rather than a fundamental redesign of education (Section 5.3). We also found
that existing work is anthropocentric, motivating sustainability for human
well-being (Section 4.1).

With the exception of a few articles, education is mostly conceived of as
training pre-defined knowledge, skills or competencies (Section 4.3). We have
identified an alternative understanding of education, which we termed
“emancipatory education”, inspired by previous research. Viewing education as
training has been argued to be too limited. It renders education into an
instrument to bring about certain competencies that are decided upon by the
few, in the current unsustainable system. It locks education to being about
training certain competencies thereby reducing the possibilities for
alternative futures to emerge in education and limiting the potential of
education for change (holfelder2019towards, 77, 62). The potential of
education for alternative futures and radical change has been argued to lie in
being a space for reflection, experimentation and creativity, allowing for the
possibility of the impossible, not yet imagined
(osberg2010TakingCareOfTheFuture, 139, 62). Education could be a place in
which new ideas, understandings and ways of being in the world evolve, drawing
on and taking into account the diverse experiences that all involved parties
may bring (students, teachers, others outside school or universities)
(osberg2010TakingCareOfTheFuture, 139, 77). It would require to make space for
the individual learner’s experiences and meanings. Such education has the
purpose of “subjectification” (biesta2009subjectificationArticle, 17). Ideas
of education for radical change and sustainability stand in contrast to
dominant framings of contemporary education. Education has increasingly become
oriented towards producing certain disciplinary outcomes in an effective way
and has become instrumentalised to achieve economic goals
(amsler2017contesting, 6, 140, 124). In education for radical change, concrete
learning objectives may still be important, but they may be used in new ways,
exploring change and potentials of education as a part of transforming
society. Importantly, learning outcomes should not only be defined on the
grounds of pre-existing occupational profiles and businesses or economic
agendas, but rather help explore novel and potentially disruptive profiles.

Among the work that embraces the need for radical change, we find the argument
that education should play a more activistic role considering the change that
is needed (SB-05, 116). Activism in and through education in computing
education is hardly discussed. In light of the literature on the role of
education, we should be cautious not to turn education into an instrument for
activism as it is perceived by certain people. This would go against the idea
of education as emancipatory, non-normative and less instrumental. However,
education might involve activism or direct actions as means for learning and
acquiring competences. If students are to take part of making and unmaking
futures, they need to be emancipated. Students need to be given opportunities
for deep reflections on the root causes of the current crisis in order to
contribute to a social and political movement for a fundamentally different
society (kaufmann2019building, 92). As a part of this, students need not only
cognitive but also action capabilities, they need to engage with values and
emotions.

Engaging with our predicaments likely evokes unpleasant emotions
(ojala_hope_2017, 138). Emotions have gained increasing attention in the past
years in the education for sustainability literature, albeit less so in
computing education. However, the learning outcomes identified in this work
point to emotions: students become more aware, they become “deeply concerned”,
anxious and restless (Section 5.4). Existing research suggests that young
people today are already very concerned and even pessimistic about the state
of the world. The described outcomes of education may therefore not
necessarily be the effect of the education but may describe the students’
general state. An important question therefore is how education should engage
with emotions. A recent review by Pihkala (pihkala_eco-anxiety_2020, 165)
collects ways to address climate anxiety in education. It has inspired a
recent intervention study in a sustainability course for media technology
students (eriksson_addressing_2022, 58). More than addressing negative
emotions, education should also promote “critical hope” (ojala_hope_2017, 138)
or “active hope” (macy_active_2022, 114). Ojala (ojala_hope_2017, 138) reviews
the role of education for promoting hope and points out that hope is a
contested concept as it can be based on denial or de-emphasising the severity
of sustainability issues. Hope, however, is argued to be an “existential must”
or an ontological necessity (freire1992, 67). Gaining critical awareness and
learning about ways of regulating emotions and distress should therefore be an
important part of education for sustainability. As humans we tend to only take
action when motivated, for example if discomfort arises. It is therefore
important for students in computing to engage and deal with emotions. We pose
the open question of how such affective engagement can become stronger,
considering dominant norms and values in computing that suggest computing is a
rational, value-neutral endeavor.

