ETR&D

Nurturing student artificial intelligence (AI) competency is crucial in the future of K-12 education. Students with strong AI competency should be able to ethically, safely, healthily, and productively integrate AI into their learning. Research on student AI competency is still in its infancy, primarily focusing on theoretical and professional discussions, along with qualitative investigations. This two-stage study aims to propose an AI competency framework for students and confirm the reliability and validity of its scale—student AI competency self-efficacy (SAICS)—in K-12 education. In stage 1, we used a three-round Delphi study to propose the framework and its scale. The framework has eight dimensions: interdisciplinary learning with AI, assessment with AI, decision-making with AI, data, ethics and AI, designing AI, multimedia creation with AI, human-centric learning, and confidence with AI. Each dimension contains four items. In stage 2, we involved 448 students to validate the scale using confirmatory factor analysis and model comparisons. The analyses showed that the scale is consistent across male and female students. The SAICS scale comprises 32 items and addresses eight dimensions of AI competency. Researchers can use the framework and SAICS to design their interventions and correlational research associated with student AI competency. Teachers can use them to develop learning outcomes for AI-based learning activities, and policymakers can use them to establish national AI standards.

This article proposes an instructional model based on psycho-pedagogical theories to serve as a basic structural unit for the creation of educational reinforcement platforms aimed at strengthening quantitative competences with which students enroll mathematics and statistics subjects (or other subjects that draw on this knowledge) at university. Although there are Intelligent Tutoring Systems (ITS) that are beneficial for students, the difficulty of manipulation and programming, together with their high economic cost when lacking programming skills, have prevented a widespread use of this type of interventions. Following the first steps of the ADDIE model, this article develops an instructional model that can be easily replicated by instructors lacking in programming and digital skills, designed to be applied in free and easy-to-handle interactive tutoring platforms, such as Genially.com or Canva, among others. The main foundations on which the pedagogical guideline is based are extracted through an extensive review of academic literature on psycho-pedagogical theories such as scaffolding, effective learning, metacognition, educational reinforcement, or feedback. Through it, students will be able to strengthen their quantitative conceptual foundations and reflect on their own learning process.

In recent years, studies have discussed how to introduce computational thinking (CT) concepts in mathematics education through mobile app development. In this study, the design of mathematics lessons based on the 5E instructional model to extend the idea of CT in a mobile technology environment (i.e., mobile CT) was investigated. Twenty-three primary five students in Hong Kong participated in this study. The teacher taught the students how to develop a mobile calculation game to learn the mathematical concept “area” through paper prototyping and mobile app development activities. Using a design-based research approach, the study examined students’ performance and behavior in the classroom to acquire mathematics knowledge and mobile CT. Qualitative conversation analysis was used to interpret teacher-student interaction, code files, and screen captures of students’ work. The analysis provided evidence on how students constructed mathematics concepts about “area” and built their mobile calculation games using mobile CT concepts, practices, and perspectives. The results propose the use of the 5E instructional model to enhance students’ engagement in and motivation for mathematics learning and strengthen their problem-solving skills, critical thinking, and communication and collaboration skills. Mobile CT-integrated mathematics lessons suggest ways for future educators to incorporate other mathematics topics into CT education. This study recommends that the 5E instructional model could be suitable for the instructional design of primary school CT-integrated mathematics curriculum. A set of design principles for integrating CT into mathematics curriculum is recommended.

In this study, Q methodology was employed to explore instructional designers’ perceptions of integrating ChatGPT in their design practices. Compared with traditional survey-based instruments that rely heavily on Likert-scale items, open-ended questions, interviews, or focus groups, Q methodology has the potential to systematically reveal and study subjectivity within a certain group of participants with both quantitative and qualitative techniques. The participants of this study consisted of 19 practicing instructional designers, who were asked to sort a total of 25 statements regarding the integration of ChatGPT into instructional design practices. Findings revealed three distinct types of factors: (1) Pessimistic Evaluators, (2) Optimistic Advocates, and (3) Wary Thinkers. Characteristics are discussed with direct quotes from representative participants from each of the three factors. The study also revealed that instructional designers mainly used ChatGPT to generate content, help improve writing and problem-solving, communicate, and engage in information searching. Regarding the challenges instructional designers encounter, the study reported that they were primarily bothered by the low quality of the ChatGPT-generated content, the limitation of ChatGPT itself, and their unpreparedness to embrace the tool. Limitations of the current study, as well as recommendations for future studies were also mentioned.

