The integration of artificial intelligence (AI) topics into K–12 school curricula is a relatively new but crucial challenge faced by education systems worldwide. Attempts to address this challenge are hindered by a serious lack of curriculum materials and tools to aid teachers in teaching AI. This article introduces the theoretical foundations and design principles for implementing co-design projects in AI education, empirically tested in 12 Finnish classrooms. The article describes a project where 4th- and 7th-graders (N = 213) explored the basics of AI by creating their own AI-driven applications. Additionally, a framework for distributed scaffolding is presented, aiming to foster children's agency, understanding, creativity, and ethical awareness in the age of AI.
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.
As our society has advanced in the era of digital transformation, education has been transformed from knowledge-centered to competency-centered to solve future problems in the light of unpredictable changes and events in our lives. Programming education provides the basic knowledge needed, and fosters higher-order thinking skills in the process of generating and converging ideas to solve problems. However, in Korean elementary schools, it is mostly based on a lecture-based instructional design and focuses on knowledge delivery, which has limited the educational effects of programming. However, productive failure (PF) focuses on learning concepts in authentic problems, and lets the students generate different solutions and discuss them in an acceptable environment, with the result that they fail to solve the problem. Therefore, this study developed a PF-based educational program and tested it on sixth-grade students in a Korean elementary school. The results showed that the computational thinking (CT) and creative problem-solving (CPS) skills of the experimental group were significantly greater than those of the control group, with a medium effect size for CT and a high effect size for CPS skills. To generalize the results and increase the applicability, follow-up studies should expand the subject of the study, develop specific teaching guidelines for teachers, and invent various learning problems appropriate to the students’ level and different domains of learning.
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 insertion of Machine Learning (ML) in everyday life demonstrates the importance of popularizing an understanding of ML already in school. Accompanying this trend arises the need to assess the students’ learning. Yet, so far, few assessments have been proposed, most lacking an evaluation. Therefore, we evaluate the reliability and validity of an automated assessment of the students’ learning of an image classification model created as a learning outcome of the “ML for All!” course. Results based on data collected from 240 students indicate that the assessment can be considered reliable (coefficient Omega = 0.834/Cronbach's alpha α=0.83). We also identified moderate to strong convergent and discriminant validity based on the polychoric correlation matrix. Factor analyses indicate two underlying factors “Data Management and Model Training” and “Performance Interpretation”, completing each other. These results can guide the improvement of assessments, as well as the decision on the application of this model in order to support ML education as part of a comprehensive assessment.
Teaching programming is a complex process requiring learning to develop different skills. To minimize the challenges faced in the classroom, instructors have been adopting active methodologies in teaching computer programming. This article presents a Systematic Mapping Study (SMS) to identify and categorize the types of methodologies that instructors have adopted for teaching programming. We evaluated 3,850 papers published from 2000 to 2022. The results provide an overview and comprehensive view of active learning methodologies employed in teaching programming, technologies, programming languages, and the metrics used to observe student learning in this context. In the results, we identified thirty-seven different ALMs adopted by instructors. We realized that seventeen publications describe teaching approaches that combine more than one ALM, and the most reported methodologies in the studies are Flipped Classroom and Gamification-Based Learning. In addition, we are proposing an educational and collaborative tool called CollabProg, which summarizes the primary active learning methodologies identified in this SMS. CollabProg will assist instructors in selecting appropriate ALMs that align with their pedagogical requirements and teaching programming context.
Contemporary society is characterized by diversity and intricacy, necessitating more meaningful learning experiences. To meet these evolving needs, the incorporation of computational systems into education must acknowledge the distinctive characteristics of learners. Therefore, we conducted a Systematic Mapping Study (SMS) to investigate technologies that support the Learner eXperience (LX) design in computational systems. LX refers to learners’ perceptions, reactions, and achievements while engaging with learning resources, encompassing digital games, simulations, and multimedia. The SMS results uncovered distinct LX design technologies, with a noticeable inclination towards learner-centric strategies. Interestingly, the results highlighted a scarcity of research targeting non-traditional learning environments (e.g., technical visits) and that facilitate interactions among learners beyond their own classmates (e.g., industry experts). In this way, the SMS contributes by revealing LX design technologies, LX design elements, relevant constructs/theories, computational systems, environments, contexts, and other related factors, thereby enhancing the understanding of optimal learning experiences within computational learning systems.
This study investigates the effect of programming courses on the computational thinking (CT) skills of elementary school students and the learning effectiveness of students from different backgrounds who are studying programming. We designed a OwlSpace programming course into an elementary school curriculum. Students in fourth and fifth grades were taught the fundamentals of programming. We measured and analyzed the effectiveness of their CT skills and self-efficacy in CT. The researchers analyzed the changes in the CT of different gender, different grade, and different past experience students in programming courses and then made specific recommendations for information technigy teachers and related units. The results demonstrate that students learned and improved their CT skills by taking OwlSpace programming course. Additionally, gender, grade, and past experience are found to have no impact on the students’ learning that means the course can improve students ability without limited any characteristics.
There is an increasing interest in the integration of computational thinking (CT) in the K-12 curriculum. By integrating CT into other disciplines, the aim is to equip students with essential skills to navigate domain-specific challenges. This study conducts a systematic review of 108 peer-reviewed scientific papers to analyze in which K-12 subjects CT is being integrated, learning objectives, CT integration levels, instructional strategies, technologies and tools employed, assessment strategies, research designs and educational stages of participants. The findings reveal that: (a) over two-thirds of the CT integration studies predominantly focus on science and mathematics; (b) the majority of the studies implement CT at the substitution level rather than achieving a transformation impact; (c) active learning is a commonly mentioned instructional strategy, with block-based languages and physical devices being frequently utilized tools; (d) in terms of assessment, the emphasis primarily lies in evaluating attitudes towards technology or the learning context, rather than developing valid and reliable assessment instruments. These findings shed light on the current state of CT integration in K-12 education. The identified trends provide valuable insights for educators, curriculum designers, and policymakers seeking to effectively incorporate CT across various disciplines in a manner that fosters meaningful skill development with an interdisciplinary approach. By leveraging these insights, we can strive to enhance CT integration efforts, ensuring the holistic development of students' computational thinking abilities and promoting their preparedness for the increasingly interdisciplinary domains of digital world.
When it comes to mastering the digital world, the education system is more and more facing the task of making students competent and self-determined agents when interacting with digital artefacts. This task often falls to computing education. In the traditional fields of computing education, a plethora of models, guidelines, and principles exist, which help scholars and teachers identify what the relevant aspects are and which of them one should cover in the classroom. When it comes to explaining the world of digital artefacts, however, there is hardly any such guiding model. The ARIadne model introduced in this paper provides a means of explanation and exploration of digital artefacts which help teachers and students to do a subject analysis of digital artefacts by scrutinizing them from several perspectives. Instead of artificially separating aspects which target the same phenomena within different areas of education (like computing, ICT or media education), the model integrates technological aspects of digital artefacts and the relevant societal discourses of their usage, their impacts and the reasons behind their development into a coherent explanation model.