Research on norms, values or the culture of computing education has been
conducted for decades with a focus on understanding under-representation,
mostly of women (see e.g. (Rasmussen1991, 175, 122, 185, 160, 161, 142, 143)).
Much of the work has been conducted by gender researchers. The research
repeatedly concludes computing is socially (re-)produced or positioned as
purely rational, technical or non-human, abstract and reductionist.
Reductionism is not only understood in terms of reducing complex problems to
smaller parts, but also avoiding to engage with problems in their complexity,
approaching all problems with mathematical and technical methods. Even though
rationality is valued high, emotional engagement does exist, though it is
found to be directed to machines and solving technical problems. Computing
education provides few opportunities to learn about technology in meaningful
contexts (e.g. computing for sustainability). Students dis-engage and adapt an
instrumental approach to studying computing (ottemo_kon_2015, 142). This
culture is argued to be aligned to, or “co-produced with”, (hegemonic)
masculinity. It excludes women, puts them off (salminen-karlsson2003, 184).
This body on computing culture could inform the work on sustainability
education in the future. A recent review of work on climate mitigation
(stoddard_three_2021, 198) has drawn a similar conclusion: an important step
is to connect the work on equality and climate change, which has so far hardly
happened. They find that reasons for our collective failures to address
climate change are a “narrow-techno-economic mindset” and failures of
education to open vistas for imagining fair and sustainable societies.

There are some discussions of norms and values in the articles that could gain
from the connection to existing research on equality or gender. For example,
Easterbrook (SB-40, 49) criticized computational thinking as the focus in
computing education and suggests systems thinking as a necessary competence to
work for sustainability (see also Section 5.1, learning objectives).
Computational thinking is reductionist and the work on gender and equality
helps in understanding why and how reductionist framings are maintained.
Another example is the work in which Mann argues that students should become
change agents promoting strong sustainability, which “will be at odds with
other, less forceful sustainability perspectives” (SB-19, 150). He suggests:
“In the best of all possible worlds, we would like them to act as insiders who
are part of the dominant culture, but who at the same time try to change the
system they are part of from within.” (SB-19, 150, p.19). There is lots of
gender research on cultures of computing, science, and engineering, as well as
education in these fields that help understanding culture and how they are
maintained. Connecting those two fields could be useful to understand the
complex challenges and develop education for system change.

In light of the dominant framings of sustainability education in computing as
training in separate modules, we raise concerns about student well-being. What
happens to students if they learn about the great challenges we are facing in
a module on sustainability, with the outcomes of being restless and very
concerned, feeling discomforting emotions? They may have gotten support to
deal with emotions during the sustainability module but might need to suppress
anxiety or grief in the rest of education, as it lies outside the norm.
Suppressing uncomfortable emotions is one unsustainable way of handling them,
which can lead to depression and stress (pihkala_eco-anxiety_2020, 165). This
is another reason why sustainability cannot be left to a course or few shorter
modules only. Addressing these concerns, emotions and well-being is important
for all courses. There is evidence that computer workers and their products
can significantly benefit from well-being practices (penzenstadler2021take,
159) that can be taught to computing students. If computing is to contribute
to the well-being of others, humans and other species or the planet, then the
discipline needs to evolve as a discipline going beyond the understanding of
technology as a purely rational means of interaction.

In light of all this, we ask the question of how to proceed with curricula and
pedagogical approaches for sustainability education in computing. On the one
hand, we might have reached a critical mass of work that could be used to
define standards and curricula. It could increase the recognition of
sustainability education in computing and hence promote change of norms. It
may also help to push or even “enforce” sustainability education, making sure
all students get a chance to learn about and engage with different aspects of
sustainability. The learning objectives described in the literature reviewed
could inform such a step. On the other hand, we have identified that more
encompassing views of education, which we chose to call “emancipatory
education” in-line with the literature on education for sustainability
(Section 4.3), are mostly absent from the material reviewed. This implies we
should develop education in other ways that grant freedom and trust, allowing
creativity and diverse individuals to develop and contribute to knowledge and
change in “their ways”.

We find few publications on more specific topics within computing and
sustainability, e.g. on software architecture or software testing and
sustainability. This may be due to our search query that looked for papers
within computing and sustainability. Our SLR could hence be extended to cover
these computing-specific education papers.

