teacher engaged with the students during the module
implementation in the class sessions. However, members of the
research team were available inside the Minecra server to assist in
resolving potential student problems. e research of Manahan and
Rodrigo (2022) provides a more thorough explanation of the
preparation and support given to partner teachers and their classes in
integrating and implementing WHIMC in their curriculum.
In this study, the partner teachers from a middle school in the
Philippines developed two (2) learning modules for their Grade 8
science curriculum. Since Minecra uses a biome system and
adopts representation of real-world animals (Ekaputra etal., 2013),
the partner teachers utilized WHIMC to teach topics on ecology.
e partner teachers chose ecosystem as the topic for Module 1
and biodiversity and evolution for Module 2. e developed
modules employed asynchronous and synchronous teaching
modalities. e learning modules implemented a self-discovery
teaching strategy where students are provided access to the
WHIMC worlds before the 1 h synchronous sessions to give
students ample time to explore, provide observations, and infer an
understanding of the worlds. e Minecra game-play was
integrated into the modules as a pre-lecture and motivation
activity. Wang et al. (2022) found that students at dierent
educational levels respond dierently to games. Primary school
students are at a developmental stage where they are unable to
master the rules of the games quickly and are therefore attracted
by the freshness and novelty of games. However, secondary and
higher education students master the game rules quickly, resulting
in decreased interest. us, the Minecra game-play integrated
into the module has no specic time limit to allow students to
explore the worlds at their own pace. However, each Minecra
session must be completed before the synchronous session.
Students need to complete 2 Minecra game-play sessions. e
learning tasks integrated into the WHIMC-based modules were
designed to apply a number of higher-order thinking skills
represented in Bloom’s taxonomy. e game attribute of the
modules consists of the exploration of the simulated environment
of the WHIMC worlds. Students underwent training and
orientation in Module 1, wherein they explored the space station
and experienced the hub that supports life. ey explored the
built-in ecosystem of the Lunar Base LeGuin to identify the biotic
and abiotic components and observe the systemic relationships of
the sta in the area. In Module 2, students explored the What-If
worlds, wherein they experienced the life of an astronaut. ey also
experienced dierent What-If scenarios of the planet Earth (Tilted
Earth, No Moon, Colder Sun) that showed them opportunities to
observe the planet under altered conditions. e observation of the
students must revolve around the environmental change of the
dierent versions of Earth compared to normal Earth, the
appearance of trees, plants, and topography, the existence and
behavior of animals, and compare the pressure, temperature,
oxygen, radiation, atmosphere, altitude, and wind for each world.
Each module began with an asynchronous session in which
students explored the WHIMC worlds and recorded their observations
as indicated in the module. Aer the asynchronous session, students
turn in their answers for the formative assessments and activity
worksheets. e 1 h synchronous session focused on the discussion of
the lesson using simulations and inquiry-based learning to encourage
student active participation, followed by a knowledge assessment
related to the topic. See Figures2, 3 for the excerpt of the developed
WHIMC-based modules.
2.2. Participants
e entire Grade 8 school population consisting of 8 class
sections were recruited for the study. However, out of the 212
prospective participants, 31 opt not to participate and 64 did not
complete the survey questionnaires they were asked to answer. us,
the total participants in this study were 117 middle school students
(53 male and 64 female) aged 13–14 years old. e collection of data
from the participants was approved by the University Research Ethics
Oce (UREO). e students submit the signed consent forms
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Frontiers in Education 05 frontiersin.org
indicating their participation in the experiment prior to data
collection. e data used in the analysis come from the Stem Interest
Questionnaire (SIQ) ratings, Game Experience Questionnaire (GEQ)
ratings, and answers to the open-ended questions about the module
attributes, alongside the performance ratings (high or low) of
the participants.
2.3. Pre-test and post-test
Before using WHIMC, the students complete the pre-SIQ to
determine their baseline interest in these domains. e students took
knowledge assessments, the GEQ, and the post-SIQ as post-test aer
using WHIMC. e SIQ was given as a pre-test and post-test to
determine whether using the WHIMC-based modules made an
impact on the STEM interests of students.
2.4. Knowledge assessment
Students took knowledge assessments aer the asynchronous and
synchronous sessions of each module. e out-of-game assessments
consisted of formative evaluations, asynchronous worksheets, and
long tests. e observations made by the students while using
WHIMC served as formative evaluations. Aer the asynchronous
session, students must complete the asynchronous worksheets
associated with each module topic. Further, long tests consisting of
identication and essay questions related to the module topics were
administered aer the synchronous sessions. High-performers and
low-performers were identied based on their out-of-game assessment
scores. High-performers (HP) are those students with total assessment
scores exceeding the mean score (HP = s > x). Conversely,
low-performers (LP) are those students with total assessment scores
below or equal to the mean score (LP = s ≤ x).
