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กิจกรรมที่ 4 (ปรับปรุง) การเขียนที่มาและความสำคัญของงานวิจัย

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กิจกรรมที่ 4 (ปรับปรุง) การเขียนที่มาและความสำคัญของงานวิจัย

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1. เขียนเป็นความเรียงภาษาเขียนทางการ แบ่งย่อหน้า ตามประเด็นที่นำเสนอ

2. เล่าเรื่อง ปัญหาของตัวแปรตามทั้ง 2 ตัวก่อน และตามด้วยแนวทางแก้ปัญหาผู้เรียน ด้วยนวัตกรรมทางการศึกษาที่นักวิจัยต้องการพัฒนา และสรุปด้วยงานวิจัยที่จะทำ เล่าทั้งหมดประมาณ 6-7 ย่อหน้าขึ้นไป

3. เพิ่มการอ้างอิงในเนื้อหาด้วย เนื้อหาแต่ละส่วนอ้างอิงงานจากใคร

4. ส่งเป็นไฟล์ PDF ใน Comment ของโพสนี้ ภายในวันอาทิตย์ ที่ 19 กรกฎาคม 2569

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กิจกรรมที่ 5 research framework & research objective

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กิจกรรมที่ 5 research framework & research objective

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การพัฒนาชุดนวัตกรรมการเรียนรู้ "Food Waste STEAM Crayon Kit" เพื่อส่งเสริมมโนทัศน์ ทางเคมีและทักษะการแก้ปัญหาเชิงสร้างสรรค์

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การพัฒนาชุดนวัตกรรมการเรียนรู้ Food Waste STEAM Crayon Kit เพื่อส่งเสริมมโนทัศน์ ทางเคมีและทักษะการแก้ป 2

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กิจกรรม 2: วิเคราะห์การเขียนที่มาและความสำคัญจากบทความวิจัยที่สืบค้นมากจากสัปดาห์ก่อนหน้า 

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กิจกรรม 2: วิเคราะห์การเขียนที่มาและความสำคัญจากบทความวิจัยที่สืบค้นมากจากสัปดาห์ก่อนหน้า 

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1. เลือกบทความความวิจัย 1 บทความ ที่เห็นว่ามีวิธีการเขียนที่มาและความสำคัญสอดคล้อง 5 ขั้น มากที่สุด

2. ทำเครื่องหมาย, สัญลักษณ์, ขีดเส้นใต้ แล้วระบุหมายเลข ดังนี้

1 = สภาพที่พึงประสงค์ (Desired State)

2 = สภาพจริง (Current State)

3 = ช่องว่างของปัญหา (Problem Gap)

4 = สาเหตุของปัญหา (Cause)

5 = แนวทางแก้ไข (Solution)

3. หากไม่พบบทความที่มีครบ 5 ขั้น ระบุเป็นข้อเสนอแนะเพิ่มเติม พร้อมตัวอย่างข้อความที่ควรเพิ่มเติม

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การพัฒนาชุดนวัตกรรมการเรียนรู้ "Food Waste STEAM Crayon Kit" เพื่อส่งเสริมมโนทัศน์ ทางเคมีและทักษะการแก้ปัญหาเชิงสร้างสรรค์

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การพัฒนาชุดนวัตกรรมการเรียนรู้ "Food Waste STEAM Crayon Kit" เพื่อส่งเสริมมโนทัศน์ ทางเคมีและทักษะการแก้ปัญหาเชิงสร้างสรรค์

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การพัฒนาชุดนวัตกรรมการเรียนรู้ Food Waste STEAM Crayon Kit เพื่อส่งเสริมมโนทัศน์ ทางเคมีและทักษะการแก้ปัญหาเชิงสร้างสรรค์

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กิจกรรมที่ 3 งานวิจัยของกลุ่ม

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กิจกรรมที่ 3 งานวิจัยของกลุ่ม

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กลุ่ม 1 การพัฒนาชุดนวัตกรรมการเรียนรู้ "Food Waste STEAM Crayon Kit" เพื่อส่งเสริมมโนทัศน์ทางเคมีและความตระหนักด้านการใช้ทรัพยากรอย่างคุ่มค่า

กลุ่ม 2 การพัฒนาชุดนวัตกรรมการเรียนรู้ "Food Waste STEAM Crayon Kit" เพื่อส่งเสริมมโนทัศน์ทางเคมีและทักษะการแก้ปัญหาเชิงสร้างสรรค์

ทั้งกลุ่มช่วยกันเขียนที่มาและความสำคัญตามหัวข้อวิจัยที่ได้รับ ให้ครอบ 5 ขั้นตอน ส่งเป็นไฟล์ PDF ภายในวันอาทิตย์ ที่ 12 กรกฎาคม 2569 ไม่เกินเที่ยงคืน โดยโพสใหม่ทาง Padlet

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กิจกรรม 1: วิเคราะห์ตัวอย่างที่ 1

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กิจกรรม 1: วิเคราะห์ตัวอย่างที่ 1

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ใบความรู้_การเขียนที่มาและความสำคัญของการวิจัย_พร้อมตัวอย่าง

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2. คำถามวิจัยเพื่อนำไปสู่การแก้ปัญหาผู้เรียน จากปัญหาวิจัย "นักศึกษามีมโนทัศน์การวิจัยที่คลาดเคลื่อน"

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2. คำถามวิจัยเพื่อนำไปสู่การแก้ปัญหาผู้เรียน จากปัญหาวิจัย "นักศึกษามีมโนทัศน์การวิจัยที่คลาดเคลื่อน"

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1. ตั้งคำถามเพื่อนำไปสู่การแก้ปัญหาผู้เรียน

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1. ตั้งคำถามเพื่อนำไปสู่การแก้ปัญหาผู้เรียน

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ปัญหาวิจัยคือ นักศึกษาไม่ชอบการวิจัย/มีเจตคติที่ไม่ดีต่อการวิจัย

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Reflection4 กิจกรรมเปลี่ยนขยะเป็นของมีค่า (STEAM)

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Reflection4 กิจกรรมเปลี่ยนขยะเป็นของมีค่า (STEAM)

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จากปัญหาขยะอาหารที่พบ เราจะออกแบบการเรียนรู้เพื่อเปลี่ยนแปลงอะไรในตัวผู้เรียนได้บ้าง?

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ณัฐิดาไทย (006) แก้ไขใหม่

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ณัฐิดาไทย (006) แก้ไขใหม่

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ค้นหา : การออกเเบบกิจกรรม STEAM ในระดับประถมศึกษา

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ผู้แต่ง วันดี พันธุ์โท - จรินทร อุ่มไกร ไกยสิทธิ์ อภิระติง

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การจัดการเรียนรู้แบบโครงงานเป็นฐานผ่านคลาวด์เลิร์นนิงร่วมกับสะตีมศึกษา เพื่อพัฒนาทักษะการสร้างสรรค์ชิ้นงาน สำหรับนักเรียนชั้นประถมศึกษาปีที่ 6 | วารสารวิทยาศาสตร์และเทคโนโลยี มทร.สุวรรณภูมิ

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การวิจัยครั้งนี้มีวัตถุประสงค์เพื่อพัฒนาและประเมินผลของกิจกรรมการจัดการเรียนรู้แบบโครงงาน         เป็นฐานผ่านคลาวด์เลิร์นนิงร่วมกับสะตีมศึกษา เพื่อพัฒนาทักษะการสร้างสรรค์ชิ้นงาน สำหรับนักเรียน                    ชั้นประถมศึกษาปีที่ 6 รวมทั้งเปรียบเทียบผลสัมฤทธิ์ทางการเรียน ความสามารถในด้านทักษะการสร้างสรรค์ชิ้นงาน และความพึงพอใจของนักเรียนที่มีต่อกิจกรรมดังกล่าว กลุ่มตัวอย่างคือ นักเรียนชั้นประถมศึกษาปีที่ 6 โรงเรียน        บ้านคลองหลวง จำนวน 17 คน ซึ่งได้รับการเลือกโดยใช้การเลือกสุ่มแบบกลุ่ม (Cluster Sampling) เครื่องมือที่ใช้ในการวิจัย ได้แก่ แผนการจัดการเรียนรู้ สื่อการสอนผ่าน Google Site แบบทดสอบวัดผลสัมฤทธิ์ แบบวัดทักษะการสร้างสรรค์ชิ้นงาน แบบสอบถามความพึงพอใจ การวิเคราะห์ข้อมูลใช้สถิติเชิงพรรณนาและการทดสอบ t-test ผลการวิจัยพบว่า กิจกรรมที่พัฒนามีประสิทธิภาพเท่ากับ 79.41/77.45 สูงกว่าเกณฑ์ 75/75 อย่างมีนัยสำคัญ          ทางสถิติที่ระดับ .05 ผลสัมฤทธิ์ทางการเรียนและทักษะการสร้างสรรค์ชิ้นงานหลังเรียนสูงกว่าก่อนเรียนอย่าง             มีนัยสำคัญทางสถิติที่ระดับ .05 และนักเรียนมีความพึงพอใจต่อกิจกรรมในระดับดีมาก

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https://www.tandfonline.com/doi/full/10.1080/00043125.2016.1224869

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มธุรดา001 ต่างชาติ(ใหม่)

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มธุรดา001 ต่างชาติ(ใหม่)

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คำที่ใช้ในการค้น "Designing STEAM Activities in Primary

Education"

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How Can Strategy Games Teach Design Thinking in Primary Education? A Design-based Study of Maker-oriented STEAM Learning Through Game Redesign - TechTrends

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Primary education STEAM projects often remain organized by subject boundaries, and classroom technology is frequently used for delivery rather than student authorship. This article presents a design-based study of a maker-oriented project in which Grade 5 students redesigned strategy games through digital design, digital fabrication, and QR-linked documentation. Working in small teams, students analyzed existing games, redesigned boards and pieces with accessible 2D and 3D design tools, fabricated components via laser cutting and 3D printing, and created rule guides and short videos in the languages they were learning for peer users. Evidence for reflection included student artifacts, documentation, and observations during classroom playtesting and a student-led board game club. Across phases, students revised rules and materials in response to play, adopted differentiated design roles, and used documentation to support independent use by others. The article offers design principles for integrating maker tools to support authorship, iteration, and learning that extends into informal school spaces.

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วิชญาพร 002 (ต่างชาติ) ใหม่

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วิชญาพร 002 (ต่างชาติ) ใหม่

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วิธีการค้นหา : Designing STEAM activities at primary level

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Developing engaging STEAM-geometry activities: Fostering mathematical creativity through the engineering design process using Indonesian cuisine context | Infinity Journal

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Infinity Journal published by STKIP Siliwangi Bandung publishes original research or theoretical papers about teaching and learning in mathematics education

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คำที่ใช้ค้นหา Designing STEM Activities At Primary Level

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จิราธิป งานไทย

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จิราธิป งานไทย

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ผู้แต่ง กาญจนาพร นนทรัตน์ สำนักงานอธิการบดี กองบริหารงานบุคคล มหาวิทยาลัยราชภัฏบ้านสมเด็จเจ้าพระยา

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การเปรียบเทียบผลสัมฤทธิ์ของชุดการสอน เรื่องนาฏยศัพท์ ตามแนวคิด STEAM Education ของนักเรียนชั้นประถมศึกษาปีที่ 5 กรณีศึกษา โรงเรียนวัดประดิษฐาราม เขตธนบุรี กรุงเทพมหานคร | วารสารสถาบันวิจัยและพัฒนา มหาวิทยาลัยราชภัฏบ้านสมเด็จเจ้าพระยา

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การวิจัยครั้งนี้มีวัตถุประสงค์เพื่อเปรียบเทียบผลสัมฤทธิ์ทางการเรียนของนักเรียนชั้นประถมศึกษาปีที่ 5 ที่ได้รับการเรียนด้วย ชุดการสอนเรื่องนาฏยศัพท์ตามแนวคิด STEAM Education กับก่อนเรียน กรณีศึกษา โรงเรียนวัดประดิษฐาราม เขตธนบุรี กรุงเทพมหานคร กลุ่มตัวอย่างที่ใช้ในการวิจัย ได้แก่ นักเรียนชั้นประถมศึกษาปีที่ 5 ภาคเรียนที่ 1 ปีการศึกษา 2564 โรงเรียนวัดประดิษฐาราม เขตธนบุรี กรุงเทพมหานคร เครื่องมือที่ใช้ในการวิจัย ได้แก่ ชุดการสอนเรื่องนาฏยศัพท์ตามแนวคิด STEAM Education ซึ่งประกอบด้วยแผนการจัดการเรียนรู้จำนวน 6 ชั่วโมง และแบบทดสอบวัดผลสัมฤทธิ์ทางการเรียน การวิเคราะห์ข้อมูลใช้สถิติ t-test (Dependent Sample t-test) ผลการวิจัยพบว่า ชุดการสอนเรื่องนาฏยศัพท์ตามแนวคิด STEAM Education ที่ผู้วิจัยพัฒนาขึ้นมีประสิทธิภาพสูงกว่ามาตรฐานที่กำหนด นักเรียนมีผลสัมฤทธิ์ทางการเรียนหลังเรียนสูงกว่าก่อนเรียนอย่างมีนัยสำคัญทางสถิติที่ระดับ .01 อีกทั้งการบูรณาการแนวคิด STEAM Education ช่วยให้นักเรียนเกิดความสนใจในการเรียนนาฏศิลป์มากขึ้น มีความสนุกในการเรียนรู้ มีส่วนร่วมในกิจกรรมการเรียนรู้ และพัฒนาทักษะชีวิตที่จำเป็นต่อการดำรงชีวิตในศตวรรษที่ 21 ได้อย่างมีประสิทธิภาพ

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Designing STEM activities at the elementary level.

