Computational thinking is a way of thinking about the world in terms of data. This mindset recasts ideas or problems as quantification tasks, in which data, theory, models and simulations are combined creatively to achieve quantitative answers (Weintrop et al. 2016; Cramer et al. 2018 ). Computational thinking skills include: (i) defining a quantifiable problem (e.g. how quickly can a virus spread?), (ii) using logic reasoning for mapping the problem in a computable model of reality (e.g. using data and theory for simulating how individuals can get infected), (iii) computing the answer (e.g., each infected person can infect three more, on average), and (iv) interpreting the results (e.g., the virus spreads faster than the flu). Computational thinking is a challenging way of understanding our world, requiring creativity, expertise, data availability and knowledge. Computers are not essential for the easier tasks, but prove invaluable for analysing large volumes of data or testing theoretical predictions by coding simulations (e.g., NASA's picture of a black hole required processing 5 petabytes of data).
As a way of perceiving and linking different elements of reality, i.e. a mindset, a definition of computational thinking grounded in cognitive science remains an open question. Identifying the presence of this mindset is key in our society, which increasingly depends on data processing (e.g., for simulating virus spreading) and requires more and more computational skills that are in high demand in the job market (OECD, 2019).
The attitude toward the subject is an important part of mindsets. Studies show that while mathematical knowledge could be more highly related to short-term outcomes, such as achievement in a statistics course (Beurze et al., 2013; Chiesi & Primi, 2010) , the attitudes toward statistics are related to long-term outcomes, such as dissertation grade (Vanhoof et al., 2006) . However, other studies show that some components of these attitudes have a strong relationship even with short-term results (Dempster & McCorry, 2009 ; Emmioglu & Capa-Aydin, 2012). More complex models suggest that components of attitudes, such as interest and value, could affect others, such as effort and expectancy for success, that, in turn, are directly related to subject achievements (Ivaniushina et al., 2016; Ramirez et al., 2012) .
In general, computational thinking is "the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer-human or machine-can effectively carry out" (Wing, 2014) . There is no consensus about an exact definition, but in all approaches it is broader than counting skills and also includes the ability to think logically and abstractly and use problem-solving skills (see Wing, 2006 This paper moves a step forward toward investigating the presence and structure of a computational thinking mindset by using the framework of forma mentis networks (FMNs), i.e. knowledge graphs that map conceptual knowledge and sentiment as perceived by individuals ). In computational cognitive science, knowledge graphs map conceptual associations (Popping, 2003) , including a wide variety of memory recall or semantic links between concepts and information (e.g. Siew 2019). In addition to knowledge structure, the positive/negative sentiment attributed to each concept also plays a fundamental role in thought processes (Kuperman et al. 2013 ). Forma mentis (Latin for "mindset") networks combine these elements in order to reconstruct the semantic frame (Fillmore, 2006) , or meaning, and emotions attributed by individuals to concepts, thus reconstructing a networked model of their mindset .
In this study we examine the representation of key concepts of data science and computational thinking in experts' and students' mindsets. Based on the literature, several key concepts were chosen. Firstly, such general concepts as "computer science" and "computers" were used. Second, one of the important components of computational thinking -"logical reasoning" -was considered " simulation", "data" and "code" were highlighted.
Herein we focus on detecting traces of computational thinking patterns in the mindsets of two populations: (i) 159 high school students enrolled in a STEM-focused curriculum, and (ii) 59 STEM researchers working on modelling complex systems. STEM experts' mindset is seen to be fully aligned with key aspects of computational thinking as identified by previous studies, namely awareness about logic reasoning; a positive attitude toward data, simulations and models; and the ability to frame coding as a tool for achieving new knowledge.
Students' mindset was quite different. Although they are aware of logic reasoning, students framed "model" as vaguely related to computing. They also completely missed any link between "simulation" and computation, which was perceived as an anxiety-eliciting construct. We interpret this lack of perception as a partially undeveloped computational mindset, possessing fundamental tools for logic reasoning but lacking the "sense of purpose" that psychologically drives computational thinking with a need to solve a challenge for understanding our world. Restoring this purpose in students, as it is in researchers, is key for educational policies promoting computational literacy among high school students and teachers.
The analysis presented here is based on data collected by Stella textual forma mentis networks (TFMNs). The latter reconstruct knowledge from text analysis and hence do not pass through behavioral tasks as BFMNs do. Both BFMNs and TFMNs are forma mentis networks (FMNs), in that they combine conceptual knowledge and affect patterns. BFMNs include subjective norms for sentiment, as concepts are also rated as "positive", "negative" or "neutral" by individuals. Combining knowledge structure and affective patterns, forma mentis networks identify how concepts are perceived in a given set of individuals or populations. By cross-validation with independent affective norms, words mostly surrounded by negative associates in BFMNs were also found to correspond to anxiety-eliciting concepts, including STEM anxiety (an increased alertness inspired by facing STEM problems) and test anxiety (a feeling of stress/tension felt before or during a test/quiz/exam). For visualisation purposes, in the following plots all Italian words were translated from Italian to English.