FIGURE2
Lesson excerpt of module 1.
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2.5. Stem interest questionnaire
e pre-SIQ determined their interests prior to using
WHIMC. Aer answering the SIQ, students were given access to the
WHIMC worlds and instructed to follow the guidelines described in
the teacher-created learning modules. Students then answered the
post-SIQ and the Game Experience Questionnaire (GEQ) aer using
WHIMC. e out-of-game assessment questions that are part of the
teacher-created learning modules were then given to the students to
complete the data collection process.
The SIQ used in this study is an abridged version of an original
Student Interest and Choice in STEM (SIC-STEM) questionnaire
developed by Roller etal. (2018), which was based on the Social
Cognitive Career Theory (SCCT) questionnaire of Lent and
Brown (2008). This instrument is employed to characterize and
assess the propensity of students to pursue STEM careers. In this
framework, five dimensions (SCCT constructs) are identified to
describe STEM interests: Self-efficacy: the judgment of one’s
perceived ability; Outcome Expectations: the perceived
consequences of one’s decisions and; Interests: the affinities of a
person; Choice Goals: the perception that the choice to acquire
STEM-related knowledge is important in the future; and Choice
Actions: the perception that STEM-related actions today will
provide support in a future career.
FIGURE3
Lesson excerpt of module 2.
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e SIQ used in this study consisted of 10 items from the
SIC-STEM questionnaire based on their relevance to WHIMC and the
teacher-created learning modules. e respondents rate their level of
agreement using a 5-point Likert scale format (1 – strongly disagree, 2
– disagree, 3 – neutral, 4 – agree, 5 – strongly agree). Table1 presents
the mapping of the SIQ items to the SIC-STEM constructs.
2.6. Game experience questionnaire
e GEQ used in this study is also an abridged version of the
instrument developed by IJsselsteijn etal. (2013) to measure the
factors in a game that contribute to an engaging gameful experience
described across seven (7) dimensions of the player experience
namely, Immersion: how strongly the players felt connected to the
game; Flow: how much the player lost track of their own eort or time
while playing the game; Competence: the player’s judgment of their
own performance against the game’s goals; Positive Aect and
Negative Aect: reports of positive and negative emotional
experiences while playing the game; Tension: reports relating to
frustration and annoyance; and Challenge: an indication of how
dicult the players found the game to be. Johnson et al. (2018)
validated the GEQ used in this study and the ndings suggest a
revised structure that reduces the seven dimensions to ve factors.
Flow, immersion, competence, and positive aect dimensions have
some empirical support. However, it was noted that items in the
negative aect, tension, and challenge dimensions overlap and should
not beevaluated independently. It would bemore acceptable to see
these aspects as being merged into a single negative factor. Since
wewanted a ne-grained analysis of the negative gaming experience
of the students while using WHIMC, wetreated the negative aect,
tension, and challenge dimensions separately.
e questionnaire used in this study was adopted from Casano
and Rodrigo (2022b). e instrument only included 23 items that
seemed relevant to the context of WHIMC and the teacher-created
learning modules out of the 33 core module items of the original
GEQ. e respondents rate their level of agreement with the items
using a 5-point Likert scale format (not at all - 1, slightly - 2,
moderately- 3, fairly- 4, extremely- 5). Table2 presents a mapping of
the GEQ items to the player experience components.
Four (4) open-ended questions were appended to the GEQ. ese
questions were: What was your favorite part of the module and why?;
What was your least favorite part of the module and why?; What about
WHIMC made the topic fun, interesting, or easy to learn?; and What
about WHIMC made the topic boring and/or dicult to learn?.
2.7. Data analysis
To answer the research questions of this study, weconducted
statistical analyses of the pre-SIQ and post-SIQ, GEQ, and answers to
the open-ended questions on the module attributes. Paired samples
t-test was used to analyze the pre-SIQ and post-SIQ ratings of the
students to determine the eect of using WHIMC on the STEM
interests of students. Independent samples t-test was used to compare
the game experience between the high-performers and low-performers
using their GEQ ratings. A point-biserial correlation was used to
determine the strength and direction of association of each favorite
module attribute between the high-performers and low-performers.
