Informatics in Education

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.

This research discusses the use of a gamified web platform for studying software modeling with Unified Modeling Language (UML). Although UML is constantly being improved and studied, many works show that there is difficulty in teaching and learning the subject, due to the complexity of its concepts and the students' cognitive difficulties with abstraction. There are challenges for instructors to find different pedagogical strategies to teach modeling. The platform proposed allowed students to complement their UML knowledge in an environment with game elements. From the results, it can be concluded that the platform obtained great acceptance and satisfaction of use. Most of the students participating in the research were satisfied with the usability of the platform, reporting a feeling of contribution of the tool to studying the content, in addition to pointing out the satisfaction of using gamification as a pedagogical strategy.

Nowadays, the rapid development of ICT has brought more flexible forms that push the boundaries of classic teaching methodology. This paper is an analysis of online teaching and learning forced by the COVID-19 pandemic, as compared with traditional education approaches. In this regard, we assessed the performance of students studying in the face-to-face, online and hybrid mode for an engineering degree in Computer Science at the Lublin University of Technology during the years 2019-2022. A total of 1827 final test scores were examined using machine learning models and the Shapley additive explanations method. The results show an average increase in performance on final tests scores for students using online and hybrid modes, but the difference did not exceed 10% of the point maximum. Moreover, the students' work had a much higher impact on the final test scores than did the study system and their profile features.

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.

In Education 4.0, a personalized learning process is expected, and that students are the protagonist. In this new education format, it is necessary to prepare students with the skills and competencies of the 21st-Century, such as teamwork, creativity, and autonomy. One of the ways to develop skills and competencies in students can be through block programming, which can be used with emerging technologies such as robotics and IoT and in an interdisciplinary way. Thus, block programming in High School is important because it is possible to work on aspects such as problem-solving, algorithmic thinking, among other skills (Perin et al., 2021), which are necessary in the contemporary world. Thus, our Systematic Mapping Study (SMS) aims to identify which block programming tools support of Education 4.0 in High School. Overall, 46 papers were selected, and data were extracted. Based on the results, a total of 24 identified block programming tools that can be used in high school collaboratively and playfully and with an interdisciplinary methodology. Moreover, it was possible to see that most studies address block programming with high school students, demonstrating a lack of studies that address block programming with teachers. This SMS contributed to identifying block programming tools, emerging technologies, audience (teacher or student), and learning spaces where block programming is being worked on.

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.

Computational Thinking (CT) has emerged in recent years as a thematic trend in education in many countries and several initiatives have been developed for its inclusion in school curricula. There are many pedagogical strategies to promote the development of elementary school students’ CT skills and knowledge. Unplugged learning tasks, block-based programming projects, and educational robotics are 3 of the most used strategies. This paper aimed to analyze the effect of Scratch-based activities, developed during one scholar year, on the computational thinking skills developed and concepts achieved by 4th-grade students. The study involved 189 students from two school clusters organized into an experimental group and a control group. To assess students’ computational knowledge, the Beginners Computational Thinking Test developed by Several Zapata-Cáceres et al. (2020) was used. The results indicate statistically significant differences between the groups, in which students in the experimental group (who performed activities with scratch) scored higher on the test than students in the control group (who did not use Scratch).

Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.

Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.

In a previous publication we examined the connections between high-school computer science (CS) and computing higher education. The results were promising—students who were exposed to computing in high school were more likely to take one of the computing disciplines. However, these correlations were not necessarily causal. Possibly those students who took CS courses, and especially high-level CS courses in high school, were already a priori inclined to pursue computing education. This uncertainty led us to pursue the current research. We aimed at finding those factors that induced students to choose CS at high school and later at higher-education institutes. We present quantitative findings obtained from analyzing freshmen computing students' responses to a designated questionnaire. The findings show that not only did high-school CS studies have a major impact on students’ choice whether to study computing in higher education—it may have also improved their view of the discipline.