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Abstract

Although introducing AI education into K-12 schools has attracted significant interest from educators and researchers, research into effective strategies for enhancing elementary students' AI literacy remains in its infancy. Grounded in design-based learning, we propose a pedagogical framework, "Students as AI literate designers" (SAILD), to facilitate young learners' acquisition of AI literacy by designing solutions for real-world problem-solving. This paper reports on our mixed-methods study evaluating the effectiveness of SAILD in Grade-5 students' AI literacy learning and teaching in Hong Kong, regarding AI knowledge, skills, ethics, and attitudes. The quantitative findings show significant differences in students' AI knowledge, ethics, and attitudes between pre- and post-test scores; the qualitative findings reveal students' problem-solving processes using AI skills. While the present work provides empirical evidence supporting the adoption of SAILD in elementary AI education, it underlines the challenges of SAILD and suggests areas for further research.

Introduction

The widespread adoption of artificial intelligence (AI) technologies in daily life has underscored the importance of AI literacy in a technology-driven future, highlighting the need to equip citizens with AI-related competencies (Eguchi et al., Citation2021; Fundi et al., Citation2024; Su et al., Citation2023; UNESCO, Citation2022). AI education is now receiving attention at the K-12 level, as evidenced by global initiatives like China's New Generation of Artificial Intelligence Development Plan (State Council of China, Citation2017), Australia's Digital Technologies MOOC programs for teachers led by the Computer Science Education Research Group (CSER; Australian Government, Citation2021), and the AI4K12 Initiative in the United States sponsored by the National Science Foundation (Touretzky et al., Citation2019). For example, under its New Generation of Artificial Intelligence Development Plan (State Council of China, Citation2017), China aims to become the global leader in AI innovation by 2030, placing a strategic focus on establishing a robust pipeline of AI talent. A key initiative of this plan emphasizes a full integration of AI into China's education system by 2025, beginning in primary and secondary schools and continuing through to universities. Similarly, in the US, the "Five Big Ideas in AI" framework advocates for introducing AI technologies as early as kindergarten through Grade-2, suggesting the importance of early exposure to AI in fostering foundational understanding. Indeed, the increasing presence of AI in everyday life exposes students to AI products more frequently, posing risks of misconceptions and overtrust in AI. This calls for the cultivation of a generation of informed and critical citizens, particularly in an AI-infused world (Dai, Citation2024; Mertala et al., Citation2022). Early introduction to AI can thus help students navigate the complexities of an AI-driven society, foster their adaptability, and unlock opportunities for meaningful contributions in the digital era (Dai et al., Citation2024; Kong & Abelson, Citation2022; Pangrazio & Selwyn, Citation2021; Touretzky et al., Citation2019).

Despite numerous efforts to embed AI education into the K-12 curriculum through national policies and innovative programs, the incorporation of AI education at the elementary level is still in its infancy, and it faces considerable challenges (Dai, Citation2024). One major challenge is to determine what to teach for K-12, especially for upper elementary students. Scholars have sought to define what AI literacy-the essential competencies K-12 students require-should entail to ensure that future generations become informed users who can engage with, communicate with, and critically evaluate AI (Long & Magerko, Citation2020; Touretzky et al., Citation2019). This definition extends beyond merely recognizing AI products; it requires understanding the fundamental concepts of AI and the capability to use AI, coupled with an awareness of its societal impacts and ethical concerns. The complex nature of AI literacy poses substantial challenges for educators who need to familiarize themselves with a new subject area to effectively develop and implement relevant courses (Yue et al., Citation2024).

Another challenge is the lack of research and established pedagogical foundations for AI education at the K-12 level. Unlike traditional subjects like science and mathematics, which benefit from a long history of evidence-based development, AI education lacks comprehensive empirical studies on effective learning and teaching methods (Wang & Lester, Citation2023). While K-12 AI courses have started to appear internationally, review studies indicate that many of these courses lack a solid theoretical foundation in pedagogical design (Rizvi et al., Citation2023; Yue et al., Citation2021, Citation2022). Concepts such as machine learning, big data, and programming are acknowledged as challenging for through effective pedagogical strategies remain insufficient (Dinçer & Doğanay, Citation2017; Ottenbreit-Leftwich et al., Citation2023). Consequently, it is crucial to develop a theory-driven pedagogical framework and establish an evidence-based foundation for AI literacy education in elementary settings (Kim & Kwon, Citation2023; Yue et al., Citation2022, Citation2024).

Grounded in Fortus et al. ( Citation2004) theoretical foundation of design-based learning (DBL), we have proposed a pedagogical framework called "Students as AI Literate Designers" (SAILD) to integrate DBL into AI literacy education. DBL, which has been extensively used in engineering and technology education, allows students to apply specialized knowledge to develop an artifact or system, through which students are engaged with design principles inherent in engineering or technology concepts (Gómez Puente et al., Citation2011). In AI education, designing products for problem-solving requires students not only to understand and apply AI knowledge and use AI-driven tools but also to contemplate their ethical and social issues in real-world scenarios. Therefore, integrating DBL into AI education holds promising potential in addressing the multifaceted demands of AI literacy. As highlighted in UNESCO's framework on AI literacy (2022), this study aims not only to enhance understanding and engagement with AI technologies, but also to raise awareness of ethical and societal considerations, such as risks related to data privacy, AI misuse, and decision-making bias, thereby fostering critical attitudes and a balanced perspective toward AI.

In sum, this paper has two-folded purposes. First, it is to delineate the pedagogical design of SAILD through a theory-driven approach. Second, it empirically assesses and discusses the pedagogical effectiveness of SAILD on AI literacy with the four aspects: (1) students' acquisition of AI knowledge; (2) students' development of AI skills; (3) students' understanding of AI ethics; and (4) students' attitudes toward AI.

Literature review

AI literacy education in K-12

Conceptualizing AI literacy

The scholarly discourse on AI literacy has burgeoned, with significant contributions from researchers such as Kandlhofer et al. ( Citation2016), Aoun ( Citation2017), and Long and Magerko ( Citation2020). Notably, the definition proposed by Long and Magerko ( Citation2020) has been highly influential. They define AI literacy as a set of competencies that enable individuals to critically assess AI technology, effectively engage and collaborate with AI, and proficiently utilize AI tools in diverse environments. More recently, other conceptions and disciplinary-related usages have been applied to further manifest AI literacy, such as connecting it with information literacy, technology literacy, digital literacy and data literacy (Carolus et al., Citation2023; Schüller, Citation2022; Stolpe & Hallström, Citation2024; Wang et al., Citation2023), contextualizing it in voice-based AI systems or AI-related workplaces (Carolus et al., Citation2023; Cetindamar et al., Citation2024), and featuring it with the combination of proficiency dimensions and different subject areas (Pinski & Benlian, Citation2024). There is a growing consensus among scholars that AI literacy will become as crucial as traditional literacy skills like reading and writing, advocating for its integration into education from an early age, as suggested by Kandlhofer et al. ( Citation2016) and further supported by studies focusing on preschool education (Su et al., Citation2023; Williams et al., Citation2019).

In this study, we conceptualize AI literacy through a four-dimensional framework comprising AI knowledge, AI skills, AI ethics, and AI attitudes. This framework reflects the current scholarly discourse on AI literacy and aligns with Gagne's Theory of Instruction (Gagne, Citation1974), which categorizes learning outcomes into cognitive, psychomotor, and affective domains. Here, the cognitive domain includes factual and conceptual knowledge, the psychomotor domain focuses on practical skills and hands-on experience, and the affective domain encompasses individual attitudes and values, influencing decision-making and actions. The proposed framework, detailed in Table 1, aims to guide the development of AI literacy education for elementary students.

Current efforts in elementary AI literacy education

Design-based learning (DBL)

Engineering and programming education is considered highly relevant to AI literacy education (Chiu, Citation2021; Dai et al., Citation2024), where students learn to design, assemble, and program agents with sensors, actuators, and other elements to solve problems. Design-based learning (DBL) is one of the most influential approaches in engineering education (Gómez Puente et al., Citation2011). Grounded in constructionism (Harel & Papert, Citation1991), case-based reasoning (Kolodner, Citation1994), and situated cognition theories (Brown et al., Citation1989), DBL encourages students to utilize practical skills and domain-specific knowledge through project-based tasks. Consequently, the skills and knowledge gained are readily adaptable and applicable to real-world situations (de Vries, Citation2006). The design cycle includes five phases: identifying and defining context, conducting background research, developing personal and group ideas, constructing 2D and 3D artifacts, and providing feedback (Fortus et al., Citation2004). Design acts as a structured and analytical methodology in this learning process, where learners are guided to engage in inquiry, problem-solving, and self-reflection (Song et al., Citation2016).

Prior research shows that DBL provides a practical context for engaging students in knowledge integration (e.g. Fortus et al., Citation2004; Kolodner et al., Citation2003), thereby facilitating their development of essential critical thinking skills, including representation, reasoning, and creative thinking (e.g. de Vries, Citation2006; Kafai, Citation1995, Citation2005; Li, Citation2012). This facilitation occurs through the synthesis and application of content knowledge, underscoring the role of DBL in fostering higher-order cognitive skills. Kafai and her colleagues (Kafai, Citation1995; Kafai et al., Citation2012) adopted a constructionist lens to aid elementary students in learning fractions through designing computer games with Logo and found student development in mathematical representations, design skills, and mathematical inquiry throughout the design process. Furthermore, Kolodner et al. ( Citation2003) investigated the Learning by Design ™ (LBD) curriculum in a middle school science context, which integrated case-based reasoning with problem-based learning. The tailored instructional activities aimed to enhance design-based scientific inquiry. This research revealed that students in the LBD group learned earth and physical science concepts more effectively than their peers and showed superior performance in scientific experiment design, analysis, teamwork, and metacognitive strategies. Similarly, Fortus et al. ( Citation2004) examined the impact of design-based science teaching on the scientific understanding and practical problem-solving capabilities of high school students. After participating in the design-based curriculum, students from various achievement levels showed significant improvements in both areas. More recently, Li et al. ( Citation2023) adopted DBL to improve elementary school students' computational thinking by solving real-world problems using LabPlus electronic kits and Scratch programming software. The abovementioned research highlights the effectiveness of DBL in enhancing comprehensive cognitive competencies across a varied student population, affirming its value in educational practice.