For the qualitative analysis, the text data (responses to the favorite
and least favorite open-ended questions on module attributes) were
analyzed using thematic analysis. e text data were assessed and
tagged by coders as being related to the learning topic, learning task,
or game attribute of the teacher-created learning module.
e resulting dataset was then subjected to the bag-of-words
approach for text analytics. In particular, pre-processing was conducted
to transform the text data into a quantiable form. e text data was
converted into lowercase form, removal of punctuations, special symbols,
numbers, and extra whitespaces, stopwords (pronouns and other
common yet irrelevant words), stemming (transformation to base form),
and stem completion (transformation to sensible form). Finally, the text
data were tokenized and transformed into a document-term matrix.
e transformed text data was then merged with the performance
and thematic tagging data, and were then subjected to statistical
treatments. Descriptive visualizations were employed to characterize
the responses of the students. Word clouds were used to show the
relative frequencies of dominant words for each module and each type
of response (favorite or least favorite attribute).
3. Results
3.1. Analysis of SIQ ratings
e students answered the SIQ twice: before and aer playing
WHIMC. A paired-samples t-test was conducted to compare the SIQ
ratings of the students before and aer using WHIMC as a learning
tool. e analysis of the SIQ ratings revealed that there was no
signicant dierence in the overall pre-SIQ ratings (M = 3.60,
SD = 0.27) and post-SIQ ratings (M = 3.65, SD = 0.29) using WHIMC;
t(116) = −1.78, p = 0.077. ere is only a slight increase in the overall
SIQ ratings aer using WHIMC. is result suggests that using
WHIMC as a learning tool only has a minimal eect on the STEM
interests of the students.
To conduct further analysis on the SIQ ratings, paired samples
t-tests were conducted to compare the SIQ ratings of the students
TABLE1 Mapping of SIQ items to the SCCT constructs.
SIC-STEM
constructs
Items
(SE) Self-Ecacy 1 Iknow Ican do well in science.
4 Ithink Science is challenging to learn.
(OE) Outcome
Expectations
9 Aer Inish high school, Iwill use Science oen.
10 Ibelieve that Ican use Math and Science to solve
problems in the future.
(I) Interests 2 Ienjoy Science activities.
3 Ienjoy solving Science and Math problems.
(CG) Choice
Goals
5 Leaning Science will help me get a good job.
6 Knowing how to use Math and Science together will help
me to invent useful things.
7 Understanding engineering is not important for my career.
(CA) Choice
Action s
8 Itry to get a good grade in science because Ihave an
interest in science jobs.
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before and aer using WHIMC on the dierent SIC-STEM constructs.
e result of the statistical analysis revealed that only the Choice
Actions construct of the 5 SIC-STEM constructs showed a statistically
signicant dierence. e pre-SIQ rating of the Choice Actions
construct (M = 3.34, SD = 1.13) signicantly increased aer using
WHIMC (M = 3.50, SD = 1.00); t(116) = −2.263, p = 0.025. is result
indicates that the students understood the importance of studying
hard and earning high marks in class if they are interested in STEM-
related careers. Figure4 presents the bar chart showing the aggregated
pre-SIQ and post-SIQ ratings on each SIC-STEM construct.
Figure 5A shows the bar charts of the pre-SIQ and post-SIQ
ratings on each SIC-STEM construct of the low-performers. Paired-
samples t-tests were conducted on each construct and results show
that the pre-SIQ rating for the Self-ecacy construct (M = 3.49,
SD = 0.59) signicantly increased aer using WHIMC (M = 3.63,
SD = 0.67); t(53) = −2.127, p = 0.038. is nding might indicate that
the low-performers gained some condence in their ability to
understand science concepts.
Figure5B shows the bar charts of the pre-SIQ and post-SIQ ratings
on each SIC-STEM construct of the high-performers. Paired-samples
t-tests were conducted on each construct and results revealed that the
pre-SIQ rating for the Interest construct (M = 3.60, SD = 0.77) signicantly
increased aer using WHIMC (M = 3.74, SD = 0.80); t(62) = −2.092,
p = 0.041. High-performers’ increased level of agreement in the Interests
construct may berelated to how much they enjoyed and persisted in
completing the assigned tasks from the WHIMC-based modules.
e observations on the analysis of each SIC-STEM construct
provided some evidence that the teacher-created learning modules
using WHIMC increased some aspects of STEM interest
among students.
