Informatics in Education

In today's world, the ability to think computationally is essential. The skillset expected of a computer scientist is no longer solely based on the old stereotype but also a crucial skill for adapting to the future. This perspective presents a new educational challenge for society. Everyone must have a positive attitude toward understanding and using these skills daily. One thousand two hundred seven documents about computational thinking (CT) may be found while searching the Scopus database from 1987 to 2023. Data from Scopus were analyzed using VOSviewer software. This study educates academics by delving into the fundamentals of what is known about the CT of visual and quantitative research skills. This approach allows for a more in-depth look at the literature and a better understanding of the research gap in CT. This bibliometrics analysis demonstrates that (1) research on CT is common to all sciences and will develop in the future; (2) the majority of articles on CT are published in journals in the fields of education, engineering, science and technology, computing and the social sciences; (3) the United States is the most dominant country in CT publications with a variety of collaborations; (4) keywords that often appear are CT, engineering, education, and mathematics, and (5) research on CT has developed significantly since 2013. Our investigation reveals the beginnings and progression of the academic field of research into CT. Furthermore, it offers a road map indicating how this study area will expand in the coming years.

With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.

This research investigates university students’ success in their first programming course (CS1) in relation to their motivation, mathematical ability, programming self-efficacy, and initial goal setting. To our knowledge, these constructs have not been measured in a single study before in the Finnish context. The selection of the constructs is in line with the statistical model that predicts student performance (“PreSS”) (Quille and Bergin, 2018). The constructs are compared with various demographic and background variables, such as study major, prior programming experience, and average weekly working hours. Some of the main results of this study are as follows: (1) students generally entered with a high interest in programming and high motivation, but these factors did not increase during the course, i.e., interest in programming did not increase. (2) Having prior experience yielded higher initial programming self-efficacy, grade expectations, and spending less time on tasks, but not better grades (although worse neither). While these results can be seen as preliminary (and alarming in some parts), they give rise to future research for investigating possible expectation–performance gaps in CS1 and later CS studies. As our dataset accumulates, we also hope to be able to construct a valid success prediction model.

Massive Open Online Courses (MOOCs) have become hugely popular recently. MOOCs can offer high-quality education for anyone interested and equalize the whole education field. Still, there are different methodologies for running MOOCs. Coming up with the most suitable methodology benefits both students and teachers. In this study, we have limited the methodological focus to observing scheduled and unscheduled instances of similar MOOC courses. While unscheduled MOOC courses can provide flexibility, they also require self-regulated learning strategies for students to succeed. To observe this, we compare the effectiveness of scheduled and unscheduled programming MOOC courses to find the most effective methodology. For this, we compare the pass rates and grade averages of five instances (two unscheduled and three scheduled) of Python and Java programming MOOCs. The results show that while the attendance numbers are higher in the unscheduled versions, in the scheduled instances the pass rate is significantly better, and students’ progression is much swifter. It also seems that the higher proportion of university students enrolled in a MOOC course positively affects the retention rate. Moreover, the students in the recent unscheduled Python version seem to score significantly higher grades than in its scheduled counterpart. Based on our experiments, the scheduled and unscheduled versions complement each other. Hence, we suggest that, whenever feasible, the maximal benefits would be gained if both types of MOOCs are run simultaneously.

Programs in bioinformatics, offered in many academic institutes, are assumed to expand women’s representation in computer science (CS). Women’s enrolment in these programs is high; Our questions are: Do these programs attract different women from those attracted to CS programs? What factors underlie women’s decision to enroll in bioinformatics programs? How do these factors differ from those of women who choose CS, if at all? What career opportunities do these women anticipate and pursue? Using questionnaires and interviews, we found a statistically significant difference between the factors that motivate women to choose bioinformatics and others to study CS. Many bioinformatics students did not consider CS as an alternative. Post-facto they learned to love computing, albeit with a biology-oriented purpose. “Computing with purpose” underlies many participants’ pursuit of careers in research, CS, and bio-tech. We thus conclude that bioinformatics programs do indeed expand women’s representation in CS.