However, previous research has underscored several issues that need to be addressed when formulating and applying DBL initiatives. One significant challenge is linking design activities directly to the intended domain-specific content knowledge. Often, learners may concentrate on the technical aspects of design, neglecting deeper exploration of content or the formation of structured knowledge (Kafai, Citation1995; Citation2005; Kolodner et al., Citation2003). This neglect can disconnect design tasks from knowledge construction. Thus, the interaction between design activities and knowledge acquisition is often mismatched, which can impede learning outcomes (Bozkurt Altan & Tan, Citation2021). Moreover, although there have been various approaches to developing DBL to facilitate learning (Delen & Sen, Citation2023), existing studies on DBL predominantly center on engineering education (e.g. Doppelt et al., Citation2008; Ke, Citation2014; Mehalik et al., Citation2008); leaving a gap in exploring the potential of DBL in AI education to further extend the DBL theory. Therefore, it is essential to develop a pedagogical framework grounded in DBL theory and evaluate its effectiveness in fostering elementary students' AI literacy.

Theory-driven design: students as AI literate designers (SAILD)

In the present study, we develop a pedagogical framework called SAILD, which integrates DBL into AI literacy education in elementary schools. Informed by the five phases of DBL proposed by Fortus et al. ( Citation2004), this framework involves two interactive parts: design and research ( Figure 1). The design part invites students to engage in the design cycle, which includes problem identification, design solution generation, digital artifact creation, and evaluation. The phase of background research originating from the design part is extracted as a distinct research part, parallel to the design part. In the research part, students are encouraged to engage in brainstorming, hands-on exploratory practice, critical discussion, and reflection. These two types of activities alternate in an iterative sequence. Specifically, students are first motivated to identify a real-life problem. Based on a brief introduction to domain-specific knowledge, students generate an initial design solution aimed at addressing the real-life problem. Subsequently, teachers provide guidance to support their comprehension of challenging concepts encountered during the design activities. This newly acquired knowledge enables students to refine their designs. Following this improvement, teachers introduce more advanced knowledge essential for the ongoing design iteration. Students then apply the advanced knowledge to adapt and enhance their designs. To obtain a comprehensive reflection on their design solutions, students are guided to assess their work via evaluation processes.

The iterative process inherent in SAILD possesses certain key features and benefits. First, design consistently serves as the driving force. By initiating the process with the design of a solution to a real-world problem, this approach fully leverages the advantages of DBL to enhance student engagement and motivation. Second, the subsequent research activities strive to bridge the gap between knowledge acquisition and design tasks, particularly in the context of AI. Given that AI encompasses many abstract and complex concepts, the emphasis on the research process can assist students in acquiring the necessary knowledge for their design solutions. Additionally, it provides justifications and insights that fuel the next cycle of design improvements. Finally, the iterative process seeks to maintain a balance between design and research. On one hand, students apply the learned knowledge to support their design. On the other hand, their involvement in design activities, encounters with design challenges, and attempts to reason and resolve these challenges can deepen their understanding of the underlying AI-related concepts of the design solution.

SAILD is designed to foster young students' AI literacy. First, it emphasizes the research component, providing students with a scaffold to construct their understanding of AI while actively developing AI-based solutions. This framework effectively integrates intellectual learning with the design cycle. In the design process, students are encouraged to apply AI knowledge to create solutions for real-life problems, thereby enhancing their practical AI skills. Moreover, as students refine their design solutions, they are expected to address the potential issues associated with AI, including inaccurate decision-making, data privacy concerns, job loss to AI, and possible malicious uses of AI. This process enables students to reflect on the societal and ethical implications of AI, promoting their ethical awareness and societal responsibility-critical concerns in an increasingly AI-driven world (Ouchchy et al., Citation2020). Furthermore, students' attitudes toward AI can be shaped by contemplating their positions on AI and negotiating with various stakeholders involved in AI-related issues. Thus, it is promising to adopt SAILD as a pedagogical framework to support students in acquiring AI literacy.

Methodology

This study employed a mixed-methods design with the combination of both quantitative and qualitative methods to enhance the breadth and depth of our understanding of complex research issues (Creswell & Clark, Citation2017). This methodological approach is particularly valuable for dissecting intricate phenomena from various perspectives, offering a richer analysis than either qualitative or quantitative methods could achieve independently (Teddlie & Tashakkori, Citation2011).

Participants and research context

In this study, 50 Grade-5 students (aged between 10 and 12 years) from an elementary school in Hong Kong were recruited. Research consent was obtained from all participating students, their guardians, and the school principal, regarding the research and data collection process. The guardians of these participating students were informed of responsible AI usage and privacy policies in our study. First, students' online activities (e.g. Google Quick Draw and Google Teachable Machine) are conducted with parental consent and do not require personal accounts, record individual information, or store any personal data in AI systems. This aligns with the regulation outlined in the Children's Online Privacy and Protection Act (COPPA, Citation2013) regarding children's use of AI technologies. Second, the Ethical AI Framework of Hong Kong (Digital Policy Office, Citation2024) stipulates that personal data must only be used for its intended purpose and should not be retained longer than necessary, a principle that was communicated to participants. The consent form explicitly stated that collected data would only be used for research purposes and would be permanently deleted upon the study's completion. Therefore, all data and information collected during the research would remain strictly confidential, with no risk of disclosure.

Participating students were divided into ten groups (i.e. five students per group) to partake in collaborative activities and design tasks. Group formation was based on each student's overall academic performance, aiming to ensure a balanced distribution of ability levels within each group. All learning and teaching activities in this study, apart from the pre- and post-tests, were conducted in a group-based format ( Figure 2). This approach was designed to provide students with an immersive and practical learning environment to foster collaborative learning and enhance the educational experience by encouraging students to engage collectively in discussions, problem-solving, and design tasks.

Research procedure

This study consisted of five 90-minute lessons held on five consecutive days, focusing on AI-related topics, as delineated in Table 2. The entire intervention and data collection were carried out over two weeks, involving the following steps:

1. Day 1: Pretest. Prior to the lessons, students completed a pretest to evaluate their existing knowledge of AI and their attitudes toward AI and the subject. This assessment comprised a 30-minute knowledge test (KT) and a 15-minute self-reported questionnaire. Although the KT in the pretest consisted of content from all topics covered in the intervention, the pretest results did not influence the teaching approach or content delivery during the intervention. More details are provided in the next subsection.

2. Day 2 to Day 6: Intervention. The teaching content was guided by the Key Learning Area Curriculum Guide of Technology Education in Hong Kong (Education Bureau, Citation2017) and the Five Big Ideas in AI (Touretzky et al., Citation2023). This study specifically focused on the topics of "perception," "learning," and "societal impact." The AI-based learning tools utilized in this study included Google Quick Draw and Google Teachable Machine. Over five consecutive days, the teacher implemented the lessons designed for this study. On the first day, students were given a general introduction to AI. Then, focused lessons on perception and machine learning were conducted over the next two days. The fourth-day lesson mainly elaborated on the societal and ethical impacts of AI. The lesson concluded with students' presentation on the last day. The application of SAILD in each lesson is depicted in Figure 3.

3. Day 7: Post-test. The day after the intervention ended, students were given a post-test that mirrored the structure of the pretest. While the specific questions in the multiple-choice quiz differed, the level of difficulty was maintained for consistency. The post-test was administered solely for research purposes to evaluate the effectiveness of the intervention. Students were not provided with their individual scores, as the primary aim of the post-test was not to serve as a feedback tool for them but rather to gather research data within the constraints of the available time.

4. Day 10: Semi-structured interviews. Focus group interviews were conducted with the same student groups who participated in the lessons. Each focus group interview lasted 30-45 minutes. The interviews aimed to gather more nuanced feedback on students' learning experiences and perceptions of SAILD and provide a supplementary understanding of students' AI literacy.

Data collection and analysis

Knowledge test (KT)

A multiple-choice knowledge test (KT) was specifically designed to measure students' conceptual understanding of AI. It was reviewed, revised, and validated by three AI experts, two elementary school STEM teachers and two AI educators to ensure content accuracy and appropriate difficulty level for elementary students. It was then piloted with 30 Grade-6 students from the same school. We selected Grade-6 students for the pilot test for the following reasons. Primarily, the 50 research participants were already the population of the Grade-5 students in the participating school. To preserve the integrity of the pretest data, these students were not included in the pilot test. Instead, Grade-6 students were chosen, as they share comparable academic experiences with the Grade-5 students (i.e. both grades are allowed to select the same STEM extracurricular activities in the school). Additionally, selecting students from the same school ensures a consistent socioeconomic background with the research participants, thereby maintaining contextual continuity in our study.

The final version of the KT contained 20 questions, consisting of 5 questions on general AI understanding, 5 on perception, 5 on machine learning, and 5 on societal and ethical impacts of AI, covering the entire content of the intervention. Table 3 shows the 4 constructs and a question example in each construct. Items in the pre- and post-tests varied in wording but maintained the same content structure and difficulty level. Paired-sample t-tests were employed to examine the difference between pre- and post-tests.

Survey instruments

To investigate students' AI attitudes and other affective perspectives, several self-reported surveys were adopted. The first was the general attitudes toward AI scale (GAAIS) by Schepman and Rodway ( Citation2020) (see https://doi.org/10.6084/m9.figshare.27073942.v1); six items for positive attitudes and six items for negative attitudes were selected. The internal consistency of the original scale was good, with α = 0.88 for Positive GAAIS and α = 0.83 for Negative GAAIS. The second was the motivation scale by Glynn et al. ( Citation2011) (see https://coe.uga.edu/outreach/programs/science-motivation/); eight items from the first factor, intrinsic motivation, were selected. The motivation scale also had good internal consistency (α = 0.92). All the items were contextualized in AI. For example, the term "I intend to continue learning XXX" was changed to "I intend to continue learning AI." Paired-sample t-test was used to examine the difference between pre- and post-survey.

Qualitative data

In addition to the knowledge test and surveys, two types of qualitative data were collected. The first was the user portfolios created for each group (10 portfolios in total), which consisted of structured worksheets (WS) and design solution plans (DS). The WS contained questions that targeted students' conceptual understandings of the teaching content, such as "What are the differences between AI and non-AI technology?." The DS consisted of four major sections: an overview of the solution, background research, a detailed explanation of the AI-powered solution, and a reflection on the plan. The WS and DS were reviewed, evaluated, and revised by three AI experts, two experienced STEM teachers and two AI educators to ensure their quality. These portfolios served as a rich source of evidence, showcasing students' statements and reflections on AI knowledge, AI skills, AI ethics, and AI attitudes. The second source of qualitative data was derived from semi-structured interviews, which were designed to delve deeper into the students' AI attitudes, AI ethics, and their perception of the activities of SAILD through open-ended questions. Sample questions included: "Which activity do you like/unlike most and why?" and "How do you think the design work can or cannot help with your understanding of AI, and why?."

A deductive coding approach was employed to analyze the qualitative data. This approach, grounded in the preexisting theory, guided the initial stages of the analysis (Bradley et al., Citation2007; Burnard et al., Citation2008). This method involves applying a predetermined set of codes, often referred to as a start list (Miles & Huberman, Citation1994), to identify certain key aspects of the data that directly relate to the research goals (Bryman, Citation2014). In this study, deductive coding was conducted using a start list derived from the AI literacy categories ( Table 4). We utilized the research goal as a lens to assign emerging codes to the portfolios and interview data, exploring the impacts of SAILD on students' AI literacy (Elo & Kyngäs, Citation2008). We further analyzed the coded data using the thematic analysis framework described by Braun and Clarke ( Citation2006). This involves a systematic process of coding and identifying patterns within the data, thereby enabling us to construct meaningful themes related to AI literacy. Through this comprehensive qualitative analysis, we aim to construct a holistic depiction of Grade-5 students' AI literacy development and contribute valuable insights into the effectiveness of SAILD in AI education, offering a nuanced perspective on pedagogical outcomes.

Results

Acquisition of AI knowledge

Paired-sample t-test indicates that students had significant improvement in the KT after the intervention ( M = 13.77, SD = 3.38) compared to the pretest ( M = 9.52, SD = 2.58), with a large effect size ( p < .001, Cohen's d  = 1.67).