3.2. Analysis of the GEQ answers
e GEQ was administered to measure the factors in a game that
contribute to an engaging gameful experience described across 7
dimensions of the player experience: Positive Aect (PA), Negative
Aect (NA), Immersion (I), Flow (F), Competence (C), Challenge
(Ch), and Tension (T). Independent samples t-test was used to
determine if there is a signicant dierence in the overall GEQ
ratings between the high- and low-performers. e statistical test
result revealed no statistically signicant dierence in the overall
GEQ ratings between the high-performers (M = 2.48, SD = 0.13) and
low-performers (M = 2.38, SD = 0.19); t(103) = −1.311, p = 0.193. is
result revealed that both groups had the same level of engagement in
using WHIMC as a learning tool. Independent samples t-tests were
used on each dimension to check for dierences between high- and
low-performers. e tests revealed that only the Immersion
dimension had a signicant dierence between the groups. High-
performers have signicantly higher GEQ ratings (M = 3.34,
SD = 0.56) compared to the low-performers (M = 3.04, SD = 0.68) aer
using WHIMC; t(106) = −2.584, p = 0.011. is nding suggested that
high-performers connected more deeply with the game and may
therefore have had a more engaging learning experience than
low-performers. Figure6 shows the GEQ ratings of the high- and
low-performers on each GEQ dimension.
3.3. Analysis of the open-ended answers
Insights drawn from analyzing the answers to the open-ended
questions about the module attributes might complement the
observations from the analysis of the SIQ and GEQ ratings discussed
in the previous sections. Weconducted qualitative analysis of the
responses to the open-ended questions to determine the relationship
between the module attributes and student performance.
e individual answers of the students about their favorite and
least favorite attributes of the module were assessed and tagged as
feedback about the learning topic, learning task, or game module
attribute. ree coders categorized 468 rows of open-ended answers
using the criteria described in Table3. e coders coded independently
using a spreadsheet containing the class numbers with the
TABLE2 Mapping of GEQ items to the player experience components.
GEQ component Items GEQ component Items
(I) Immersion 2 Iwas interested in the game’s story (P) Positive Aect 1 Ifelt content.
9 It was esthetically pleasing. 3 Ithought it was fun.
14 Ifelt imaginative. 5 Ifelt happy.
15 Ifelt that Icould explore things. 10 It felt good.
19 Ifound it impressive.
22 It felt like a rich experience.
(F) Flow 4 Iwas fully occupied with the game. (N) Negative Aect 6 It gave me a bad mood.
20 Iwas deeply concentrated on the game. 7 Ifound it tiresome.
12 Ifelt bored.
(C) Competence 8 Ifelt competent. (T) ension 17 Ifelt annoyed
11 Iwas good at it. 21 Ifelt frustrated
13 Ifelt successful.
16 Iwas fast at reaching the game’s targets.
(CH) Challenge 18 Ifelt challenged
23 Ifelt time pressured
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corresponding open-ended answers and three (3) columns with
headings indicating the three module attributes. Each coder tagged
the open-ended answer by lling in the columns with either 1 or 0
indicating the presence or absence of the module attribute in the
feedback. e coders unanimously coded 995 (70.87%) module
attributes, two (2) coders were in agreement for the 383 (27.28%)
module attributes, and 26 (1.85%) module attributes were coded
dierently by each coder. e coders then convened to reach a
consensus on the dierences in the coding.
3.3.1. Analysis of the answers to the favorite part
of the module
e 234 rows of labeled data containing the values of favorite
module attributes were analyzed using frequency count to determine
the favorite module attributes and the number of favorite attributes.
A point-biserial correlation was also performed to determine the
strength and direction of association of each favorite module attribute
between the high-performers and low-performers. is statistical
analysis was utilized since the nature of the data is dichotomous.
e bag-of-words text analytics approach was then applied to the
text data. e transformed text data was then merged with the
performance for quantitative text analytics. is analysis was performed
to characterize the text data and identify the underlying themes.
Figure7A shows that the favorite module attribute of both groups
is the learning topic of the modules. is result implies that high-
performers and low-performers enjoyed the lessons integrated into
the WHIMC-based learning modules. High-performers liked all the
module attributes except the learning task attribute of Module 2. On
the other hand, low-performers prefer the learning topic module
attribute over the learning task and game module attributes.
e percentage of respondents on the number of favorite
attributes (Figure 7B) revealed that most of the low performers
mentioned 2 module attributes whereas high performers mentioned
3 module attributes in their responses about their favorite attributes
in Module 1. However, for Module 2, both groups identied only one
(1) module attribute as their favorite. Based on the data presented in
Figure7A, low-performers chose the learning topic and tasks as their
favorite module attributes of Module 1. Further, both groups liked
the learning topic more than the learning task and game module
attributes of Module 2.