In order to find other holes and get a more “complete” picture of what
sustainability education in computing can entail, our findings could be mapped
to other existing standards, e.g. to the Dublin Descriptors like, for instance
“making judgments”, which emerged as especially important in sustainability in
computing, for its intrinsic need of an holistic discourse. If such mapping
would be present when combining computing- and sustainability topics in the
design of new courses (or re-design of existing ones), educators could use the
mapping to reflect on which learning objectives (e.g. “making judgements”)
should be achieved with a true combination of computing-sustainability topics,
like architecture assessment methods that embed sustainability dimensions, and
which would instead be achieved by separate sustainability- and computing-
specific topics that are orthogonal and strengthen one another (like
architecture quality assessment methods on the one side, and sustainability
theories on the other side). They are the European Union standard
classification used in educational frameworks for curricula development and
accreditation. As such, they should serve well for the purpose of integrating
sustainability into computing curricula. They have however received criticism
for not addressing attitudes and competencies as for example proposed by
(frezza_modelling_2018, 68). Also, to cover the relationship between existing
computing topics and what is generally understood about the societal
transformations needed may be missing the point, as the changes needed are
claimed to be emancipatory or at least concern new key competencies. Key
sustainability competencies were only found based on the work on by Wiek et
al. from 2011 (see Section 5.1). However, these have been updated since
(pacis2020key, 145).

We hope that this work will lead to further developments of existing
frameworks for computing curricula. A first scan of the ACM/IEEE curriculum
published in 2020 (acm_ieee_curricula, 65) shows, for example, that indirect
impacts or rebound effects of computing systems are not mentioned.

One question for the development of computing curricula is how to make space
for sustainability education. We see that existing computing curricula such as
the ACM/IEEE curriculum (acm_ieee_curricula, 65) get expanded item by item.
Sustainability is one of many items being added or injected into the
curriculum, just as other items. It is next to impossible to discard any items
because they all still seem relevant to some degree by some. As argued by
Klotz (Klotz2021, 98), humans have a tendency to solve challenges by adding
things and making things more complex — rarely do we manage to instead
simplify and subtract. Computing is a young discipline, so it should be
easier, compared to other subjects, to fundamentally rethink the curriculum.
Getting rid of mandatory curricula rather than creating ever more might be a
preferable approach to engage with sustainability, especially as
“sustainability add-ons” risk being seen as less relevant to core technical
topics as long as a techno-centric computing culture persists. Such an idea is
well-aligned with the view of education as emancipatory. It would require a
broad and critical introduction to technology and our current predicaments, a
change of computing culture that orients students to abstract, reductionist
technical competence. Embracing the idea of education as emancipatory (see
Section 5.3), as something that goes beyond training that which we already
know, implies we open up education and leave space for concerns and
development that is not determined by the educator or other people in power.
This could mean to leave spaces in the curriculum for students to define, or
even moving away from the idea to design education based on curricula.

Our analysis reveals that a large number of the articles are experience
reports rather than thorough research studies. The current state of research
on sustainability education in computing reminds of the state of computing
education in 2004. Most of the papers then were classified as “Marco-Polo
papers” (valentine_cs_2004, 220), work of the nature “I went there [e.g.
programming education, here computing education for sustainability] and I saw
this” [e.g. the students liked it]. Such work is of value for sharing
experiences among educators. Conducting more rigorous research could possibly
increase the value of the research by providing more trustworthy evidence.
Setting up standards for research could help but also limit creativity and
experimentation. As argued before when discussing curricula for education,
standards for research could lock research to our contemporary system and
ideas of how to conduct relevant research. In any case, more empirical
research is needed, using a variety of empirical research methods. Funding for
such research is necessary. Section 6 provides quality attributes from the ACM
standard. For the experience report, the standard is less applicable so
developing standards might be useful. Inspirations could also be gained from
the larger field of computing education (fincher_robins_2019, 63) or from
research methods used in education (Cohen17researchMethodsEdu, 30). A new
standard for research on educational experiments and interventions that
assures quality and grants freedom would be valuable, i.e. guidelines for
experience reports.

With an understanding of education as emancipatory (see Section 4.3), the role
of research could be to capture personal growth or novel, more sustainable
ways of living, understanding, or relating to the world. Research here might
be more agile, adapting methods and theories to what emerges from education
practices. Creative empirical methods (e.g. (Kara20researchMethodsCreative,
91)) might also be a source of inspiration. Another step forward could be the
collaboration with like-minded people from other disciplines and external
stakeholders, which is already used as a pedagogical approach (Section 5.3).
As many have argued, we need to cross disciplines and benefit from
interrelationships, rather than sticking to silo thinking.

###  7.2. Threats to validity

For this section we considered various classic sources, e.g., (Kitchenham2002,
94, 229); however, as observed by Ampatzoglou et al. (TTV-SLR-2019, 5), there
is no standard way of writing about the threats to validity, in general and
specifically for SLRs, but it is common to include: construct validity,
reliability, internal validity and external validity, according to the
guideline proposed by (Runeson2008, 182, 230), which we adopted in the
following.