For AI knowledge, we are most interested in students' conceptual understanding of the term "AI." Students were asked to answer the question on the worksheet: "What do you think AI is?" at the beginning of the first lesson and at the end of the last lesson. Table 5 summarizes the themes before and after the intervention, with multiple expressions coded independently. Before the lesson, the most common conceptual understanding of AI held by students was "robots," followed by "interactive features." For example, S23 wrote in the worksheet, "AI is a robot." S12 answered, "AI is a machine that can communicate with humans." The remaining students used AI-powered products they had been using to represent AI. For example, S07 stated that "AI is Siri." After the intervention, more themes were summarized regarding the same question. The first is "AI is a simulation of humans." Sample answers included "AI can simulate the thinking process of a human" and "AI can make decisions like humans." "Machine learning" stands out as the second common theme, such as "AI needs learning supervised by humans" and "The difference between AI and non-AI is the ability to learn." Around one-fifth of the answers mentioned "solve a real-world problem." For example, "AI can solve complicated problems such as recognizing people." However, about a quarter of the answers still focused on robots and interactive features, which is identical to the pre-lesson perception.

Notably, while the overall score of AI knowledge among students increased, as evidenced by the results of the KT, there remain significant gaps in their conceptual understanding. A concerning observation is that 25% of students continued to hold a rudimentary view of AI after the lesson, equating it with specific robots or other AI-powered products. Furthermore, approximately one-third of the students exhibited a partially correct understanding of AI, seeing it as a simulation of human intelligence. This viewpoint, while closer to the reality of AI functions, still misses the nuanced differences between AI processing and human cognition.

Development of AI skills

Regarding AI skills, students demonstrate their competencies in two dimensions: determining the problems that can be solved by AI and training a supervised learning model. In the first lesson, students were required to answer the question, "When do you think we should use AI to solve a problem and when not to?" In the design task, the guiding question was "Describe in general your plan to protect the target animals; which part will you use AI and which part will use the non-AI technique?" Table 6 summarizes the themes before and after the intervention. Before the intervention, most students either used real-life products such as auto-drive and Siri to represent what problem to be solved by AI (3 groups), or believed that AI was used to solve problems beyond human ability (4 groups). However, none of the students explicitly expressed which problems were more appropriately addressed by AI and which were better suited for non-AI solutions. In the design solutions after the intervention, all groups were able to successfully identify problems that could be solved using AI, and distinguish other procedures that might employ non-AI techniques in their design solutions. Specifically, students identified two types of problems suitable for AI: using AI to find the target (8 groups) and using AI to make decisions (2 groups), with actions carried out by non-AI systems to solve the problem. For example, Group E answered, "To protect polar bears, I will build many small robots and put them on the ice. They will search and monitor the polar bear using AI. Then the robots will send the information to humans automatically; no need to use AI (the part of sending a message)."

When training a supervised learning model on Google Teachable Machine, students went through the process involving feature selection, label identification, and model training and testing. Most of the groups (7 out of 10) described strategies for data selection and classification within the machine learning process, with two discussing continuous model improvement. Table 7 lists some of the strategies students employed.

In addition to the design solution, the post-interview also shed light on the impacts of SAILD on student development of AI skills. Students acknowledged the utility of AI in problem-solving, as evidenced by responses such as "The design project is helpful (to learn AI) because I can experience AI functions and how AI can be used to solve a real-life problem" (Group A). Furthermore, students expanded their understanding of AI applications through the design activities, with remarks like, "I think the design activity provides more ideas on how to use AI" (Group E) and "Through the design, I realized that AI can be used in this way" (Group F).

Understanding of AI ethics

As for AI ethics, the paired-sample t-test also shows a significant improvement in the post-test ( M = 3.38, SD = 1.06) compared to the pretest ( M = 2.44, SD = 1.17), with a large effect size ( p < .001, Cohen's d  = 0.98). Students demonstrated their ethical consideration of AI in the DS. Most groups (9 out of 10) considered the accuracy of AI-generated results, highlighting it either as a potential weakness of their plan or as a facet requiring human verification. Additionally, some students (4 out of 10) acknowledged the potential risks associated with their designed products being used for malicious purposes. For example, when answering the guiding question "What potential problems do you think your solution will have, and how to solve it?" most groups considered the accuracy of AI decision-making, such as "It needs a human to manually review the pictures (of targeted animals)" (Group E), as well as the potential misuse of AI. For example, "While AI can find the whale, it might also provide information to bad people, maybe poachers, and they will use it to hunt them" (Group B).

During the interview, two themes related to AI ethics emerged: critical consideration of the consequences of using AI and the social impact of using AI, listed in Table 8. For critical reflection on the consequences of using AI, students recognized the need for human oversight in AI applications, particularly in scenarios involving image recognition, noting that "it is not always accurate." The design projects further enabled students to comprehend the potential risks of AI technology and underscored the importance of addressing these risks. Regarding social considerations, the design activities fostered an understanding of the wider societal implications of AI, as evidenced by responses such as "I realize that using AI for problem-solving can benefit individuals and society" and "I am interested in using AI to protect animals in my future career," illustrating the perceived societal advantages of employing AI.

Attitudes toward AI

Regarding AI attitudes, the survey achieves a reliability of 0.72 for positive attitudes and 0.71 for negative attitudes, which is acceptable for further analysis. Paired-sample t-test indicates that students held fewer positive attitudes in the post-test ( M = 3.58, SD = 0.52) than pretest ( M = 3.82, SD = 0.69) in general ( p < .01). Especially for the item "Most of the society will benefit from a future filled with AI" (from 3.96 to 3.67) and "AI system can outperform human" (from 3.50 to 3.02). Regarding the items of negative attitudes, the results reveal a less negative attitude in the post-test ( M = 3.14, SD = 0.82) than in the pretest ( M = 3.36, SD = 0.74) in general ( p < .05), especially on the item: "I shiver with discomfort when I think about future uses of AI" (from 3.10 to 2.67). To conclude, the changes in positive and negative attitudes suggest that students had more rational and neutral standpoints toward AI. They tended not to believe that AI was omnipotent, nor did they fear the use of AI in the future. The survey on motivation for learning AI demonstrates high reliability overall (0.93) and in each factor-interest (0.85), confidence (0.89), and utility (0.77). The results reveal an upward trend in students' motivation. Specifically, in the dimension of perceived interest, the mean increased from 3.51 (SD = 0.90) in the pretest to 3.95 (SD = 0.62) in the post-test ( p < .01). Similarly, in the dimension of utility, the mean rose from 3.67 (SD = 0.74) in the pretest to 4.00 (SD = 0.56) in the post-test ( p < .01). No significant differences in confidence are observed. These results suggest that students are increasingly recognizing both the engaging nature of AI and its practical value.

Students also expressed their attitudes toward AI in the interview. When asked the question, "Do you think AI is good?," most groups had double-sided answers. For example, Group F mentioned, "I think there are both good and bad aspects of AI. If the AI is designed with a bad purpose, then it is not a good AI." Group E stated, "AI is good, but it might make mistakes. When it makes mistakes, it will become a problem."

Discussions

Students' development of AI literacy: strengths and weakness

The learning outcomes in students' AI literacy present a nuanced picture. Despite the overall increase in AI knowledge as indicated by the KT change, there were considerable gaps in their conceptual understanding. Notably, 25% of the students continued to hold a simplistic view of AI, equating it with robots capable of human-like interactions. This misconception underscores the teaching challenges of AI, as students often develop a preconceived understanding of AI based on its everyday applications, which impedes their ability to grasp more nuanced concepts (Dai, Citation2024). Students' prior knowledge primarily focused on AI applications such as robotic vacuums, Siri/Alexa, and YouTube, without a deep understanding of the underlying concepts and mechanisms, such as how computers collected and processed data for representation and reasoning (Ottenbreit-Leftwich et al., Citation2023). Additionally, about one-third of the students showed a partially correct understanding of AI, interpreting it as a simulation of human intelligence. Although this perspective was closer to the actual functions of AI, it overlooked the crucial differences between AI processes and human cognition, illustrating the challenge students faced in comprehending abstract and complex AI concepts. It suggests that considerable efforts have been devoted to decreasing the cognitive load for students learning about AI. However, some risks have emerged, such as the potential oversimplification of the complex and diverse nature of AI technologies (Aung et al., Citation2022; Vartiainen et al., Citation2021). According to Dai et al. ( Citation2024), employing analogies can be helpful but might also result in overly simplistic comparisons between AI operations and human thought processes.

The intervention has notably improved students' AI skills and their awareness of the ethical and social implications of AI, marking a vital step forward. This progress suggests that students were not only successfully solving problems with AI, but also beginning to contemplate the broader impacts of AI on society and recognize the ethical dilemmas and societal challenges it poses-a crucial aspect of holistic AI literacy. The inclusion of societal and ethical considerations in AI education across K-12 levels is crucial (Sanusi et al., Citation2023). Ali et al. ( Citation2019) emphasize the importance of educating the next generation of technologists on both the ethical and societal impacts of technology, encouraging them to view AI not merely as a tool. Furthermore, Skirpan et al. ( Citation2018) showed that integrating ethical dilemmas into the computer science curriculum can significantly enhance student interest. SAILD responds to these calls to cultivate ethical awareness and social responsibility among young students regarding AI technologies they encounter daily.

Another significant finding relates to the shift in students' attitudes toward AI following the intervention. Despite the rapid advancement of AI, public comprehension has lagged, fostering misconceptions about its capabilities and associated risks (Brauner et al., Citation2023). The media often blur the lines between realistic AI portrayals and speculative, human-centric visions of intelligence enhancement (Hansen, Citation2022). Consequently, there is a discrepancy between the public's view of AI as a universal solution and its actual, more constrained utility in addressing specific, clearly defined problems (Upshall, Citation2019). This gap leads to unrealistic expectations and misunderstandings about the current capabilities of AI among the public, including young learners. However, this study reveals a decline in both overly optimistic and pessimistic attitudes among the participating students, indicating the development of a more balanced and realistic understanding of AI. This shift suggests a maturing view of AI, transitioning from a universal remedy or a menace to a nuanced tool with both possibilities and constraints.

Insights from design projects

The design project in our study provides substantial insights into the educational dynamics of AI literacy. Significantly, students exhibited marked improvements in their practical AI skills, ethical understanding, and overall attitudes toward AI, indicating that the hands-on, project-based approach successfully engaged them and enriched their perceptions of AI's real-world applications and ethical considerations. This is further illustrated by the findings of Huang et al.'s study ( Citation2024), which indicate that design projects provide opportunities for students to switch their perspectives among designers, users, and evaluators, thereby sensing multidimensional needs. The perspective switching encourages students to ponder the benefits and risks of AI and consider its societal impacts to negotiate the needs of different stakeholders.

Nevertheless, the design activity did not notably advance the conceptual understanding of AI. This discrepancy might stem from DBL's focus on practical skills and applications rather than theoretical comprehension. Although SAILD emphasizes the research part to align the taught concepts with the design process, which aims to resolve the gaps in conveying in-depth content knowledge of DBL (Kafai, Citation2005; Kolodner et al., Citation2003), it lacks the theory-based scaffolding necessary to effectively unpack sophisticated AI concepts for students. Consequently, while students become proficient in utilizing AI tools and evaluating their impacts, their understanding of the fundamental principles of AI remains rudimentary. This situation highlights a potential shortfall in the current educational model for AI literacy. To bridge this gap, an integrated pedagogical framework that merges the hands-on, project-oriented approach of DBL with theory-based direct instruction could be beneficial. This approach aligns with the research findings of Henry et al. ( Citation2021), which indicate that elementary students require more concrete and descriptive explanations in their learning activities. The work of Samuelsson ( Citation2024) also provided empirical evidence supporting the significance of combining teacher-led instruction with child-centered learning in programming robotics education. Such a comprehensive framework might enable students to not only practically apply AI but also grasp the core concepts and working processes underlying AI technologies.

Moreover, the design activities emphasize the educational value of constructionism and the creation process in AI, reflecting the transition from traditional, physical artifact creation to digital innovation (e.g. Ng & Cui, Citation2021; Rich et al., Citation2024; Weng et al., Citation2023). This study introduces an alternative perspective, highlighting problem-solving with digital examples over the production of physical artifacts. By utilizing accessible and open AI platforms like Teachable Machine, students could bring their solution proposals to life in a virtual setting. The elimination of expensive tangible AI resources, such as AI-enabled robots, significantly reduces the barriers to AI education, allowing students from varied socioeconomic backgrounds to engage and learn AI.