Forma mentis networks give structure to the way concepts are semantically framed and perceived.
The following analysis focuses on semantic frames (Fillmore, 2006) , which correspond to the semantic associations attributed by individuals to a given concept in the free association game. The sentiment data included in an FMN enable also the identification of positive/negative/neutral perceptions pervading a given semantic frame and thus altering the connotation or aura attributed to a given concept through its associations. Figure 1 compares the reconstructed mindset around "model", with high-schoolers associating it mostly with jargon about fashion, role-models and a few scientific terms.
In their mindset, STEM experts strongly associate "model" with computational thinking, research and experimenting.
[HERE FIGURE 1] Figure 2 reports students' perceptions toward these concepts, including methodological aspects such as "code" and one of the main subjects involved in computing, i.e. "computer science" (for other computational sciences we refer the interested reader to ).
Students perceived "computation" as a negative concept, surrounded by a negative emotional aura of negative associates. Negative auras are relative to concentrations of negative valence, which in turn elicit strong anxiety and stress, such as students', "computation"-elicited anxiety. Taking a closer look, this negative aura emerges mainly from algorithmic concepts, the perception of which might be dry and detached from their effective relevance in real-world applications of calculations. This pattern is further indicated by the only positive associations being examples of real-world systems related to calculations, i.e. brain, power, logic and reasoning. Students also provided a rich number of semantic associations to "computer science" and "code", which were distinctly perceived as neutral, and thus different from the mathematical jargon reported in "computation". This sharp emotional difference might indicate that students perceive "code" to be a different entity compared to dry mathematical jargon.
[HERE FIGURE 2] There are also positive patterns. Students' reconstructed mindsets around "logic reasoning", a key Computational thinking fundamentally relies on simulations, data and, to a lesser extent, computers (Weintrop et al. 2016) . Figure 3 reports students' and researchers' perceptions of these concepts. For students, "simulation" reminds them only of exams, grades and related anxiety-eliciting concepts (e.g. a simulation of a quiz). This obliviousness to computational concepts indicates a deep lack of awareness about computer simulations, which by comparison are prominently featured in STEM experts' mindset.
[HERE FIGURE 3] As compared with "code", students perceived "data" as a negative entity, mainly linked to statistics, a concept eliciting statistics anxiety in the sampled student population (see ). Notice how in students' reconstructed mindset, "simulation" was not associated with "data", whereas this was a positive and strong link in researchers' networked mindset.
Last but not least, forma mentis networks indicate that students perceived a computer as a technological device for surfing the web and doing calculations, whereas for researchers a computer was an instrument for exploring the science of natural systems through models and simulations. The absence of "model" and "simulation" in the students' mindset suggests a limited perception in students, who see only the computation aspects of computers, i.e. crunching the numbers without understanding their relevance to the real world.
Characterising the cognitive footprint of a computational thinking mindset is urgently needed for devising effective educational policies promoting data literacy skills of relevance for future job prospects (OECD, 2019) and for fighting "functional illiteracy", a concerning inability for literate people The investigated population of students possessed advanced training in STEM subjects and was trained in basic numerical simulations. Despite this, students' computational thinking exhibited awareness of the knowledge behind logic reasoning but it identified "computing" as a negative, anxiety-eliciting concept, and completely missed any link between "simulation", "model" and computing itself. Students' semantic frame of "computing" portrayed a perception equivalent to mere number crunching, without associations linking computing to understanding the real world. However, these conceptual associations were prominently detected in the forma mentis network of STEM experts.
Strongly different semantic frames were also found with "model" and "simulation". In researchers, "model" was semantically framed as a key tool for research. In the students' FMN, "model" was semantically framed mostly as related to role-models or fashion runaways, and it elicited only a few scientific associations. This difference suggests that a lack of computational thinking might be related Model-facilitated learning enables students to gradually explore problems from an active perspective. Aided by a model, students can identify the structure of a given problem, making it progressively more complicated and tackling the challenge of determining whether predicted outcomes occur. In this way, models can provide a learning experience more similar to actual research than that usually found in mainstream classroom teaching and can be beneficial for fostering computational thinking. Recent approaches such as the one by Sabitzer and colleagues (2020) identified modelling as a key approach for learning computational skills in primary and secondary school, integrating computational thinking with everyday situations and challenges.