Construct validity: This concerns the correct data collection and the correct
measurement of the theoretical concepts (Easterbrook2008, 50). The authors are
all experts in the field of the study. While their background knowledge was
used to shape the study design (and this might have introduced strong biases),
we both defined an explicit protocol following the customary guidelines in the
field, and refined it in a number of iterations to ensure a thorough research
process.

Reliability: This concerns whether the study is replicable and extensible by
other researchers. We carefully documented the overall structure of our study
in an explicit protocol, including the research questions, criteria for
inclusion/exclusion, the search string, and the snowball process. In
particular, inclusion and exclusion of primary studies are essential
components of our research protocol. Therefore, we assigned two researchers to
look at each paper; each gave a relevance score independently. In the case of
disagreement, a third researcher looked at the paper so that consensus could
be achieved. Finally, the whole group did the snowballing. For each paper, one
group member identified the relevant papers. Then, a different member analysed
the accepted papers, validating their relevance according to the inclusion and
exclusion criteria. Again, in case of disagreement, a third member was
involved.

Internal validity: This concerns whether researchers interpret the data
correctly. Accordingly, one possible threat is misinterpretations of terms
used in papers that were collected. To mitigate it, we discussed each finding
in groups of at least three researchers and had weekly meetings for discussion
during the past two years. We have a strong background in this domain. The
researchers come from five different European countries and nine different
universities. We have different experiences within the fields of
sustainability, computing and education. This background gives us diverse
perspectives thanks to our international origin.

External validity: This concerns how well the study is generalizable. Since
SLRs collect papers based on a search string, any papers that meet the search
string are included; bias can pertain the search string itself and the
snowball activity. We discussed the search string among all authors to
mitigate this threat. After several interactions of rectifying to include as
many relevant papers as possible and avoid biases like culture and sex, we
have obtained the status quo regarding sustainability education in software
engineering. We also had a few papers we knew should be found by the search.
This was one of the reasons we also included the education workshop ICT4S
since that contained additional paper that we did not find in the four
databases and was relevant for our study. It is possible that we have overseen
papers in the snowball process, given that it was done manually. However, we
tried to avoid this from happening as much as possible by having a rigours
process. We all participated in this process that lasted 22 months, where we
met regularly every week to work together and discuss our findings.

##  8\. Conclusion

This paper presents the state of the art on conceptions, implementations, and
research on sustainability education in computing. Educators and researchers
can use them to position their own work, e.g. on how they conceive of
sustainability as well as computing and education for sustainability. The
framework presented in Section 5 captures different aspects of current
implementations, such as learning objectives and outcomes as well as
pedagogical approaches. We find some evidence that sustainability education is
appreciated by the students, they become aware, concerned, and gain
competencies. We were able to map observed learning outcomes to the identified
learning objectives. We have also described other outcomes, suggesting that
sustainability education is about more than learning but change, e.g. of
computing and education practices.

We find 90 articles describing sustainability education in computing, but the
work that engages with the severity and complexity of challenges and the
dysfunctioning and violence of our current system or ways of organising life
is rather scarce. Adding sustainability as a topic as other topics is
inappropriate. We need to transform education and explore the potential of
education and computing in a process in which new practices and processes
evolve. Developing sustainability education in computing needs to take
inspiration from various fields of knowledge, especially critical studies of
technology and sustainability education. Education should not be reduced to
acquiring competence but can be seen to be about contributing to democratic
change and learning as a part of the process.

A condition for an education that takes responsibility is an affirmative or
caring orientation to the world and the future. This would mean to challenge
and change norms and values in computing education that encourage individuals
to engage in computing as a purely technical, abstract and rational
undertaking. Though understanding technical underpinnings and development
trajectories of computing are relevant, reproducing or continuing on them
might not be. We are missing connection to previous work on gender and under-
representation that help to understand how norms and values are reproduced in
power structures. Arguing for the potential of education for novelty and hope,
we pose the open question of how computing education can orient itself more
towards emancipation, based on care, experimentation, critical thinking and
reflection.