Implications and limitations

The results of our research have several implications for the evolution of AI literacy education. Firstly, the persistence of AI misconceptions among students-such as the tendency to associate AI primarily with robots or other AI-based products-underscores the necessity for a more refined educational approach to scaffold students in constructing a deeper understanding of the underlying principles of AI. The prevalence of oversimplified perceptions of AI indicates that current educational strategies may not adequately address the sophisticated nature of AI technologies. As a result, there is a significant need for educators to devise and implement curriculum enhancements aimed at enriching students' conceptual understanding of AI. This would involve transcending mere surface-level applications to delve into the intricacies and foundational principles of AI systems.

The notable enhancement in students' skills and comprehension of the ethical and societal consequences of AI marks a commendable progression toward more responsible and enlightened interactions with technology. This development attests to the effectiveness of incorporating ethical considerations within AI practices, resonating with the broader educational objectives of promoting critical thinking and moral discernment (Zhou et al., Citation2020). It is imperative for educational policymakers and curriculum developers to acknowledge these findings, emphasizing the vital role of ethics in technology education to prepare students not only as proficient users and creators of AI but also as conscientious members of a technologically advanced society.

Moreover, the varied outcomes from the design project underscore both the strengths and constraints of DBL in AI educational contexts. The combination of AI with social good as a design project topic encourages students' active participation in the learning activities, which is consistent with the research findings of Huang et al.'s ( Citation2024) work. Although DBL has been shown to be effective in fostering practical abilities and ethical insights, its influence on enhancing conceptual understanding appears to be constrained. This necessitates a reconsideration of how to effectively employ a student-centered constructionist learning approach. We suggest integrating DBL with conventional educational methods to create a more comprehensive AI learning experience that endows students with both the practical competencies required to deploy AI and the foundational knowledge necessary to comprehend its mechanisms.

A notable limitation of this study is the short-term interventions and evaluations, which may restrict the potential impacts of SAILD on AI literacy. For example, the limited time for students to process abstract concepts, coupled with their exposure only to Google Teachable Machine and Google Quick Draw, may constrain their understanding and perception of AI. Long-term investigations remain needed to gauge the enduring effects of SAILD and to track the progression of students' comprehension and attitudes toward AI over time. Furthermore, another possible challenge to this study is the sample size and research site. This study involved 50 students from a single school in Hong Kong, where the socioeconomic status (SES) of students was relatively high compared to other developing countries. Consequently, there is uncertainty about whether the pedagogical design of SAILD can be effective for a larger and more diverse student population with varying SES backgrounds.

Conclusion

Although the integration of AI education into the K-12 context has garnered significant global attention, there is a dearth of empirical research exploring effective methods to support students in acquiring comprehensive AI literacy, particularly in upper elementary schools. The present work developed and empirically evaluated SAILD. It also highlighted the design component as the driving force, operating in parallel with the research component to support simultaneous knowledge construction and application by students. The results indicated overall positive impacts on students' AI literacy development. The intervention significantly improved students' AI skills and awareness of AI's ethical and social implications, while fostering more rational and neutral standpoints toward AI. Further efforts could focus on providing sufficient scaffolding to support students' conceptual understanding of AI in DBL. Our work pioneers the integration of DBL into AI literacy education and informs the development of AI-focused pedagogy in elementary education. SAILD can be further extended and adapted to K-12 education to offer school-age students an accessible and engaging pedagogical approach.

Ethical approval

Research approval was obtained from the Survey and Behavioral Research Ethics Committee of the corresponding authors' institution.

Informed consent Statement

Every participant had read and signed the voluntary informed consent before he/she participated in the study.

Disclosure statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Data availability Statement

The data that support the findings of this study are available on request from the corresponding author.

The work described in this paper was substantially supported by the Department of Curriculum and Instruction, The Chinese University of Hong Kong.

Notes on contributors

References

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42. https://doi.org/10.3102/0013189X018001032

Huang, B., Jong, M. S. Y., Tsai, C. C., & Shang, J. (2024). Unlocking students' creative potential in designing technological-enriched design solutions. Journal of Research on Technology in Education, 1-17. https://doi.org/10.1080/15391523.2024.2342915

Piaget, J. (1952). The origins of intelligence in children (M. Cook, Trans.). W. W. Norton & Co. https://doi.org/10.1037/11494-000

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Students as AI literate designers: a pedagogical framework for learning and teaching AI literacy in elementary education

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The widespread adoption of artificial intelligence (AI) technologies in daily life has underscored the importance of AI literacy in a technology-driven future, highlighting the need to equip citize...

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คำค้นหา : การออกแบบกิจกรรม STEAM ในระดับประถมศึกษา ของต่างประเทศ

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คำที่ใช้ค้นหาdesigning STEAM activities at primary level

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หัวข้อที่ใช้สืบค้น : STEAM learning activities primary education ในประเทศไทย

https://so06.tci-thaijo.org/index.php/dhammathas/article/view/287009

หัวข้อที่ใช้สืบค้น : Design-based STEAM

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กฤษดา สงวนสิน, คมกริช แม่นยำ, ปัฐมาภรณ์ พิมพ์ทอง

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ปีที่ 20 ฉบับที่ 2 (2026): วารสารพัฒนาการเรียนการสอน มหาวิทยาลัยรังสิต | วารสารพัฒนาการเรียนการสอน มหาวิทยาลัยรังสิต

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คำค้นหา : การออกแบบกิจกรรม STEAM ในระดับประถมศึกษาในประเทศไทย

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คำที่ใช้ค้นหา กิจกรรม STEAM ผลงานไทย1งาน ระดับประถมศึกษา

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วิธีการค้นหา : พิมพ์ Designing STEAM activities at primary level

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Enhancing 21st century skills of primary school students in rural areas through STEM activities†

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Primary school students studying in rural areas in Türkiye are among the disadvantaged groups in the context of the principle of equality of opportunity in education. It is thought that providing S…

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วิชญาพร 002 (งานต่างชาติ)

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ใช้คำค้นหา : Designing STEAM activities at primary level

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Centre for Learning and Teaching, University of Groningen, Autonomous University of Barcelona, 08193 Bellaterra, Spain

Faculty of Education, Autonomous University of Barcelona, Cerdanyola del Vallès, 08193 Bellaterra, Spain

Authors to whom correspondence should be addressed.

1. Introduction

All across the world, there is a need to address environmental and social challenges, such as overpopulation, resource management, health, decrease of biodiversity or even the impact of new technological tools. These challenges are expected to increasingly gain importance and demand for a multi-layered response and a collective effort which will ask for informed decisions from the whole population [ 1]. Therefore, part of this challenge pertains to education, where many countries worldwide have already adapted their educational goals to focus on competency-based standards. In this context, STEM education emerges as an essential factor to enable decision making and forces us to rethink more suitable ways to be competent in the STEM disciplines in today's society. Part of this rethinking process has already pointed out the need to adopt intersectional lenses to understand STEM from both affective and epistemic domains [ 2]. Only then can we make sense of the current low interest towards STEM from specific groups of people and identify the best ways to interconnect such disciplines for all to gain STEM literacy.

The following discussion on STEM integration encompasses issues such as which forms of knowledge and disciplinary practices should STEM be accounted for, as well as the best methods of integrating disciplines, through transdisciplinary, interdisciplinary, multidisciplinary approaches, and more [ 3]. The discussion has become even more complex with the addition of the "A" for Arts in STEAM, where controversy is raised around its understanding and its role [ 4].

In this still confusing scenario with different interpretations of STE(A)M, national and international plans for promoting STEM and STEAM education are deployed. In some contexts, these plans are normally turning into a wide diversity of methodological approaches that claim to address STEAM literacy in secondary education.

One of the available options to promote STEAM education is project-based learning (PBL), due to its characteristics that potentially facilitate 21st century skills that are essential for addressing the aforementioned challenges [ 5]. We believe that a clearer focus on how STEAM PBL becomes tangible in the day-to-day classroom is key to make sense of how teachers set and share expectations and reach decisions around activities, and find ways of assessing or strategies to unfold contents. Therefore, we refer to STEAM PBL instructional designs as comprising the entire set of resources established in the educational instructional units within the PBL pedagogical framework that serve to acquire STEAM competences. While there is increasing empirical research around effective examples of STEAM implementation through PBL instructional designs [ 6], there is still little literature that showcases how these different STEAM PBL instructional designs are displayed in practice.

Particularly, there is little research analysing if these STEAM PBL projects are good teaching and learning scenarios from the lenses of widely accepted frameworks in science education that place the focus on the practices and not only the products of science [ 7]. In a national context, this framework resonates with the ideas of Izquierdo-Aymerich and Adúriz-Bravo [ 8] that embrace the same philosophy and also make operative other key educational principles, such as (1) selecting core ideas of science, (2) context-based learning and (3) formative assessment.

For this purpose, this study focuses on a metropolitan region of Catalonia in Spain, which serves as a compelling case for the empirical development of STEAM PBL in secondary school classrooms. In Catalonia, STEAM education has gained prominence both at the policy and academic levels, coinciding with a broader innovation movement that embraces PBL as a preferred teaching and learning approach [ 9]. At a policy level, the pedagogical plan involves methodological features, such as context-based and student-centred pedagogies. These approaches enable critical decision making in a sustainable and inclusive manner while encompassing a formative assessment perspective [ 10]. The secondary schools in this region serve as pertinent cases for exploring how educators conceptualize STEAM and PBL through the creation of so-called 'STEAM projects'.

To shed light on these matters, this paper aims to characterize the impact of the STEAM movement in contemporary STEAM PBL instructional designs implemented in five schools of Spain from the STEM practice-based framework. This general goal is further concretized into three sub-objectives:

Identify the instructional design sophistication of 46 currently already implemented STEAM projects by utilizing a rubric instrument built from the STEM practice-based framework.

Compare how the STEAM projects gathered from five secondary schools are characterized in their instructional design sophistication dimensions.

Analyse the differences and similarities between schools that exclusively convey science curriculum through STEAM projects from those which also use other complementary methodological approaches.

In the following section, we delve into the theoretical framework from which this paper addresses the previous research questions and facilitates further discussion of the results.

2. Theoretical Framework

2.1. From STEM to STEAM Education

There are already different research contributions that have attempted to shed light on the conceptualization of STEM (Science, Technology, Engineering and Mathematics) [ 11], and STEAM [ 12]. While we hold a better understanding of the socio-political origins of the STEM acronym, today there is a rich conjunction of perspectives and motives for STEM education at different educational levels [ 13]. These motives may include the need for learning in more authentic situations, looking at the world from a set of different epistemological lenses, a more active and empowered citizenship, etc. [ 2, 9]. A comparable analysis is conducted concerning STEAM, examining the rationale behind the integration of the Arts (A), and presenting different perspectives on balancing STEM versus Arts disciplines or seeking a more overarching educational term that emphasizes creativity [ 4].

As mentioned, the integration of these specific S-T-E-M disciplines is not arising from an initial academic reflection, which has raised concerns about what is truly new from it [ 14]. Notwithstanding, different authors have explored the epistemological foundations of these disciplines enabling different interpretations to integrate them [ 3, 12, 15]. This discussion has triggered the need of specifying when new instructional proposals are truly "integrated STEAM", which preferably adopts interdisciplinary and transdisciplinary approaches of discipline integration [ 12].

The appearance of the A for "Arts" in STEAM has also placed the discussion into the Arts meaning and its role in conjunction with the rest of the STEM disciplines. From the current literature, Arts are normally related to a set of disciplines such as liberal arts (humanities, social studies, language) and visual and fine arts (also considering physical and musical disciplines) [ 16]. At the same time, this "A" can be strategically introduced to provide a better balance of disciplines or enhance certain skills and attitudes, such as creativity, critical thinking and innovative spirit [ 4, 17].

From the ongoing reflection about STEM and STEAM, we align with those conceptualizations that are grounded on promoting STEM literacy for all students. From our perspective, this purpose entails two clear needs: developing students' STEM competence, which demands a revaluation of STEAM practices from an epistemic reflection and developing students' identities in a way that are caring and reconcile students' selves with how they think STEM people are [ 2].