Learning programming logic remains an obstacle for students from different academic fields. Considered one of the essential disciplines in the field of Science and Technology, it is vital to investigate the new tools or techniques used in the teaching and learning of Programming Language. This work presents a systematic literature review (SLR) on approaches using Mobile Learning methodology and the process of learning programming in introductory courses, including mobile applications and their evaluation and validation. We consulted three digital libraries, considering articles published from 2011 to 2022 related to Mobile Learning and Programming Learning. As a result, we found twelve mobile tools for learning or teaching programming logic. Most are free and used in universities. In addition, these tools positively affect the learning process, engagement, motivation, and retention, providing a better understanding, and improving content transmission.

While Internet of Things (IoT) devices have increased in popularity and usage, their users have become more susceptible to cyber-attacks, thus emphasizing the need to manage the resulting security risks. However, existing works reveal research gaps in IoT security risk management frameworks where the IoT architecture – building blocks of the system – are not adequately considered for analysis. Also, security risk management includes complex tasks requiring appropriate training and teaching methods to be applied effectively. To address these points, we first proposed a security risk management framework that captures the IoT architecture perspective as an input to further security risk management activities. We then proposed a hackathon learning model as a practical approach to teach hackathon participants to apply the IoT security risk management framework. To evaluate the benefits of the framework and the hackathon learning model, we conducted an action research study that integrated the hackathon learning model into a cybersecurity course, where students learn how to apply the framework. Our findings show that the IoT-SRM framework was beneficial in guiding students towards IoT security risk management and producing repeatable outcomes. Additionally, the study demonstrated the applicability of the hackathon model and its interventions in supporting the learning of IoT security risk management and applying the proposed framework to real-world scenarios.

This study aims to examine the impact of interdisciplinary computational thinking (CT) skills training on primary school teachers’ perceptions of CT skills. The sample of the study consisted of 30 primary school teachers in Istanbul. In this study, where quantitative and qualitative methods were used together, qualitative data were obtained from the teacher identification form. Quantitative data were obtained from the scale for CT skills. After the pre-test was applied to the study group, “CT Skills Training” was applied. During the training, the basic concepts of CT skills and the subskills were covered theoretically and practically. From the quantitative data, the education applied was determined to have had a positive effect on the primary school teachers' perceptions of CT skills. From the qualitative data, it was determined that the participants had a positive opinion about the applied training and thought that they gained skills related to CT.

Knowledge about Machine Learning is becoming essential, yet it remains a restricted privilege that may not be available to students from a low socio-economic status background. Thus, in order to provide equal opportunities, we taught ML concepts and applications to 158 middle and high school students from a low socio-economic background in Brazil. Results show that these students can understand how ML works and execute the main steps of a human-centered process for developing an image classification model. No substantial differences regarding class periods, educational stage, and sex assigned at birth were observed. The course was perceived as fun and motivating, especially to girls. Despite the limitations in this context, the results show that they can be overcome. Mitigating solutions involve partnerships between social institutions and university, an adapted pedagogical approach as well as increased on-by-one assistance. These findings can be used to guide course designs for teaching ML in the context of underprivileged students from a low socio-economic status background and thus contribute to the inclusion of these students.

The article describes a study carried out on pupils aged 12-13 with no prior programming experience. The study examined how they learn to use loops with a fixed number of repetitions. Pupils were given a set of programming tasks to solve, without any preparatory or accompanying instruction or explanation, in a block-based visual programming environment. Pupils’ programs were analyzed to identify possible misconceptions and factors influencing them. Four misconceptions involving comprehension of the loop concept and repeat command were detected. Some of these misconceptions were found to have an impact on a pupil’s need to ask the computer to check the correctness of his/her program. Some of the changes made to tasks had an impact on the frequency of these misconceptions and could be the factors influencing them. Teachers and course book writers will be able to use the results of our research to create an appropriate curriculum. This will enable pupils to acquire and subsequently deal with misconceptions that could prevent the correct understanding of created concepts.