Ana Sovic Krzic

The rapid growth of programming education and online learning environments has increased the demand for scalable and reliable assessment methods. Although many automated grading approaches exist, the literature spans traditional test-based methods and recent solutions based on artificial intelligence, making it difficult to obtain a coherent overview of the field. This study conducts a systematic literature review on automated programming assessment, analysing grading approaches, the use of artificial intelligence techniques, educational contexts, and comparisons between automated and human grading. The review synthesises evidence from 39 primary studies to identify technological trends and research gaps. The results show a shift from execution-based grading toward deep learning and large language model approaches, with most systems applied in higher education and limited research on visual programming assessment. Automated grading is evolving into an intelligent educational support tool, but challenges remain with regard to reliability and pedagogical impact. Future work should prioritise empirical validation, hybrid grading models, and broader educational applications.

RoboCupJunior is a project-oriented competition for primary and secondary school students that promotes robotics, computer science and programing. Through real life scenarios, students are encouraged to engage in robotics in order to help people. One of the popular categories is Rescue Line, in which an autonomous robot has to find and rescue victims. The victim is in the shape of a silver ball that reflects light and is electrically conductive. The robot should find the victim and place it in the evacuation zone. Teams mostly detect victims (balls) using random walk or distant sensors. In this preliminary study, we explored the possibility of using a camera, Hough transform (HT) and deep learning methods for finding and locating balls with the educational mobile robot Fischertechnik with Raspberry Pi (RPi). We trained, tested and validated the performance of different algorithms (convolutional neural networks for object detection and U-NET architecture for sematic segmentation) on a handmade dataset made of images of balls in different light conditions and surroundings. RESNET50 was the most accurate, and MOBILENET_V3_LARGE_320 was the fastest object detection method, while EFFICIENTNET-B0 proved to be the most accurate, and MOBILENET_V2 was the fastest semantic segmentation method on the RPi. HT was by far the fastest method, but produced significantly worse results. These methods were then implemented on a robot and tested in a simplified environment (one silver ball with white surroundings and different light conditions) where HT had the best ratio of speed and accuracy (4.71 s, DICE 0.7989, IoU 0.6651). The results show that microcomputers without GPUs are still too weak for complicated deep learning algorithms in real-time situations, although these algorithms show much higher accuracy in complicated environment situations.

Background: With the advancement of technology and its’ potential as a tool in educational activities, the transformation of the ways teachers organize and teach in modern classrooms was imposed. Yet, learning new technologies and developing pedagogical activities that incorporate such tools could be a challenge for teachers. Because of the different background knowledge of the participants in this study and also their students, the traditional ex-cathedra approach might not be suitable to promote quality progression, facilitate experimentation and interestingness with the platform, and maintain interestObjective: This innovative practice full paper presents a user experience and usability study targeting teachers. The first aim of the study was to investigate the personal experience of teachers and reveal issues associated with the utilization of the platform and materials. On the other hand, the second objective was related to the practical applicability of such in their everyday teaching. Teachers’ feedback and recommendations are important for the improvement of the materials to fit the needs of the classroom and to make use of the full potential a platform offers.Methods: Data collection was conducted during the workshop utilizing an emerging developing microcontroller, VIDI X. VIDI X is a new educational tool that can have ample uses in technology education as a multifunctional microcontroller, a developing board, or as a cluster hub with multiple sensors. The workshop aimed to familiarize teachers with the new learning tool and propose content and pedagogy that could be adapted to the classroom environment. They constituted a basic introduction to electronic components and programming in Arduino IDE. The instructional methodology for the workshop was based on instructional scaffolding. The workbook was organized into lessons with gradually increasing task difficulty. The educators provided instructions when needed to each participant individually. To evaluate the content of the workshop and the microcontroller, a questionnaire was administered immediately after the workshop. The questionnaire comprised: 1) participants’ background information, 2) content of the workshop, and 3) VIDI X. User Experience Questionnaire (UEQ) that covers attractiveness, pragmatic, and hedonic quality was utilized to appraise VIDI X.Results: The analysis of the workshop questionnaire showed that the teachers’ liked the workshop and the workbook. Furthermore, the microcontroller and the workbook are suitable for high school students. The analysis of the UEQ showed that the VIDI X was perceived positively on all items, and scales compared to the UEQ benchmark.Conclusion: As today’s students have different prior knowledge in the field of programming, electronics and robotics, the proposed workshop where everyone is progressing at their own pace accompanied with the support of educators, seems to be a good approach to educating in the future.

The quality of a teaching method used in a classroom can be assessed by observing the facial expressions of students. To automate this, Facial Expression Recognition (FER) can be employed. Based on the recognized emotions of students, teachers can improve their lectures by determining which activities during the lecture evoke which emotions and how these emotions are related to the tasks solved by the students. Previous work mostly addresses the problem in the context of passive teaching, where teachers present while students listen and take notes, and usually in online courses. We take this a step further and develop predictive models that can classify emotions in the context of active teaching, specifically a robotics workshop, which is more challenging. The two best generalizing models (Inception-v3 and ResNet-34) on the test set were combined with the goal of real-time emotion prediction on videos of workshop participants solving eight tasks using an educational robot. As a proof of concept, we applied the models to the video data and analyzed the predicted emotions with regard to activities, tasks, and gender of the participants. Statistical analysis showed that female participants were more likely to show emotions in almost all activity types. In addition, for all activity types, the emotion of happiness was most likely regardless of gender. Finally, the activity type in which the analyzed emotions were the most frequent was programming. These results indicate that students’ facial expressions are related to the activities they are currently engaged in and contain valuable information for teachers about what they can improve in their teaching practice.

Contribution: Understanding pupils’ conceptualization of robots and programming can help teachers to avoid a disconfirmation experience by selecting more appropriate educational tools, robot designers in improving the robot design, and researchers in further improvement of the field. Background: Human–robot interaction (HRI) is affected by the actual but also expected robot’s appearance and capabilities. Multiple factors, such as age, gender, media exposure, ICT exposure, or culture, influence mental models regarding robots; therefore, it is important to investigate those for a specific cohort in the designated geographical area. Research Questions: The mental models regarding robots and programming, and the way they are possibly biased by popular culture, exposure to ICT or parental influence were studied. Research questions concerned cognitive elements of mental models, namely, definitions and knowledge of robots, and programming and how those progress in time. Also, the research studied a figurative aspect of mental models regarding robots, with a focus on anthropomorphic features. Methodology: To research the influence of the short-term HRI, four classes of eight to nine years old elementary school pupils were included in a workshop where pre and postquestionnaires were used as research instruments. Besides pupils, later in this two-phase longitudinal research, after a year of formal education in Informatics, research instruments also included teachers and parents, to investigate their influence on children’s mental models. Findings: The change of mental models under the influence of the one-time workshop was not permanent. However, a combination of maturation with informal and formal intervention supports the conceptualization of programming.