By understanding how learners relate and identify with STEM, better inclusive initiatives that allow students to bring their own prior experiences, practices and ways of being to the arena can be developed. This demands not only less stereotyped examples of STEM people, but also paying attention to other aspects such as self-efficacy, aspirations or capacities [ 18]. We suspect that these attempts of designing and enacting more inclusive and equitable approaches into the classroom can be also supported when these approaches are interwoven with the second aforementioned need. By shifting the focus and including more representative practices of STEM disciplines in the classroom, a more naturalized image of these disciplines can be delivered, which at the same time can provide good opportunities to transform the stereotyped and sometimes exclusive self-representations of STEM into an activity that is more inviting to share and discuss everyone's ideas [ 19]. This second need also points out the need to grasp a better understanding of each discipline and how they can relate to each other to enhance STEM competences [ 20]. Within this framework, integrated STEAM education should be accountable for distinguishing core defining elements of every discipline, such as their goals and practices (which may allow for a multidisciplinary approach), from shared elements of different STEM disciplines, such as their problems, methods and values (which can be easier integrated in a more sophisticated manner) [ 21].

When specifically talking about the role of Arts, we find it particularly interesting to accentuate how the Arts conception can be connected with other approaches with a longer tradition in science education, such as Science, Technology and Society (STS) [ 22]. From the mentioned understandings of A in STEAM, the STS approach resonates with the need of integrating Arts as liberal arts, bringing the unavoidable social and humanistic lenses of every complex STEAM challenge. Despite this we adopt a broader perspective that acknowledges and embraces the diversity of forms and roles that Arts play in the STEAM instructional designs.

2.2. Project-Based Learning as a Means of STEAM Education

Among the diverse palette of methodological approaches to convey STEAM education (tinkering, design-based learning, inquiry-based learning, maker movement, etc.), PBL has been one of the preferred ones [ 6, 15, 16, 23]. This alignment seems natural due to the overlapping interests of STEAM education and the overarching purposes from which PBL is normally justified. As an example, both STEAM and PBL educational approaches demand students to have an active role in their learning and their tasks by connecting them to the school community and tangible outcomes, which are normally referred attributes of PBL [ 24, 25].

The literature regarding PBL in science education started in the 1990s [ 26, 27], where the influence of the evolving trends enabled the conceptualization of the so-called project-based science (PBS). This PBS is conceptualized with 6 key features: (1) starting with a driving question with an implicit problem, (2) focusing on key learning goals, (3) engaging in scientific practices to answer the driving question, (4) participating in collaborative activities, (5) use of learning technologies to scaffold the practices of science, and (6) creation of a tangible product as an external representation of the class learning [ 28, 29]. In this context, some authors have pointed out the complexity of finding a balance between the engagement in in-depth curricular contents and the practical effect of the project [ 30]. The author describes it as an "unresolved tension" that especially appears in "performance PBS" projects, which are defined as those whose principal activity is designing, within the sense of design-based science [ 31]. This performance PBS approach to convey the Science curriculum seems to be the most commonly found when looking at the literature about PBL within STEM education [ 32, 33]. Even when other existing initiatives emphasize the role of inquiry in STEM PBL instructional designs [ 34], a more general conception is frequently adopted around inquiry, which may become distanced from the more scientific purpose of it [ 27]. In this scenario, further insight is necessary to understand how STEAM education may be dealing with these tensions that have been also reported in other science learning contexts [ 35, 36].

3. STEM Practices as an Analytical Framework to Study STEAM PBL

In order to analyse STEAM projects, this paper opts for a perspective that enables insight from a coherent and operational framework that focuses on teaching and learning STEM practices in the same trend as Science and Engineering practices are already jointly presented [ 37].

We align with those frameworks that focus on the practices of STEM disciplines since they are widely supported by many researchers and educational policies [ 7] and resonate with the reflections from philosophical, psychological and historical reflections in the context of science education [ 38]. From this perspective, a cognitive model for STEM education can be developed in a similar way, problematizing how students engage in the different spheres of activity of Science [ 39] and Engineering [ 21]. More recent literature with the same focus on the practices also explores the synergies and boundaries between them, for example, between Science, Engineering and Mathematics [ 2] or between STEM and Arts [ 40]. Part of the endeavour of being STEM competent is therefore being knowledgeable about the epistemic objectives of each discipline. For example, the practice of 'analysing and interpreting data' can imply the mobilization of very different skills for different purposes. In Science, data analysis serves the purpose of improving descriptions and explanations of phenomena; for Engineering, it enables the identification of improvement points; and it allows for the verification of mathematical solutions in Mathematics [ 2].

In the context of this study, this practice-based framework aligns and is further operationalized by the School Scientific Activity framework, which also interprets that science classrooms must emulate the social, discursive and cognitive activities of scientists while the pursued goals, methods and final mental constructs should be tailored for the school settings and purposes [ 8, 41]. We believe that the SSA framework provides extra insights into the STEM practice-based framework since it allows to grasp other key aspects for both scaffolding instructional designs and promoting adequate STEAM teaching and learning. More specifically, it provides operative guidelines to (a) address context-based proposals [ 42], focusing on the role of context towards a balance between the sense making of phenomena and finding personal relevance for the learner; (b) structure knowledge in core ideas in a coherent way with other proposals [ 7, 43]; and (c) focus on formative assessment practices embedded in the school STEM activity [ 44].

4. Methods

This paper is part of a greater research project that is inspired by ethnographic approaches. Therefore, it is aimed to address and better understand teaching beliefs, practices and choices around the emerging use of STEAM PBL instructional designs in the context of a Spanish region (Catalonia). The research methodology is presented from an interpretative paradigm where both qualitative and quantitative data were used. Therefore, we conceptualize the term impact in an exploratory and qualitative manner, where the resulting sophistication levels in STEAM PBL instructional designs are informative of differential characteristics influenced by the STEAM approach we stand for.

4.1. Data Collection

In order to gather STEAM PBL instructional designs, normally referred to as simply "STEAM projects", a selection of five high schools that design and implement STEAM and STEM projects was performed in convenience for this research. Secondary school centres were chosen for the following key features:

Secondary school centres belong to a self-organized network of schools that discuss and develop curriculum materials with a PBL methodological approach.

Secondary school centres are socially and culturally diverse and representative of the region they belong to.

Every secondary school centre implements the PBL methodological approach in different pedagogical settings in terms of schedule structure, project duration, teacher expertise, integration with other methodologies, etc.

Contact with the different high school centres was maintained from 2017 to 2019. In this period, the authors had formal and informal interviews with different teachers involved in STEAM project design and participated in one of the school network meetings. These interactions with the participant schools allowed the authors to share the aim of this research and gather the required STEAM project data. In this process, participant schools showed a rich variety of ways to approach STEAM education. This diversity involved different ways of integrating STEAM disciplines and different ways of understanding the "A" in STEAM. Further information of the characteristics of every school are available in Appendix A.

Collecting STEAM PBL instructional designs from schools entailed gathering data in various formats, including virtual curriculum materials, teaching guides, curriculum planning documents, assessment tools, webpages, etc.

The criteria used to select STEAM PBL instructional designs were as follows:

PBL instructional designs should be acknowledged as "STEAM projects" by the teachers who design and implement them. "STEM projects" were also included when "Arts" were explicitly integrated in the forms of plastic/musical arts, liberal arts and creativity skills.

PBL instructional designs should incorporate operative elements of the PBL methodology and Science and Technology curricular standards.

PBL instructional designs should have been tested at least once with 12-14-year-old learners.

Since this research is aimed to embrace a rich perspective around STEAM education, as authors, we did not limit the understanding of STEAM to a constraining vision or conceptualization. At the same time, we did not exclude STEAM projects that also embraced other compatible educational approaches (STS, IBSE, etc.). From the 49 STEAM projects gathered, 46 followed the required criteria for this study (see Appendix C). The titles of these STEAM projects provide insights into a diverse range of learning contexts and outcomes. They span from constructing a car for a competition (seen in the 'Electric Car Race' project) to engaging in a climate-focused conference ('Air Water Congress' or the 'Pollution, Health, and Environment' project).

4.2. Research Instruments

In order to evaluate the impact of the STEAM movement on real STEAM projects from the perspective of the SSA, we used theSTEM PBL rubric instrument [ 36] due to the following reasons:

The STEM PBL rubricis already published and its content validated in an evaluative process.

The design and validation process was part of the same research within a greater research project, providing to the rubric a strong theoretical foundation in the use of the PBL methodological approach in Science and Technology.

Participants in rubric design were Science and Technology teachers and science education academics that were both informed about the SSA framework and the socio-educational context where the study was developed.

The rubric offers a thorough analysis of STEAM projects since it considers 21 rubric criteria that enable evaluation of 6 different fundamental aspects of PBL in Science and Technology education. Therefore, a wider perspective can be offered as compared to other available rubrics [ 45].

Despite the benefits of this rubric, it should also be outlined that the SSA framework has traditionally been focused on Science and Technology education, and requires further exploration of mathematics aspects, which are not considered in this study.

The STEM PBL rubric is distinguished by a collection of 21 criteria (as listed in Appendix B) organized under 6 fundamental aspects: (a) Project Objectives; (b) Action; (c) Context; (d) Contents; (e) Science and Engineering Practices; (f) Assessment, ICT, and Collaboration. The authors also pointed out that 18 of these criteria can be divided between those that serve discipline-specific objectives of Science and Technology education (disciplinary criteria) and those that serve non-discipline-specific objectives of education, so-called meta-disciplinary criteria. The other three rubric criteria integrate both types of purposes.

Within each criterion, a breakdown of four distinct levels of sophistication was provided showing specific characteristics in a short description. These levels collectively delineate the path towards design enhancement. The term "sophistication" was used to describe the complexity of instructional design across each criterion.

4.3. Data Analysis

Data analysis implied two phases using qualitative and quantitative data. In the first phase, qualitative data were used from the curriculum materials of STEAM PBL instructional designs. Every facilitated document was coded per centre and STEAM project. A descriptive summary for the distinctive elements of every STEAM project was created to facilitate an overview and further evaluation. To showcase the relevance of this task, certain STEAM project summaries described which types of activities were more frequent than others and thus facilitated further interpretation of the significance of these activities in each STEAM project. In other cases, a STEAM project summary was relevant when it facilitated the interpretation of a potential mismatch between the explicit general learning outcomes and the activities the learners were involved in.

In this phase, every STEAM project was later interpreted by means of the STEM PBL rubric. This task involved analysing the STEAM project primary and secondary (summaries) documents and linking them to one of the four available descriptors for each rubric criterion ( Figure 1). For every criterion, an explicit justification for the selected sophistication level was provided.

As an illustration, an excerpt from the STEM PBL rubric is included to demonstrate the progression in complexity concerning how STEAM projects engage with conceptual contents ( Table 1). STEAM projects that introduce fragmented or unconnected information are categorized as level 1 in terms of sophistication. In contrast, those STEAM projects structured to encounter and systematically unfold complex scientific core concepts, such as Energy, Chemical Change, or Life Forms, are affiliated with higher sophistication levels.

This phase resulted in a dataset of 46 STEAM projects with 21 sophistication levels linked to each one (ranging from 1 to 4). To warrant reliability of this first-phase analysis, two external researchers analysed 9 random STEAM projects using the rubric. Most rubric sophistication levels were identically interpreted and those which were not identical were further discussed and agreed on their interpretation for the rest of the STEAM projects.

The second quantitative phase implied the use of descriptive statistics to analyse the generated dataset. In order to answer the first research question, the arithmetic means, as well as the standard deviations, were calculated for every STEM PBL rubric criteria. From this information, every criterion mean was discussed with the support of the STEM PBL rubric sophistication level descriptions.

In order to answer the second research question, data from the rubric criteria were pooled for every STEAM project by adding every resulting sophistication level score (from 1 to 4). In this case, only 18 criteria were used to create a STEAM project score. The reason for this reduction of rubric criteria was the need to select just those criteria that were clearly linked to disciplinary and meta-disciplinary aspects of STEAM PBL instructional designs. This data representation method enabled a further interpretation of the STEAM projects' sophistication for these two particular sets of criteria. The scoring system was, therefore, providing two different scores for every STEAM project with a potential maximum score of 32 points for disciplinary criteria and 40 points for meta-disciplinary criteria. Results were represented in a two-dimensional map. To delve deeper into the findings, the meta-disciplinary criteria scores, primarily indicating PBL characteristics and objectives, were used as a practical means to assess how STEAM projects facilitate 21st-century skills. Likewise, the disciplinary criteria were employed as a practical method to evaluate scientific competence. This correlation is endorsed by the creators of the STEM PBL rubric [ 36].

