ETR&D

The recent emergence of generative artificial intelligence (GenAI) agents has brought significant potential and challenges in education. In science, technology, engineering, arts, and mathematics (STEAM) education, it is especially vital to cultivate students’ GenAI literacy and critical thinking skills. These competencies enable students to effectively apply artificial intelligence (AI) technologies in their STEAM learning. The present study proposed a GenAI agent-based STEAM learning approach and adopted the information world mapping (IWM) method to explore how a GenAI agent enhanced the engagement of 12 middle school students in a STEAM course, and their perceptions of GenAI agent-supported STEAM learning. These students fully participated in a 16-week course titled “Creative Design in STEAM Education.” Participants were asked to illustrate significant individuals, items, locations, and their interconnections to delve into their perceptions of and feedback on GenAI agent-supported STEAM learning. The results showed that while middle school students’ GenAI literacy improved, it did not reach a statistically significant level. In contrast, there was a significant enhancement in their critical thinking tendencies. Regarding students’ perceptions of GenAI agent-supported STEAM learning, the majority acknowledged that the GenAI agent played the roles of information provider and interaction facilitator in STEAM learning activities, particularly excelling in information retrieval and learning interactions. Moreover, the GenAI agent had positive influences on students in various aspects, including learning support, task completion, tool functionality, user experience, usage confidence and attitudes, as well as perceived warmth. Overall, the GenAI agent-based STEAM learning approach effectively enhanced middle school students’ technical skills and problem-solving abilities while also strengthening their teamwork, ethical awareness, and reflective capabilities, thereby demonstrating its potential for future applications in education.

Metacognitive skills are key transversal skills, strongly linked to academic achievement and essential for effective lifelong learning. They are widely recognized to underpin many of the core competencies required for effective social and professional participation, including core competencies such as critical thinking, problem-solving, and self-awareness, highlighted by United Nations Educational, Scientific and Cultural Organization (UNESCO) as key to Education for Sustainable Development. Digital learning technologies and virtual learning environments increasingly form a core strategic dimension of teaching and learning within Higher Education, however effectively harnessing them to enhance student learning remains challenging. This paper presents the Metacognition Design Framework, an evidence-based learning design that scaffolds the development of metacognitive skills in students through blended learning. It draws on the tools available within the virtual learning environment to provide a unique, and potentially powerful, opportunity to extend ‘metacognitive influence’ on student activity and learning outside standard class times with realistic levels of staff input. The design provides clarity for planning accessible learning interventions, their linkage with appropriate technology, and for guiding how face-to-face and online, or fully online, components interrelate. It also delivers an effective conduit to convey information in a practical format that readily permits adaptation to local contexts. Overall, it offers a practical way to re-frame educators as designers, increasing educator design capacity and the effective deployment of technology to enhance learning, at the same time targeting a learning outcome with high potential impact.

To address the rising cybersecurity threats associated with educational technology in K-12 schools and the gap in cybersecurity education, we designed and implemented a multimedia-based Educational Technology (Edtech) Cybersecurity and Privacy Awareness-Ask-Action (AAA) professional development (PD) for school administrators, technology support staff, and teachers. This study sought to identify the PD’s effectiveness, specifically focusing on the participants’ knowledge increments and the perceptions of their experience. Fifty K-12 school personnel with diverse demographic, technology and contextual backgrounds (district types and school levels) participated. Wilcoxon signed-rank tests indicated a significant increase in participants’ knowledge between pre- and post-test. Inductive coding identified emerging themes from post PD survey and follow-up interviews, highlighting participants’ perceived benefits, challenges encountered and future needs. Participants expressed an overall positive attitude, appreciating the increased cybersecurity awareness and enhanced ability to recognize and act against potential cyber threats to protect students and schools. However, challenges such as technical complexity and resource constraints were noted. Participants suggested future cybersecurity professional development could include more contextual guidance, AI-related cybersecurity topics, and provide up-to-date resources.

This study compares engineering college students’ learning motivation between AI-featured and traditional scaffolding methods using a mixed-methods approach. Two different types of class activities, one involving solving a computational problem and the other assembling an essay, were designed to be carried out using both AI-featured and conventional methods. All activities were designed using the Attention, Relevance, Confidence, and Satisfaction (ARCS) model and results were analyzed using both quantitative and qualitative means. Findings show that participants’ motivation was significantly higher in all Attention, Relevance, Confidence, and Satisfaction categories when they used AI-featured methods for solving computational problems while their motivation was significantly higher when they used AI-featured methods only in Attention and Confidence for the essay activity. Qualitative findings describe how AI features motivated learners based on the different types of scaffoldings. Additionally, how the AI features are related to each ARCS model categories are summarized. The results suggest strategies for optimizing the design of AI-featured scaffolds to motivate learners in STEM fields grounded in the ARCS model. This involves linking learners’ goals and interests to real scientific applications of AI by expanding ‘Relevance’ and adjusting learning requirements to strengthen ‘Confidence’ and ‘Satisfaction’.

This research investigates the design, development and implementation of Multiple Learning Analytics Dashboards (MLADs) with the goal of enhancing data-driven decision-making among teachers in primary education. The study presents a Design-Based Research (DBR) approach to ensure the iterative development and refinement of MLADs through collaboration with educational professionals. The research involved four key steps in the presented DBR approach: 1) identifying the requirements through stakeholder interviews; 2) generating design ideas through brainstorming sessions and prototyping; 3) collaboratively designing and developing MLADs and conducting usability testing to gather feedback; and 4) implementing the MLADs in real-world educational settings. This paper outlines the research methodology, the participants involved, and the progress made toward developing these Learning Analytics Dashboards (LADs). The outcomes highlight the creation of tailored MLADs for teachers across multiple municipalities, as well as the lessons learned from real-world implementation. Additionally, we analyze user feedback from teachers regarding the dashboard’s clarity, navigation, functionality, and design, providing a comprehensive view of the dashboard’s usability and areas for future enhancement.

Background Briefly describe the educational challenge, the relevance of AI and AR, and why improving practical skills learning is important. Purpose/Objective State the primary aim of the study (to investigate the impact of AI and AR on practical skills learning) and any secondary aims (motivation, engagement, retention). Methods Include study design (mixed-methods, quasi-experimental), participants, sampling, instruments (questionnaires, observations, interviews), intervention (AI + AR), and analysis methods. Results Summarize the key findings: significantly higher post-test scores in the AI/AR group, improved motivation, engagement, satisfaction, and stronger 3-month retention relative to controls. Conclusion Provide a concise interpretation that AI and AR effectively enhance practical skill acquisition and have important implications for education and training.

The present study investigated the interplay of cognitive cues and social cues in instructional videos on students’ knowledge acquisition and sense of social presence. 312 preservice teachers took part in the study. Out of these, 238 participants were randomly assigned to one of three experimental groups receiving an asynchronous online video lecture on three research methods topics, featuring either static slides with voiceover (minimal signaling), dynamic slides with voiceover (moderate signaling), or dynamic slides with additional instructor signaling (maximum signaling). 74 participants served as a control group and completed pre- and posttests without attending the course. Perceived difficulty, invested effort, motivation, and notetaking were included as covariates. Dynamic slides did not improve knowledge acquisition compared to static slides. However, instructor signaling enhanced knowledge acquisition when perceived difficulty was high. Furthermore, instructor signaling significantly increased perceptions of social presence for two of the three investigated topics, highlighting the importance of social cues.