Table 4 presents the point-biserial correlation result of the
favorite module attributes. e table shows a signicant positive
correlation between the game module attribute and performance
(rpb = 0.203, n = 117, p = 0.029). is implied that students who
liked the game attribute of Module 1 performed better in the
out-of-game assessments. For Module 2, the performance has
signicant positive correlation with the learning task (rpb = 0.270,
n = 117, p = 0.003) and game (rpb = 0.307, n = 117, p = 0.001) module
attributes while a signicant negative correlation was observed for
the learning topic (rpb = −0.237, n = 117, p = 0.010). is nding
could mean that students who chose the learning topic module
attribute as their favorite did not perform well in the assessment. In
contrast, students who performed better in the assessment chose
the game or learning task module attribute as their favorite part of
the module. We also found a signicant positive correlation
between the number of favorite attributes of Module 1 (rpb = 0.208,
n = 117, p = 0.024) and Module 2 (rpb = 0.212, n = 117, p = 0.022)
with the performance.
ese ndings corroborate the result of the analysis of the GEQ
ratings that high performers had a better quality of game experience
compared to low performers. Students who liked the game and
learning task module attributes are likely to perform better in the
out-of-game assessments. Wenote that 2 out of the 3 out-of-game
assessments are conducted aer exploring the WHIMC worlds
assigned in the modules. us, students must beengaged in the game
and learning tasks to have better assessment scores.
FIGURE4
Aggregated pre- and post-SIQ ratings on each SIC-STEM construct.
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To characterize the responses of the high- and low-performers
to the open-ended questions, word clouds were generated. As can
beseen in Figure8A, the most dominant word about the favorite
attribute of Module 1 is learn. is nding suggests that both high
performers and low performers mentioned learning in their
responses. e other dominant words such as Minecra and fun
refer to the simulated environment using WHIMC, which is related
to the game attribute of the module. e words ecosystem, biotic,
and abiotic are related to the topic or lessons in Module 1. e
word explore might berelated to the learning task module attribute
since students were asked to explore the WHIMC world Lunar
Base LeGuin to identify the biotic and abiotic components and
make observations about the systemic relationships of the people.
is nding is aligned with the results of the quantitative analysis
of the tagged text data since the dominant words relate to all the
module attributes.
Similar to the ndings in the responses about the favorite
attributes of Module 1, learn is also the top word in the responses
about the favorite attribute of Module 2 (Figure 8B). e words
dierent, worlds, explore, and fun might refer to the ability of the
students to explore the dierent worlds and the fun experience they
had using WHIMC. ese words are related to the game attribute of
the module. e words that relate to the learning topic attribute are
animals, things, interesting, and adapt. Students did not mention much
in their responses about quests and observations, which are words
related to the learning task attribute. is result indicates that while
the students enjoyed the learning topic and game component of
Module 2, they were less enthusiastic about the learning tasks.
A
B
FIGURE5
Pre- and post-SIQ ratings on each SIC-STEM construct, (A) Low-performers. (B) High-performers.
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3.3.2. Analysis of the answers to the least favorite
part of the module
e same analysis discussed in the analysis of the answers to the
favorite part of the module was also utilized to draw insights about the
least favorite part of the module.
Based on Figure9A, the game and learning task attributes of
Module 1 are the least favorite. is result might bebecause students
encountered technical diculties while playing and experienced a
hard time completing the quests or tasks assigned in the module. For
Module 2, most of the comments come from the high-performers and
they identied the learning task module attribute as their least favorite.
is might bebecause of the many tasks assigned in this module and
the need to go through 3 What-If worlds, which require more time to
complete and more observations to be recorded while playing
the game.
Figure9B presents the number of least favorite attributes of the
high- and low-performers. Wecan observe that at least 1 module
attribute has been mentioned by both groups. e game attribute of
Module 1 as shown in Figure9A was identied to bethe least favorite
of both groups. However, for Module 2, most of the low-performers
did not have a least favorite whereas high-performers mentioned at
least one least favorite module attribute. e high-performers are less
enthusiastic about the learning task module attribute.
e result of the point-biserial correlation shows that the
attributes of Module 1 and the number of least favorite attributes have
no signicant correlation with student performance as shown in
Table5. is result could mean that although students mentioned
attributes of the module that they do not like, it does not inuence
their performance. In terms of Module 2, the Task module attribute
has a signicant positive correlation with student performance
(rpb = 0.327, n = 117, p = <0.001) and the number of favorite attributes
(rpb = 0.202, n = 117, p = 0.029). e result implies that students who
mentioned the Task module attribute as their least favorite perform
better than those who did not. When high-performers comment
about the learning task module attribute, this might bebecause they
experienced a hard time doing the assigned tasks but are still
motivated to complete them.