5. Results

The results are presented in three subsections in accordance with the research objectives. Section 5.1 identifies the instructional design sophistication of 46 already-implemented STEAM projects. Section 5.2 shows a comparison between the design sophistication of STEAM projects in five secondary schools based on disciplinary and multidisciplinary criteria. Section 5.3 provides an analysis of the differences and similarities among schools that convey science curriculum through STEAM projects in different ways.

5.1. STEAM PBL Instructional Design Sophistication: A Breakdown of 21 Criteria across 46 STEAM Projects

In this study, 46 STEM projects were analysed by using a rubric instrument that allowed to associate an instructional design score up to 84 points (21 criteria with 4 progression levels) to every project. The average scoring of this set of projects was 45 points with a standard deviation of 8.78, which represents the (53% of the maximum scoring). This result shows the challenge to design highly sophisticated projects according to all the rubric criteria.

In Figure 2, a detailed picture of the average scoring of every criterion for the 46 STEAM projects can be read and further interpreted. As a general trend, dispersion bars inform of a relatively high diversity of sophistication levels for most of the criteria, which points out important differences in those projects acknowledged as STEAM. Hereafter, an analysis of the results is presented for every fundamental aspect assessed, linking each criterion to a certain sophistication level of the rubric (levels 1-4).

Rubric criteria linked to the fundamental aspect of "project objectives", which include curricular goals, didactic goals and project goals, receive a score that ranges between sophistication levels 2 and 4. According to the rubric, this implies that STEAM projects are designed expressing competence-based goals, with a clear intention to develop 21st century skills, such as creativity, critical thinking and collaboration (description of level 2 of the rubric). Some STEAM projects may also show higher sophistication in their goals, explicitly including scientific literacy goals. From a didactic goal criterion, this involves introducing activities that mainly enable knowledge transfer into practice, which align to some extent to the project challenge (levels 3 and 4).

The criteria linked to the fundamental aspect of "contents" (conceptual, procedural, attitudes and integration of contents) largely vary among STEAM projects. The STEAM projects that show low levels for these criteria are mainly selecting descriptive concepts (level 1) or introducing simple scientific concept relations (level 2). Procedural contents normally imply showing (level 1) or making use (level 2) of certain rote procedures or prompted techniques. Values and attitudinal scientific contents normally appear in an implicit way (level 2). These values are sometimes more explicit (level 3) in specific activities that enable working sustainability values, health habits, etc. Contents from different STEAM subjects are normally presented in a multidisciplinary approach with clear divisions among disciplines (level 2) or just clearly integrate the contents and practices of one subject while the rest do not hold a relevant role (level 1).

Rubric criteria linked to "contextualization" evaluate scientific significance, relevance and authenticity. STEAM projects from this study normally involve natural and physical phenomena (level 2) that are sometimes scientifically interpreted (level 3 and 4). STEAM projects also involve learners in plausible situations of the real world (level 3) that respond diversely (levels from 1 to 3) to their personal and social interests.

Rubric criteria linked to the fundamental aspect of "action" inform about a general trend of STEAM projects that execute well developed actions (level 3), which, in some cases, are later evaluated (level 4). These actions are normally guided (level 1), but in some cases there is room for decision making by learners (levels 2-3). Action impact is also variable, from projects whose action is targeted to the classroom to actions that are aimed to involve the whole school community (level 3).

Rubric criteria linked to the "engagement in scientific and engineering practices" evaluate the scientific spheres of activity of modelling, inquiry and argumentation, as well as engineering practices. Argumentation skills are promoted in a broad sense of argumentation in the majority of STEAM projects (level 2). Inquiry practices are quite variable in their presence in STEAM projects but, on average, focus on data collection rather than promoting the use of evidence or the drawing of conclusions (level 2). Modelling practices are mainly focused on descriptive forms of presenting scientific knowledge (level 1) rather than on fostering more guiding interpretations of natural and physical phenomena.

Finally, the rubric criteria linked to the assessment and the use of ICT resources and collaboration show the following behaviours. Assessment design in STEAM projects is focused on co-assessment and formative assessment strategies that make use of fixed criteria facilitated by the teacher (level 2), where the final action has the biggest weight of the summative assessment (level 2). The use of ICT (Information and Communication Technology) resources is diverse, from a limited use to support the presentation of the final action (level 1) to more sophisticated uses to organize and communicate ideas (levels 2 and 3). Collaboration is regulated in a sophisticated manner that encompasses collaboration guiding instruments (level 2 and 3) and sometimes strategies to facilitate ideas exchange (level 4).

From a general perspective, Figure 2 is informative about the general trends in which STEAM projects tend to be sophisticated. Despite a high dispersion for the whole set of criteria, there is clear room for design improvement in fundamental aspects, such as "contents" and "S&E practices". On the other hand, there is also a general trend to design STEAM projects that are sophisticated in the deployment of their actions, the ways to foster collaboration among learners and the way to present and to identify learning, didactic and project goals.

5.2. Distribution of STEAM Projects for Disciplinary and Multidisciplinary Criteria

The representation of the scores of the 46 STEAM projects in 2 dimensions allows us to better understand the diversity of STEAM proposals designed by schools.

From Figure 3, focusing the attention on the two axes, there is a clear tendency in STEAM projects to score better at multidisciplinary criteria (y axis). The cloud of projects is clearly displaced towards the upper part of the map, where no project scores less than 15 points out of 40 available. In contrast, STEAM projects spread all over x axis, showing a greater diversity of scores and therefore instructional design sophistication for disciplinary criteria. Furthermore, the map also shows that a small number of STEAM projects reach high scores in both of the two dimensions presented.

By looking at the map by schools, STEAM projects designed by different schools tend to scatter differently through the map, informing about more similar or different ways of designing STEAM projects within the same school centre. Schools 3, 4 and 5 are very representative of secondary schools that design STEAM projects very alike in their sophistication levels since they cluster in very specific regions of the map. Schools 4 and 5 are normally located in the upper left side of the graph, and encompass STEAM projects that are focused on multidisciplinary criteria but that are rather less sophisticated in S&T disciplinary criteria. In the case of School 3, the clustering is even more clear, since all their designed STEAM projects are in the central top right side of the map.

On the other side, Schools 1 and 2 show a more scattered design of STEAM projects, which are distributed all over the map, mainly across the x axis. This implies that they maintain a certain degree of sophistication for meta-disciplinary criteria (above 15/40 points) while showing a wide sophistication diversity for disciplinary criteria.

5.3. An Analysis of the Differences and Similarities between Schools That Differently Convey the Science Curriculum through STEAM Projects

The results in this section are divided between schools exclusively using STEAM projects for their Science curriculum and those employing additional complementary methodological approaches.

5.3.1. Secondary Schools That Exclusively Convey the Science Curriculum through STEAM Projects

Secondary schools that entirely convey their Science curriculum through STEAM projects have shown to perform differently on the design of their projects considering disciplinary and meta-disciplinary criteria. Schools 1 (n = 10) and 2 (n = 11) show a balanced profile between disciplinary and meta-disciplinary scoring ( Figure 4). Despite this, dispersion of disciplinary criteria ranges from 30% to 70% of the maximum score for School 1. On the other hand, School 5 (n = 13) shows an unbalanced profile between disciplinary and meta-disciplinary scores in their projects and illustrates a tendency to foster meta-disciplinary criteria (56%) over disciplinary criteria (33%).

5.3.2. Secondary Schools That Partially Convey the Science Curriculum through STEAM Projects

From the five centres, Schools 3 and 4 develop their Science curriculum through different methodologies besides the use of STEAM projects. School 3 invests half of its available time for Science learning on developing STEAM projects. For the equivalent purpose, School 4 invests ⅓ of its available time to implement STEAM projects.

These secondary schools that partially convey their Science curriculum through STEAM projects have shown to perform differently on the design of their projects considering disciplinary and meta-disciplinary criteria. School 3 (n = 5) shows a balanced profile between disciplinary and meta-disciplinary scoring ( Figure 5), being the one that shows the highest score average, although its number of STEAM projects is the lowest. On the other hand, School 4 (n = 8) shows an unbalanced profile between the disciplinary and meta-disciplinary scores in its STEAM projects. In this school, meta-disciplinary scores are comparable to other schools, while disciplinary scores vary considerably and obtain a low average score of 27% of their maximum, which is similar to School 5.

6. Discussion

6.1. Discussion of the Empirical Characterization of STEAM Projects

The resulting evaluation of the 46 STEAM projects for every criterion shows a picture where high dispersion among the evaluated criteria suggests that attempts to provide a general definition of STEAM projects in this context are troublesome. While the literature has provided different conceptualizations of PBL methodological approaches to introduce Science (such as STEM PBL, PBS, etc.), we wonder about which characteristics should be defining the features of any STEAM PBL instructional design. Data reported in this study suggest that the high dispersion among criteria could also be aligned with different forms of approaching STEAM education. This conjecture would fit into the recurring attempts in the literature to classify PBL into different types [ 30, 46].

In this paper, we have chosen a basic distinction between disciplinary and meta-disciplinary criteria to provide more insights in this matter. Figure 2 shows that STEAM projects tend to show a better sophistication alignment for those meta-disciplinary criteria that mainly inform PBL features (PBL design elements, its pursuit for 21st century skills, etc.). On the other hand, larger dispersion was perceived in the data for the disciplinary criteria, which mainly inform about Science and Technology teaching sophistication of the STEAM projects. Therefore, it can be stated that issues that aim to find common ground in a generalization of a STEAM project, even for a relatively small sample as provided in this paper, are not focused around conceiving different ways of approaching PBL but different ways of approaching of teaching and learning Science and Technology. This resonates with the different approaches that normally influence STEAM PBL instructional designs, such as design-based science [ 31], inquiry in all their different conceptions [ 28, 34, 47] or simply focusing on teaching of 21st century skills in scientifically relevant contexts [ 48].

Moreover, subtle trends in the data identify potentially concerning aspects of how STEAM projects are designed. While most of the diversity in our data can be aligned with different conceptualizations of the learning of Science and Technology through PBL, the low sophistication levels in how STEAM projects may engage learners in Science and Engineering practices is not encouraged in any conceptualization [ 49]. Therefore, the already identified tensions between different "doing the project" and "learning the content" approaches [ 30] should be better explicated in this context concerning the mentioned STEM practices.

6.2. Discussion of Differences between Subsets of STEAM Projects by Schools

To discuss the results, we propose to divide the map of Figure 2 into its four sections that help to understand how different schools tend to distribute their STEAM projects. It can be argued that disciplinary criteria, through the evaluation of STEAM PBL instructional design, can be informative of how projects support the development of a scientific competence. In the same line, it could be argued that meta-disciplinary criteria are informative of how projects support the development of 21st century skills. Therefore, according to where a project is spotted on this map, we can infer its potential to foster a higher or lower scientific competence and 21st century skills.

STEAM projects scores by school and its implications for each can be organized in three different sets.

Schools 1 and 2 ( Figure 3) convey their S&T curriculum through STEAM projects. They spread in the upper sections of the map ( Figure 2), which is the zone where 21st century skills are better developed. On the other hand, some of their STEAM projects can be really fostering scientific competence through their designs. From the lenses of how these schools manage the tension, it can be discussed that they leverage them by shifting the focus on disciplinary criteria differently on each STEAM project. Despite this, we wonder in which situations scientific competence development represented by disciplinary criteria outperform meta-disciplinary criteria, signalling a clear focus on science engagement and understanding.

School 3 is partially conveying the S&T curriculum through STEAM PBL, which attempts to constantly balance between disciplinary and meta-disciplinary purposes. Despite being in the zone that can be promoting both types of competences ensuring a minimum design quality for all the STEAM projects, the lack of dispersion also quenches the possibility for delving deeper into specific criteria. Therefore, we wonder when STEAM PBL instructional designs should be truly aiming for a balance of these tensions in every STEAM project or a balance at a curriculum level that includes intentionally imbalanced STEAM projects in different situations. In this sense, we ignore if this focus in science instruction exists when using alternative methodological approaches.

