Informatics in Education

Nowadays, few professionals understand the techniques and testing criteria to systematize the software testing activity in the software industry. Towards shedding some light on such problems and promoting software testing, professors in the area have established Massive Open Online Courses as educational initiatives. However, the main limitation is the professor’s lack of supervision of students. A conversation agent called TOB-STT has been defined in trying to avoid the problem. A previous study introduced TOB-STT; however, it did not analyze its efficacy. This article addresses a controlled experiment that analyzed its efficacy and revealed it was not expressive in its current version. Therefore, we conducted an in-depth analysis to find what caused this result and provided a detailed discussion. The findings contribute to the TOB-STT since the experimental results show that improvements need to be made in the conversational agent before we use it in Massive Open Online Courses.

The purpose of this study is to reveal the status of scientific publications on learning analytics from the past to the present in terms of bibliometric indicators. A total of 659 publications on the subject between the years 2011-2021 were found in the search using keywords after various screening processes. Publications were revealed through descriptive and bibliometric analyses. In the study, the distribution of publications by years and citation numbers, the most published journals on the subject, the most frequently cited publications, the most prolific countries, institutions and authors were examined. In addition, the cooperation between the countries, authors and institutions that publish on the subject was mentioned and a network structure was created for the relations between the keywords. It has been determined that research in this field has progressed and the number of publications and citations has increased over the years. As a result of the bibliometric analysis, it was concluded that the most influential countries in the field of learning analytics are the USA, Australia and Spain. The University of Edinburgh and Open University UK ranked first in terms of the number of citations and Monash University as the most prolific institutions in terms of the number of publications. According to the keyword co-occurrence analysis, educational data mining, MOOCS, learning analytics, blended learning, social network analysis keywords stand out in the field of learning analytics.

Problem-solving and critical thinking are associated with 21st century skills and have gained popularity as computational thinking skills in recent decades. Having such skills has become a must for all ages/grade levels. This study was conducted to examine the effects of grade level, gender, chronotype, and time on computational thinking skills. To this end, the study was designed to follow a longitudinal research model. Participants were 436 secondary school students. Computational thinking test scores were collected from the students at certain time intervals. Results indicate that computational thinking skills are independent of gender, time, and chronotype but differ significantly depending on grade level. The interaction between grade level and time of testing also has a significant impact on computational thinking skills. The difference in grade level can be interpreted as taking an information technologies course increases computational thinking. The results suggest that such courses should be promoted to children at a young age. The joint effect of gender, grade level, and chronotype were not statistically significant and it is recommended to conduct future studies to investigate this result.

User-centricity and usability are a premise of digitalization, a current trend for business model innovation based on advanced digital technologies. The article addresses a gap in the literature, in which descriptions of the cases of updating university curricula in usability are lacking. This gap also exists in the practice. The study uses the example of a project for revising the content of usability courses at the University of Turku as a case. The research objective is to explore an integrative approach to usability education. For this, we consider the data collected via interviews with the faculty teaching usability subjects. Thematic analysis is applied to examine the interview outcomes. Recommendations as to updating usability curricula are provided.

This study aims to provide a deeper understanding about the Bebras tasks, which is one of the computational thinking (CT) unplugged activities, in terms of age level, task category, and CT skills. Explanatory sequential mixed method was adopted in the study in order to collect data according to the research questions. The participants of the study were 113,653 school students from different age levels. Anonymous data was collected electronically from the Turkey 2019 Bebras challenge. Factor analysis was employed to reveal the construct validity to determine how accurately the tool measured the abstract psychological characteristics of the participants. In addition, the item discrimination index was calculated to measure how discriminating the items in the challenge were. Qualitative data gathered through the national Bebras workshop was analysed according to content analysis. The findings highlighted some interesting points about the implications of the Bebras Challenge for Turkey, which are discussed in detail. Furthermore, common problems of Bebras tasks are identified and possible suggestions for improvement are listed.

The new Croatian Informatics curriculum, which introduces computational thinking concepts into learning outcomes has been put into practice. A computational thinking assessment model reflecting the learning outcomes of the Croatian curriculum was created using an evidence-centered design approach. The possibility of assessing the computational thinking concepts, abstraction, decomposition, and algorithmic thinking, in an actual classroom situation and examples of such assessment is increasingly coming to the forefront of computer science educational research. Precisely for that purpose, the research was conducted. Research data are collected through the test and questionnaire of 407 pupils (10 middle schools, age 12), analysed by exploratory factor analysis and non-parametric tests. Results showed that the presented model was suitable to assess the understanding of the concepts of abstraction and algorithmic thinking, independently of the previous experience with programming languages and pupil's gender, while assessment of decomposition needs more work and improvement, some recommendations are provided. Also, it received positive feedback from pupils and teachers what implicated that such an assessment model could help teachers in building a real-time measurement instrument.

Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers.

Teaching algorithmic thinking enables students to use their knowledge in various contexts to reuse existing solutions to algorithmic problems. The aim of this study is to examine how students recognize which algorithmic concepts can be used in a new situation. We developed a card sorting task and investigated the ways in which secondary school students arranged algorithmic problems (Bebras tasks) into groups using algorithm as a criterion. Furthermore, we examined the students’ explanations for their groupings. The results of this qualitative study indicate that students may recognize underlying algorithmic concepts directly or by identifying similarities with a previously solved problem; however, the direct recognition was more successful. Our findings also include the factors that play a role in students’ recognition of algorithmic concepts, such as the degree of similarity to problems discussed during lessons. Our study highlights the significance of teaching students how to recognize the structure of algorithmic problems.