This research presents the results of a questionnaire survey (N = 222) exploring teachers’ experiences with using Wikipedia as a teaching tool, mostly in higher education, across various global contexts. The sample comprised educators from diverse regions, with a focus on those actively integrating Wikipedia and additional Wikimedia projects such as Wikidata, into their curricula. A mixed-methods approach was employed, combining quantitative analysis of structured questions with qualitative thematic analysis of open-ended responses. The findings reveal no significant gender or age biases among educators using Wikipedia; however, there is evidence of a global digital divide, with greater adoption observed in English-speaking countries. Most instructors reported assigning students to write or improve Wikipedia articles, typically accounting for about a quarter of the final course grade. Educators frequently utilized support tools and resources developed by the Wikimedia Community. Overall, participants reported positive teaching experiences, often linked to increased student and instructor motivation, as well as the achievement of multiple learning objectives related to academic and digital literacies. Nonetheless, the assignment was noted to be time-consuming. The study also found that Wikipedia assignments were well-suited for the transition from traditional to distance learning during the COVID-19 pandemic.

Problem-solving strategies are crucial in learning programming. Owing to their hidden nature, traditional methods such as interviews and questionnaires cannot reflect the details and differences of problem-solving strategies in programming. This study uses the Hidden Markov Model to detect and compare the problem-solving strategies of different groups in an interactive programming environment. The results suggest that high- and low-performance students have significant differences in their problem-solving strategies in programming. High-performance students had more “blank behaviors” in programming than low-performance students in video recordings. Low-performance students spent more time “searching teaching materials” than high-performance students. In the transfer task, high-performance students began the task by “identifying the problem,” while low-performance students were involved in the “implementing of strategies.” Additionally, high- and low-performance students improved from basic to transfer tasks. These findings shed light on why students performed differently in programming and how and when teachers needed to provide instructions to students in programming education.

Synchrony, or the timing of information as it is exchanged between participants, has garnered increasing study in online learning. Within this domain is bichronous online learning (BOL), the blending of asynchronous and synchronous elements within one learning environment. Some research has identified mobile instant messaging as quasi-synchronous (able to be both synchronous and asynchronous), but this affordance has been largely unexamined. This study addressed the above gaps by comparing online learning across asynchronous, synchronous, and quasi-synchronous modalities. It was framed by online learner engagement (OLE) which considers affective, behavioral, and cognitive dimensions and various environmental affordances including synchrony. This convergent mixed-method study explored engagement across forums, video chats, and mobile instant messaging (MIM) via qualitative content analysis, text mining, descriptive statistics, and social network analysis. There were several findings: The time students had to prepare their responses before interacting related to all three dimensions of engagement. Affectively, the quasi-synchronous modality appeared the most positive and least negative. Behaviorally, the quasi-synchronous and asynchronous modalities were more like one another than to the synchronous modality. Logistical flexibility afforded by the asynchronous and quasi-synchronous modalities impacted behavioral engagement. The quasi-synchronous debate had a unique dialectical structure compared to the other two modalities. Some students made use of other features within the Zoom debate to create a quasi-synchronous experience toward better cognitive engagement. Although not directly connected to synchrony, navigability seemed related to all dimensions of OLE as well. Finally, evidence among the preceding findings support the proposition that MIM’s quasi-synchronicity supports BOL.

In most physical education courses, teachers generally observe and interact with students face-to-face, so as to assess their learning status and provide immediate and appropriate guidance. However, during the...

Area measurement is a foundational component across STEM fields. The area formula offers limited insight for children trying to develop their area measurement reasoning skills, particularly when dealing with c...

The current systematic review study explores instructional designer practices to develop a comprehensive understanding of strategies employed by instructional designers and the expectations of the field. Using...

Teaching Computer Science concepts, such as graph theory, is often challenging. This study proposes an approach for teaching graph theory using an unplugged game (GraphGame) developed through a participatory design process that includes usage observation, clarifying meaning, prototyping, and implementation. This process was carried out with a group of Brazilian middle school students and involved collecting observations, interviews, ideation activities, and iterative prototyping with quantitative and qualitative tests. The game offers an interactive way to explore graph algorithms, improving abstraction capacity, a skill related to Computational Thinking. The effectiveness of the game in facilitating the learning of fundamental graphs concepts among high school students was evaluated with an independent sample of students. The results pointed out the proposal as a promising alternative for teaching graph theory, a complex computing topic, in an engaging way. By enhancing playful learning, this work offers an alternative to make teaching Computer Science more enjoyable and effective.