To characterize the responses of the high- and low-performers to
the open-ended questions on the least favorite module attributes,
word clouds were generated. e top ve dominant words for the
responses on the least favorite attributes of Module 1 (Figure10A) are
time, Minecra, hard, going, and confusing. ese words describe the
experience that the students had while playing WHIMC. Students
mentioned in their comments that they had a hard time connecting
to Minecra, going to dierent worlds or portals, and sometimes
being confused about what to do next. is nding implies that most
of the comments are related to the game attribute of the module.
e top ve dominant words for the responses on the least favorite
attributes of Module 2 (Figure 10B) are time, quests, Minecra,
confused, and nd. ese words relate to the experience that the
students had while doing the tasks integrated into the module using
WHIMC. Students commented about experiencing a hard time
FIGURE6
Game experience dimensions between the high- and low-performers.
TABLE3 Attributes of the teacher-created learning modules.
Module attribute Criteria
Game If the answer mentions elements of the WHIMC map or interactions within the game world including references to in-game mechanics, the
answer is categorized as Game.
Learning Topic If the answer mentions being able to acquire information in some way, or learning facts while interacting with the WHIMC worlds, the answer
is categorized as Learning Topic.
Learning Task If the answer makes a reference to the tasks or mentions an in-game behavior as indicated in the teacher-created learning module, tag the
answer with Learning Task.
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completing the quests, nding the NPCs, and being confused about
where to go next to complete the quests. ese comments relate to the
task and game attributes of the module.
Why did student preferences dier from Module 1 to Module 2?
Weoer some speculation: e learning objectives of Module 1 were
simple (see Figure2), and students only had to explore the biodome
A
B
FIGURE7
Responses on favorite module attributes, (A) Module attributes, (B) Number of module attributes.
TABLE4 Point-biserial correlation result of the favorite module attributes.
Variables Statistics Topic Task Game No. of favorite
attributes
Topic Task Game No. of favorite
attributes
Module 1 Module 2
Performance
Point Biserial 0.003 0.129 0.203*0.208* −0.237** 0.270** 0.307** 0.212*
Sig. (2-tailed) 0.974 0.165 0.029 0.024 0.010 0.003 0.001 0.022
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to perform the learning tasks and get the answers to the out-of-game
assessments. is means that Module 1 tended to beeasy, which may
account for why high-performers liked all the module attributes and
low-performers liked the topic and task attributes. Low-performers
did not express liking the game attribute, a sentiment echoed by their
GEQ responses, in which they had slightly higher ratings for Negative
Aect and Tension dimensions compared to high-performers.
Low-performers might have found the open-ended learning
environment confusing. ey might not have had a high-level
understanding of their location, leading them to wander without
purpose (Esclamado and Rodrigo, 2022a).
For Module 2, students had to explore three WHIMC worlds
(Tilted Earth, No Moon, Colder Sun). ey had to make observations
to infer the possible adaptations of organisms and explain how these
adaptations could lead to species diversity and survival. Module 2 was
harder and more open-ended than Module 1. is might explain why
many high performers expressed not liking the task module attribute.
4. Discussion
Learners oen nd STEM dicult because it requires complex
thinking, repeated practice, and self-discipline. Hence, educators are
thinking of innovative ways to provide an engaging learning
environment that keeps students interested and enthusiastic about
STEM subjects. Minecra is one of the innovative approaches that has
been adopted in science education (Pusey and Pusey, 2015;
Nkadimeng and Ankiewicz, 2022). us, the purpose of this study is
to continue to promote the use of WHIMC-based modules as a
learning tool to cultivate the STEM interests of Filipino middle
school students.