Our times are characterized by trouble, and equally “by ingenuity and
exploration, by invention and reinvention of old ideas”
(facer_storytelling_2019, 61). We are living in exciting times
(nicholas21_howToBeHuman, 136), in which we must accomplish unprecedented
change, setting stop not only to emissions but to violence that is built into
our modern ways of being. Sustainability education still is somewhat of a
niche activity within computing education, which urgently needs to change.
Knowledge and experience has been published and exists in all of us, educators
and students, and needs to grow in collaboration with communities and
organizations outside the university.

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## Appendix

Table 6. List of Primary Studies: by publication year, with Venue types ([J]ournal, [C]onference, [W]orkshop, or [O]ther formats) and the main Venue Focus ([E]ducation, [C]omputing, [S]ustainability)  |  | Type of Venue | Venue Focus
---|---|---|---
Ref. | Year | J | C | W | O | E | C | S
(IJSHE-5, 44) | 2002 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-30, 204) | 2007 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-34, 81) | 2007 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-48, 123) | 2007 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(ACM-14, 121) | 2008 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-163, 205) | 2009 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-21, 120) | 2009 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-70, 132) | 2009 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(ACM-12, 25) | 2010 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-88, 66) | 2010 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-Penz2011CSEET, 156) | 2011 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-171, 113) | 2011 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-35, 104) | 2011 | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$
(SB-99, 227) | 2011 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(ACM-11, 154) | 2012 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-07, 178) | 2012 | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-Penz2013cseet, 157) | 2013 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$
(SB-03, 70) | 2013 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-16, 224) | 2013 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-57, 149) | 2013 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IJSHE-13, 232) | 2014 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-147, 218) | 2014 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-165, 112) | 2014 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(ACM-10, 1) | 2014 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-04, 105) | 2014 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-40, 49) | 2014 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$
(SB-45, 55) | 2014 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$
(SB-69, 86) | 2014 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IJSHE-6, 193) | 2015 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IJSHE-21, 181) | 2015 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(Scopus-1, 47) | 2015 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(IEEE-145, 88) | 2015 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(SB-01, 144) | 2015 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-31, 119) | 2015 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$
(IEEE-161, 233) | 2016 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(IEEE-172, 171) | 2016 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-05, 116) | 2016 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$ | $\blacksquare$
(SB-14, 97) | 2016 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-19, 150) | 2016 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(SB-64, 151) | 2016 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-75, 57) | 2016 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-92, 109) | 2016 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(WOS-5, 117) | 2017 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(IEEE-153, 169) | 2017 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(ACM-07, 209) | 2017 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-32, 96) | 2017 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(IJSHE-11, 79) | 2018 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(WoS-2, 146) | 2018 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(IEEE-167, 213) | 2018 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(ACM-06, 158) | 2018 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(SB-77, 53) | 2018 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(IJSHE-10, 133) | 2019 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(CompSusEd-01, 54) | 2019 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(CompSusEd-02, 219) | 2019 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(CompSusEd-03, 106) | 2019 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-170, 16) | 2019 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(IEEE-175, 170) | 2019 | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$ | $\square$ | $\square$
(SB-08, 200) | 2019 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-15, 168) | 2019 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-33, 125) | 2019 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-44, 23) | 2019 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$
(SB-62, 56) | 2019 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$
(SB-68, 214) | 2019 | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$
(IJSHE-8, 172) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IJSHE-12, 24) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IJSHE-26, 8) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(WoS-1, 126) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(ACM-01, 59) | 2020 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$
(SB-29, 90) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-46, 102) | 2020 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(SB-53, 201) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-54, 103) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-56, 4) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(SB-71, 128) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-82, 27) | 2020 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-55, 202) | 2021 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\square$ | $\square$ | $\blacksquare$
(SB-79, 127) | 2021 | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$
(SB-89, 199) | 2021 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(ACM-New-1, 71) | 2021 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(ACM-New-2, 187) | 2021 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-New-1, 28) | 2021 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$
(IEEE-New-2, 189) | 2021 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-New-3, 110) | 2020 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-New-4, 137) | 2020 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(WoS-new-1, 162) | 2020 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(IEEE-156, 38) | 2010 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\square$
(SB-12, 89) | 2010 | $\blacksquare$ | $\square$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$
(IJSHE-7, 131) | 2010 | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\square$
(SB-87, 210) | 2010 | $\square$ | $\square$ | $\blacksquare$ | $\square$ | $\blacksquare$ | $\blacksquare$ | $\square$
(IEEE-164, 231) | 2010 | $\square$ | $\blacksquare$ | $\square$ | $\square$ | $\blacksquare$ | $\blacksquare$ | $\blacksquare$