Models enable the exploration of how different choices or initial conditions influence a certain problem, mainly through computer simulations. The concept of "simulation" features the most dramatic differences between students' and researchers' FMNs. No association reported in the students' FMN framed simulation as a computer experiment. Instead, "simulation" for students was associated mainly with tests and quizzes and heavily subject to test anxiety (cf. Stella 2020), an abnormally high level of distress perceived before or during exams that critically impairs academic performance. We argue that the utter lack of any computational association with "simulation" is an important indicator of how students are learning logic reasoning and mathematical skills without learning the importance of simulating processes or problems. A crippled or missing awareness of simulations is highly problematic for STEM learning. The principle of problem centeredness in science learning clearly indicates that learning can be more effective when learners are engaged in solving real-world problems (Merrill 2002) . Since simulations implement an exploration of real-world problems that cannot be often achieved by theoretical formulas or equations, preventing students from learning about simulations (and modelling) can greatly decrease the effectiveness of science learning and thus hamper the development of computational thinking. Notice that this problem afflicted students' mindset but not researchers'. The additional university training undertaken by the latter evidently promoted the knowledge necessary for bringing simulations, models and logic reasoning together as ways for exploring and understanding real-world problems, leading to the researchers' virtuous mindset as represented in their forma mentis network.
All in all, our cognitive map indicates an incomplete computational thinking mindset in students, lacking conceptual links between computational tools and knowledge discovery that here were identified in researchers, instead. The absence of these links suggests a lack of motivation or "sense of purpose" in the way students perceive computation. Let us briefly focus on this (lack of) motivation.
In cognitive psychology, the concept of "sense of purpose" mostly relates to personal development and indicates an intention to explore and pursue meaningful achievements that bring satisfaction and fulfillment (Maslow 1970; Neto 2015) . In other words, a sense of purpose represents a motivation that drives people to engage with and act upon their surroundings in order to achieve something meaningful, productive and satisfying. Notice that even achieving a clearer understanding of how things work represents a satisfying achievement. Maslow's theory of motivation clearly indicates that stimulating a sense of purpose in students is one of the most powerful ways for enhancing learning and skill retention (Neto 2015) . Endowing students with a sense of purpose in the context of computational learning means channelling students' computational skills toward curiosity for achieving knowledge about their surrounding world. In this way knowledge represents grounding for computational curiosity, and the combination of the two can give rise to a fully formed computational thinking mindset. Although the methodology of forma mentis networks cannot establish a causal link, the observed lack of motivation could be a cause for the detected negative perception afflicting "computation" and other computing concepts in the students' mindset. In fact, several studies have shown that a lack of a "sense of purpose" can give rise to negative perceptions promoting boredom, 
It has to be stated that the above analysis suffers from some limitations. The most prominent one is related to the necessity of involving individuals in a cognitive lab experiment, which translates into smaller population sizes, especially for researchers. The recent development of textual forma mentis networks, meaning extracting mindsets from texts, could potentially address this limitation while enabling the analysis of large written corpora extracted from educational forums or textbooks. With the possibility of improving learners' experiences through digitally collected data from social forums (cf. Poquet et al. 2020) , the detection of computational thinking patterns starting from text would represent an interesting direction for future research (see also next section). Another limitation of this study is that it measured only semantic frames and sentiment patterns but it did not measure the concrete proficiency of students in computational tasks such as coding or simulating models.
Exploring the correlation between negative/positive attitudes toward computational thinking and both coding and simulating models stands as an important direction for future work.
To create an invitation to more diverse and equitable participation in computer science and participation in the data-driven workforce, there is an urgent need to contextualize and make computational thinking meaningful and purposeful to students throughout their scholastic career in order to have an impact on attitudes toward computer science. Helping learners bring meaning to the use of computers, coding, modeling and data by providing them with opportunities to use these techniques to address problems they care about and/or are important to their communities and society is going to be essential to overcoming the stigma of computer science and related subjects in and out of school. Forma mentis networks will allow controlled trials to get a sense of the impact of meaningful computation programs with students and teachers and can guide educators, administrators, policymakers and lifelong learners in creating a computational thinking future without fear.
As the COVID-19 pandemic demonstrates, the need to understand how data are used and interpreted by researchers and public health officials and are used to inform decisions made at all levels, from personal to policy, is essential to the security and wellbeing of all. And to achieve this understanding, there must be a requisite level of computational thinking. Data science -the science of how scientific, social and economic data are acquired, organized, visualized and analyzed -is revolutionizing all sectors of society while simultaneously affecting equity in the form of data haves and have-nots. The lack of computational skills, computational thinking, and skills in the use of the tools of data science (technologies to gather data from sources such as surveys, sensors, loggers, social media, GPS, open data sources and economic transactions, and the software and computer systems to analyze and visualize them), and the lack of understanding issues of equity, security, and privacy (how and why personal and social data are gathered, analyzed and distributed), impacts equity in employment and the innovation economy.