Our rst research question is to determine the eect of using
WHIMC on the STEM interests of students. e result of the analysis
of the aggregated SIQ ratings before and aer using the WHIMC-
based modules revealed only a minimal eect on the STEM interests
of the students. is implies that the implementation of the WHIMC-
based modules in a Philippine middle school did not reveal a
signicant impact on the students’ STEM interests based on their SIQ
ratings. is nding supports the result of the analysis of the STEM
interest of primary school learners in the Philippines (Casano and
Rodrigo, 2022b). But the result of this study is promising since there
is still an increase in the SIQ ratings of students aer learning two
ecology topics with WHIMC. Further, there is a signicant increase
in the Choice Actions construct, which suggests that the students
appreciate the importance of motivation to study hard and get good
grades if they want to pursue STEM-related careers. Moreover, the
signicant increase in the Self-ecacy ratings of low-performers
might suggest that they gained some condence in their ability to
understand science concepts aer using WHIMC. is result is
aligned with Nkadimeng and Ankiewicz (2022) that using
MinecraEdu helped students gain a more concrete understanding of
abstract topics. High-performers’ increased level of agreement in the
Interests construct may berelated to how much they enjoyed and
persisted in completing the assigned tasks from the WHIMC-based
modules. is result corroborates the ndings that using a digital
game in teaching may be successful in fostering STEM interest
(Bonner and Dorneich, 2016; Saricam and Yildirim, 2021; Ishak etal.,
2022). Development of additional WHIMC-based modules focused
on ecology topics might beneeded to conduct a further evaluation to
conrm or contrast the result of this study. is endeavor will
bechallenging since successful module design and implementation is
time-consuming and requires technical, pedagogical, and
content knowledge.
We also wanted to nd out if there is a dierence in the game
experience between the high-performers and low-performers. e
analysis of the overall GEQ ratings revealed no statistically signicant
dierence between the game experience of high- and low-performers.
Wecan infer that the overall game experience with WHIMC was the
same for high and low performers, which conrms the nding of
Casano and Rodrigo (2022b). Statistical tests were also conducted for
AB
FIGURE8
Frequencies of dominant words on the favorite attributes. (A) Module 1, (B) Module 2.
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each GEQ dimension to check if there were dimensions that would
reveal statistical signicance between the high- and low-performers.
Among the 7 GEQ dimensions, only the Immersion dimension
showed a statistically signicant dierence between the groups.
Although they have the same level of agreement for Negative and
Positive Aect, Challenge, Competence, Flow, and Tension, high-
performers have signicantly higher GEQ ratings on the Immersion
dimension. With this nding, wecan infer that high-performers had
a more positive, engaging, and enjoyable learning experience with
WHIMC than the low-performers. ese results support the ndings
of other studies that game-based learning could increase learning
achievement (Hwang etal., 2012; Chu and Chang, 2014), engagement
(Lester etal., 2014; Alonso-Fernandez etal., 2019; Gadbury and Lane,
2022), desire to learn (Gadbury & Lane), and enjoyment
(Alawajee, 2021).
Lastly, we wanted to determine the relationship between the
module attributes and student performance. e results of the thematic
analysis of the open-ended questions revealed that the WHIMC-based
module attributes could aect the student performance and interests
of students in learning science concepts. e ndings on the favorite
A
B
FIGURE9
Responses on Least Favorite Module Attributes. (A) Module Attributes, (B) Number of Module Attributes.
TABLE5 Point biserial correlation result of the least favorite module attributes.
Variables Statistics Topic Task Game No. of favorite
attributes
Topic Task Game No. of favorite
attributes
Module 1 Module 2
Performance
Point Biserial 0.003 0.085 0.097 0.0.121 −0.0.024 0.327** 0.037 0.202*
Sig. (2-tailed) 0.972 0.362 0.297 0.194 0.794 0.000 0.691 0.029
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module attributes suggest that students perform better in the out-of-
game assessments when they like all the module attributes. is implies
that students must beengaged in the game and learning task aside
from being interested in the learning topic to have better assessment
scores. is nding corroborates the result of the analysis of GEQ
ratings, where high-performers have higher ratings for immersion and
ow dimensions aer using WHIMC. e dominant words and
themes of responses relate to the integration of WHIMC into the
modules that allow students to learn and have a fun and enjoyable
learning experience. e comments about the students’ ability to
understand the topics and the fun experience they had with the
WHIMC-based modules could inform us about the suitability of using
WHIMC as a learning tool in science education.
e ndings on the thematic analysis of the least favorite module
attribute revealed that the game and learning task attributes are the
least favorite for Module 1. is result might bebecause students
encountered technical diculties while playing and experienced a
hard time completing the quests or tasks assigned in the module. is
result is aligned with the ndings of Casano and Rodrigo (2022b) that
low performers experienced diculty in learning because of technical
bugs and the learning tasks made it dicult for high performers to
learn. For Module 2, most comments come from the high-performers
who identied the learning task module attribute as their least favorite.