The formation of the digital divide is influenced by both objective factors, such as insufficient digital resources, and subjective factors, such as technology acceptance. This study employs a mixed-methods approach, utilizing the KANO model to analyze learners' demand attributes and the UTAUT model to examine subjective factors influencing technology acceptance. Standardized tests and survey questionnaires are used to assess digital learning outcomes. By collecting and analyzing data from diverse learner groups, this study aims to explore strategies for bridging the digital divide when transforming traditional online education into intelligent education, particularly in technologically and infrastructurally underdeveloped regions, including developing countries. The findings indicate significant differences in demand priorities among learners, as well as notable variations in how different learner groups classify their learning needs. Based on the demand analysis, targeted functional development can be implemented to reduce development costs in developing countries while maintaining the accessibility of digital resources. Moreover, user preferences for digital learning vary across groups; AI-driven identification and personalized recommendations can facilitate a more inclusive and equitable digital learning environment. Additionally, lowering the barriers to technology use through AI, enhancing engagement, and improving perceived effectiveness can significantly strengthen learners' confidence and motivation in bridging the digital divide. Finally, governments, educational institutions, and corporations should establish stronger communication and collaboration mechanisms to jointly address the pervasive digital divide in the era of intelligent education, particularly in developing countries.

This study investigated the effectiveness of localized learning content (LLC) in enhancing computational thinking (CT) skills and learning motivation among 6th-grade elementary school students in Busan, South Korea. In contrast to conventional methodological approaches, our research focuses on the transformative power of culturally and contextually relevant educational content. The study involved eight classroom teachers and 153 students, half of whom were exposed to programming education based on LLC and the other half to a conventional curriculum approved by the Ministry of Education. Quantitative analysis revealed a statistically significant improvement in CT skills and learning motivation among students in the LLC group compared to those in the control group, with effect sizes indicating a moderate magnitude of improvement. Semi-structured interviews with teachers and students supported these findings, indicating higher engagement and perceived relevance of LLC-based courses. However, this study also uncovered challenges related to the time and resources required to develop localized content. Despite these limitations, this study supports the potential utility of LLC, aligning with the sociocultural theory and the information process theory. It also opens new avenues for future research into LLC’s long-term efficacy and logistical feasibility. Given the significant improvements in CT and student motivation, the findings underscore the potential of LLC as a transformative approach in software education.

Writing explanations is widely recognized as an effective strategy to promote meaningful learning outcomes. However, most research focused on writing explanations for fictitious peers, with limited investigation into the benefits of writing for actual peers, particularly considering the influence of peer relationship closeness. To address this gap, the present study examined how explanation type (writing explanations vs. viewing explanations) and peer relationship closeness (close peer vs. distant peer) affect student’s perceived learning experience, attention allocation, behavior patterns, metacognitive judgment, and learning performance (i.e., retention and transfer). Our findings indicate that close peer relationships enhance student motivation and mental effort without increasing perceived difficulty. Furthermore, our results highlight the benefits of writing explanations, especially for a close peer, in enhancing students’ metacognitive judgment, self-monitoring behavior, and learning performance. These results lead to a recommendation to form strategic pairs or small groups in generative learning activities.

The adoption of blended and hybrid Flipped Classroom (FC) models increased dramatically during COVID-19 and is still highly valued and recommended for enhancing the quality, flexibility, and effectiveness of teaching and learning post-pandemic. While meta-analyses indicate a small yet meaningful effect size of the FC approach, examined studies often lack theoretical groundings and/or explicit design frameworks. As a result, there is an ever-increasing need for instructional design guidance for effectively integrating and facilitating online and in-person learning in the FC context. The current paper outlines a framework intended to guide educators, designers, and researchers to maximize the synergy of online and in-person learning as they design and implement FC. Grounded in (Merrill, First principles of instruction: Identifying and designing effective, efficient and engaging instruction, Pfeiffer, 2013) First Principles of Instruction, (Zimmerman and Schunk, Zimmerman and Schunk (eds), Handbook of self-regulation of learning and performance, Routledge, 2011) Self-Regulated Learning process, (Jones, Motivating students by design: Practical strategies for professors, CreateSpace, 2018) MUSIC Model of Academic Motivation, and (Flavell, American Psychologist 34:906, 1979) Metacognition theory, we first present a conceptual framework with flipped learning cycle, self-regulated learning cycle, and metacognitive FC orientation. Informed by these theories and our FC design experience across disciplines, we convert the conceptual framework into a procedural framework by organizing the design aspects and components into a generic FC learning process. Finally, we propose theoretically and empirically grounded design strategies for individual components of the FC process, which can be further validated and refined through iterative educational design research.