is nding might bebecause of the many tasks assigned in this
module, which require more time to complete and more observations
to be recorded while playing the game. High-performers
acknowledged the diculty of the learning task but were still
motivated to complete them. Students who did not cite any least
favorite module attribute emphasized how fun learning was and how
well they understood the lessons. e negative comments about the
game and task attributes should be addressed in the future
development of WHIMC-based modules to enhance the student
learning experience and interests in STEM. Future module
developments should consider the appropriate task completion
duration since students can complete the tasks at dierent times. To
alleviate the technical diculties encountered while using WHIMC,
partner teachers should organize more time for students to develop
familiarity with the soware so that they will beable to use the game’s
function eectively and eciently.
e results of the thematic analyses on the favorite and least
favorite module attribute are consistent with the ndings about game-
based learning. Researchers found that it improves student motivation
(Hwang etal., 2012; Chu and Chang, 2014; Ennis, 2018; Leong etal.,
2018; Hussein etal., 2019; Shang etal., 2019), encourages the player
to learn (Iliya and Jabbar, 2015; Gadbury and Lane, 2022), helps in
easy understanding of topics (Nkadimeng and Ankiewicz, 2022), and
provides enjoyable coursework (Pusey and Pusey, 2015; Alawajee,
2021). With these ndings, this research could contribute to the
evidences of the impact of using game-based learning in teaching
science concepts.
is research contributes to the literature in a number of ways. It
suggests that an open-ended environment can beused to foster STEM
interest, which corroborates previous ndings on the use of Minecra
during summer camps (Yi etal., 2020, 2021; Lane etal., 2022). It
collects and analyzes game-based data from the Philippines, a
population that is underrepresented in the literature. It also contributes
to the conversation about how and when games should beused with
instruction. e study shows that Minecra can befun and engaging
but just because it is fun and engaging does not guarantee that it will
lead to increased interest in larger domains such as STEM. e study
also shows that open-ended learning environments coupled with tasks
that demand exploration, observation, and higher-ordered thinking
are demanding even on high-performers. Low-performing students
may require more scaolding and guidance. Finally, the integration of
educational games like Minecra in classes requires lengthy lesson
planning and technical preparation. Educators therefore have to
curate the games well and monitor their outcomes in order to ascertain
whether their use is truly worth the investment.
AB
FIGURE10
Frequencies of Dominant Words on the Least Favorite Attributes. (A) Module 1, (B) Module 2.
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5. Limitations to the study
e work presented in this paper has some limitations. First, the
analysis is only limited to the 2 WHIMC-based modules developed by
partner teachers in a Philippine middle school. us, the ndings
from this initial study cannot begeneralized because of the small
number of topics used to determine the eect of using the modules on
the STEM interests of students and game experience. Weplan to have
more partner teachers that will develop additional WHIMC-based
modules and deploy these to other middle schools in the Philippines
to see whether wecan replicate the ndings of this initial study.
During the module implementation, in-game data were also
collected along with the SIQ, GEQ, and open-ended questions. So far,
wehave not yet analyzed the in-game data consisting of students’
observations, use of science tools, and map explorations. In future
work, weplan to analyze these in-game data to understand the in-game
behaviors of students while interacting with the WHIMC worlds and
their relationship to student performance and STEM interests.
Data availability statement
e raw data supporting the conclusions of this article will
bemade available by the authors, without undue reservation.
Ethics statement
e studies involving human participants were reviewed and
approved by University Research Ethics Committee of the Ateneo de
Manila University. Written informed consent to participate in this
study was provided by the participants’ legal guardian/next of kin.
Author contributions
CT: writing, data analysis, coding, statistical analysis, and editing.
MR: writing, editing, reviewing, and supervision. JC: writing and review.
All authors contributed to the article and approved the submitted version.
Funding
Ateneo de Manila University and the Department of Science and
Technology for the grant entitled, “Nurturing Interest in STEM among
Filipino learners using Minecra.”
Acknowledgments
The authors thank H Chad Lane and Jeff Ginger for their
enthusiastic collaboration, Dominique Maire Antoinette
Manahan, Maricel Esclamado, Mikael William Fuentes,
and Ma. Rosario Madjos for their support, our partner
teachers and all participating students, Andhee Jacobe and
Bobby Roaring for coding the module attributes, the Ateneo
Laboratory for the Learning Sciences, and the Ateneo de
Manila University. The authors thank our funding agency, the
Department of Science and Technology for the grant entitled,
“Nurturing Interest in STEM among Filipino learners using
Minecraft,” and our monitoring agency, the Philippine Council for
Industry, Energy, and Emerging Technology Research
and Development.
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their aliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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