Artificial Intelligence (AI) tools are becoming increasingly popular in education, providing teachers with new opportunities to enhance student learning experience and build upon existing teaching practices. This study employs a co-design approach to develop AI-integrated learning materials and explore their implementation in elementary classrooms. In collaboration with researchers, twenty-five in-service teachers co-designed engaging, age-appropriate lesson plans aligned with the national elementary curriculum and adaptable to diverse classroom needs. Qualitative analysis of teachers’ focus groups (n = 25) highlighted the co-design approach's role in empowering teachers, fostering a sense of ownership, encouraging knowledge-sharing, and promoting collaboration and enthusiasm. A pilot study conducted in four elementary classrooms with 62 students evaluated the AI-integrated lesson plans and materials through quantitative survey data (n = 62). Findings indicate that students found the AI-supported learning experience enjoyable, engaging, and meaningful, particularly in deepening their understanding of literacy concepts. This paper presents the co-designed materials and provides insights for practitioners and researchers on the future of AI-powered education, offering potential directions for further research.

In educational settings, gamified learning integrates a variety of game elements to improve the learning experience, but a thorough analysis comparing different combinations of these elements is sparse. This meta-analysis consolidated data from 182 effect sizes across 37 randomized or quasi-randomized trials to assess the impact of gamified learning on student outcomes. Statistical analyses were conducted with gamified learning as the independent variable, learning outcomes as the dependent variable, and learning domain, learning stage, and intervention duration as moderating variables. The study aimed to evaluate the overall effect of gamified learning and to determine the most effective combinations of game elements. The results revealed a medium positive effect of gamified learning on learning outcomes (d = 0.566), with the “Rules/Goals + Challenge + Mystery” combination yielding the highest impact. And significant moderation was observed regarding the learning domain and the duration of the intervention. These findings offer valuable guidance for the design and application of gamified learning strategies, highlighting the need to consider moderating factors in educational practice.

Generative Artificial Intelligence (GAI) has received widespread attention recently, influencing teacher education in various ways. However, there is little discussion on pre-service teachers’ behavioral intention towards GAI. Therefore, this study employs subjective norm, AI self-efficacy, facilitating conditions, and trust to expand the Technology Acceptance Model, understanding pre-service teachers’ adoption of GAI. The study involves 486 undergraduates from a university in Jiangsu Province, China. The Partial Least Squares Structural Equation Model is used to test the research model. Research model proved to be both reliable and valid, confirming nine out of ten hypotheses. The findings indicate that: (1) AI self-efficacy strongly predicts perceived ease of use; (2) The most direct and strongest impact on perceived usefulness is perceived ease of use, followed by facilitating conditions; (3) Perceived ease of use doesn’t directly affect attitude towards use, but perceived usefulness and trust significantly influence this attitude; (4) Attitude towards use greatly predicts behavioral intention, followed by perceived usefulness and subjective norm. This research helps in advancing policy development in educational institutions and the integration of GAI and teacher education.

Computational thinking (CT) skills have become increasingly important in modern education, as they equip students with critical problem-solving skills applicable across various domains. Given the growing emphasis on digital literacy, it is essential to investigate grade- and gender-level differences in CT skills among students to support targeted interventions and to ensure that all students have equal opportunities to succeed in the digital age. This study examined CT skill development among primary school students, taking both grade- and gender-level disparities into account. Using quantitative data from a diverse sample of 517 primary school students, we conducted a comprehensive analysis of their CT scores. The results revealed no significant gender differences in CT scores among primary school students. However, notable age-related disparities emerged, with CT scores rising as students progressed through higher grades. This finding underscores the importance of considering developmental factors in CT education and highlights the need for age-appropriate CT curricula. By investigating both grade- and gender-level differences, this study aims to support educators and policymakers in developing more inclusive and effective strategies for cultivating CT skills among young learners, thereby preparing them for the challenges of the digital age.