Schools 4 and 5 show similar profiles, especially focusing on meta-disciplinary criteria and, therefore, in the left sections of the map ( Figure 2), inform about their potential to develop 21st century skills. However, these schools show a different organization to convey the S&T curriculum. While School 5 conveys the S&T curriculum through PBL completely, School 4 does not. Therefore, without enough projects that bring the required sophistication to S&T contents, practices in these schools may challenge the development of scientific competence. On the other hand, School 4 may have some room for developing this competence conveyed through other methodological choices.

7. Conclusions

Based on the preceding discussion, several conclusions emerge regarding the current design of real STEAM PBL initiatives. Firstly, it is apparent that a standardized understanding of STEAM project design lacks a consensus within the context under study. This observation stems from the wide-ranging sophistication levels found in the analysed STEAM projects and echoes the need seen previously to categorize PBL approaches.

Secondly, the variations among STEAM projects predominantly centre around differing approaches to Science and Technology education rather than distinct methods of crafting projects through the lens of PBL. This finding gains support from the differentiation between disciplinary and meta-disciplinary aspects.

Thirdly, the distinction drawn between disciplinary and meta-disciplinary aspects offers valuable insights into how schools navigate the inherent complexities of STEAM PBL. This differentiation provides a framework for more focused changes in STEAM projects. Utilising both disciplinary and meta-disciplinary criteria holds potential implications for high school centres, which vary according to the extent to which the Science and Technology curriculum is delivered through STEAM PBL methodologies.

In this paper, we have seen that despite selecting a sample of secondary schools that are networked and share their thoughts and STEAM PBL instructional designs, there are still very particular trends on how to design STEAM projects that emerge when evaluating disciplinary and meta-disciplinary criteria.

This paper also raises concerns about how STEAM is pushing for the intended purposes of creating a call for more and more diverse people into this field. By looking at the findings of how STEAM is represented in Catalonia (Spain), we believe that more focus should be placed on fostering students' motivations to STEAM through leveraging the identified joyful [ 1] and socially relevant aspects of STEAM PBL initiatives and that this programme considers a more representative engagement into scientific contents and practices [ 50]. We aim for this paper to have implications regarding how schools can clearly present their STEAM projects by raising a higher awareness in relation to their motives and how they are aligned at STEAM's project level, at a subject level and at school level.

8. Limitations

While the strength of this study relies on the interpretation of how STEAM projects are associated with a progression level (category) and that descriptive statistics allowed for a graphical representation of this interpretation, these results cannot compel statistical differences significantly. Furthermore, the rubric instrument was developed in the frame of a science education perspective, which may lack a more holistic perspective that enables it to include more specific aspects of the integration of Mathematics and Arts.

Author Contributions

All the authors have equally contributed in the conception and design of this work: Conceptualization, M.P.T., D.C.L. and C.M.B.; Methodology, M.P.T., D.C.L. and C.M.B.; Validation, M.P.T., D.C.L. and C.M.B.; Formal Analysis, M.P.T., D.C.L. and C.M.B.; Investigation, M.P.T., D.C.L. and C.M.B.; Resources, M.P.T., D.C.L. and C.M.B.; Writing-Original Draft Preparation, M.P.T., D.C.L. and C.M.B.; Writing-Review & Editing, M.P.T., D.C.L. and C.M.B.; Supervision, D.C.L. and C.M.B.; Project Administration, D.C.L. and C.M.B.; Funding Acquisition, D.C.L. and C.M.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science, Innovation and Universities (PID2022-138166NB-C22b) of the Government of Spain within the research group SGR ACELEC (2021 SGR 00647).

Institutional Review Board Statement

Ethical review and approval for this study were waived by the Ethics Committee on Animal and Human Experimentation of the Autonomous University of Barcelona due to the absence of involvement of humans or animals in the study.

Informed Consent Statement

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available to preserve the anonymity of the participant secondary school centres.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Description of the school and teaching organization of PBL for educational institutions.

C1

10 STEAM projects gathered
10 h per week for projects that fully integrate the curriculum of Natural Sciences, Social Sciences, Technology, Visual and Plastic Education and Music. Implementation is developed by class teacher tutors and 2 other support teachers who rotate for the development of the different sections of the project.

C2

13 STEAM projects gathered
Two ways of implementing PBL: projects within each subject which can integer other subjects (implemented by the teacher of every specific subject in the time slots scheduled for that subject) and a global project per term. The globalized projects start to be developed within the specific subjects. In the last two weeks of the term, teaching is stopped to devote the full time to the project. The teacher who implements it changes every hour according to the schedule prior to the project. These projects involve the inclusion of subjects such as Technology and Natural Sciences with Languages and ICT.

C3

6 STEAM projects gathered
Schedule divided by curricular areas (linguistic, social and scientific and technological). For each term, two projects are implemented in each area and one global. The time structure is maintained during the course. The projects have a duration of two weeks interspersed with periods of two weeks of teaching with other methodologies. Project design is done in teacher meetings.

C4

9 STEAM projects gathered
10 h each week are dedicated to field projects (scientific-technical-mathematical, linguistic and artistic-social) in a 2-h slot each day. The projects involve a third of the hours of each subject and are implemented with two teachers in the classroom. Project design is done in scope meetings.
Open projects (without a prior design) are also done at the end of the course.

C5

12 STEAM projects gathered
10 h to carry out globalized and international projects. The projects fully include the subjects of Technology, Natural Sciences, Social Sciences, Visual and Plastic Education and Music. They are created by the cloister as a whole.

Appendix B

Table A2. The configuration of STEM PBL rubric serves as a tool for the assessment and categorization of instructional designs within the STE(A)M PBL framework. Within this structure, 21 criteria were organized into 6 dimensions. The maximum sophistication level for every criterion is presented.

Structure of the [RUBRIC NAME]

Project Core Facets

Facet Definition

Rubric Criteria

Maximum Level of Sophistication (Level 4)

Project Objectives

A collection of aims that drive the project's fulfillment, encompassing curricular objectives, instructional goals, and the overarching purpose linked to the project challenge.

Curricular goals

The intention is for students to become scientifically competent by engaging in scientific practices that allow them to construct and master various scientific models/key ideas. This enables them to make reasoned decisions and act in a wide range of situations, mobilizing cross-cutting skills such as teamwork, creativity, communication skills, and critical thinking.

Didactic goals

The aim is to follow a learning cycle centered on the construction and application of content appearing in progressively sequenced activities, moving towards more abstract levels of thinking that are ultimately used in new specific situations.

Project goals (Challenge/Driving question)

A provocative challenge/question is posed, appropriate in difficulty and long-term scope, where analyzing and understanding a situation to make decisions is necessary. It's complex, involving different factors and constraints where taking action represents a sustained challenge throughout the project.

Contents

An array of content elements (theoretical, procedural, and attitudinal), along with chosen values, and strategies for structuring and incorporating them within the project in conjunction with content from other subjects.

Deepening on the conceptual contents

Ideas that recur regularly are selected and organized into key concepts, progressively developed across different stages to construct a theoretical model capable of explaining a wide range of phenomena.

Deepening on the procedural and epistemic contents

Complex procedural ideas reappear regularly, such as classifying, designing experiments, selecting appropriate tools and strategies for observation, data collection, and interpretation, as well as determining suitable criteria for result validation, etc.

Deepening of values and attitudinal contents

Encouragements are present to foster scientific attitudes (rigor, objectivity, recognizing limitations, assessing the certainty of generated assertions, etc.).

Integration of content between subjects

There's repeated promotion of cultivating attitudes towards science (e.g., valuing the role of science in decision-making or its implications in today's society) through specific activities that prompt reflection on values associated with the content being studied.

Action

The outcome, choice, or course of action derived from addressing the question or challenge presented within a project.

Deployment of the action

The project promotes the explicit outlining of a proposal, arguing for it, designing it, putting it into practice, evaluating its outcomes, and suggesting improvements.

Scope of social impact

The action is directed towards a social or professional community external to the school and its environment (usually associated with an external commission) and generates a sustained impact or repercussion within this community.

Action openness

The project is completely open and starts from a problematic context where students identify and choose the challenges they want to address. The ways to approach the challenge are decided upon and justified by the students themselves.

Science and engineering practices

A collection of cognitive, practical, and communicative approaches related to school science and technology, which are fostered within the project.

Participation in the evaluation of evidence and construction of arguments (Argumentation)

The focus lies in developing scientific argumentation. It's a practice that appears recurrently throughout the project and evolves in sophistication as the project progresses. Argumentation becomes a tool for dialogue between the phenomenon being investigated and the model being constructed, as well as for contrasting models among the students. Decisions made during the project are also argued.

Participation in the collection and analysis of data from observations or experiments (Inquiry)

Scientifically oriented questions are posed, encouraging the planning of investigations to observe and collect evidence that either confirms or refutes initial ideas. Drawing conclusions and developing explanations based on the acquired scientific knowledge are requested, and these explanations are evaluated. Experimental or fieldwork predominates throughout the project.

Participation in the construction of explanations, theories and models (Modeling)

Understanding a theoretical model that is sequentially built by introducing ideas contrasted with previous models is promoted. Questions arise that encourage imagining the mechanism explaining a phenomenon and revising this model. Part of the challenge of the project involves engaging in this process of developing and using this model.

Participation in engineering practices

Involvement in practices such as empathizing with an external community, defining a problem, devising a solution, prototyping, and testing it is proposed. This engagement aims to mobilize content and construct a "product" that addresses identified needs.

Contextualization

The thread used to articulate the overarching theme that imparts significance to the project's challenge or question, in addition to the content and practices being developed.

Relevance

The situations and challenges proposed aim to connect and generate sustained interests among students at an individual level (considering skills for their daily lives), a social level (preparing them for interaction in society), and/or a professional level (providing guidance). These situations aim to generate new interests and concerns that go beyond the everyday scope.

Scientific significance

A context is employed that allows for scientifically investigable questions. Phenomena are reinterpreted by incorporating new perspectives (from different disciplines). The context gives meaning to new concepts associated with a new language, offering an insight into what scientific activity entails.

Authenticity

The situations and tasks presented are either identical or very similar to those encountered in the real world "outside the school". Ambiguous situations are worked upon, involving undefined problems tackled through group work among peers and/or with individuals outside the educational institution.

Assessment, ICT and
Collaboration

Strategies for regulation that serve as guidance for both learning and action.

Formative assessment

The project objectives are discussed with the students as they are represented and how to plan the realization of some key (transferable) tasks and criteria for assessing their quality. Time is allocated for applying co-evaluation and self-assessment as a means of regulating the difficulties that arise.

Final assessment

From the competency objectives of the project, criteria or rubrics are agreed upon, and students are encouraged to find evidence in their work that allows them to deduce at what level they have achieved these objectives. Multiple perspectives are triangulated, and concerning the final product, critical reflection on potential improvements is particularly valued. Evaluation considers both specific and cross-cutting competencies in the curriculum.

Mechanisms for regulation and the utilization of information and communication technology (ICT).

Use of ICT resources

ICT tools and resources are recurrently incorporated with a clear didactic focus, aiding in thinking and facilitating the organization, construction, and communication of ideas. Reflection on alternative uses outside the context in which they are employed is encouraged. Animations and simulations might be used as well.

Mechanisms for regulating group work.

Regulation of cooperative work

Various strategies for regulating group work, such as rubrics, team commitments, progress journals, etc., evaluate the involvement and participation in the project. Students are assigned roles within the group. Group work is important in structuring new ideas.

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Figure 3. Representation of 46 STEAM projects disciplinary scores (x axis) and meta-disciplinary scores (y axis). Each school is represented using a different shape.

Deepening on the conceptual contents

Eventually, descriptive contents in the form of information or data are incorporated. They are presented disconnected from each other.

Contents are selected that allow the description and identification of specific phenomena that are easily interpretable. It mainly involves simple cause-and-effect relationships.

Key ideas are selected and organized that appear recurrently throughout the project and are specifically developed at certain moments of the project.

Key ideas are selected and organized that appear recurrently and are progressively developed over different moments to build a theoretical model that allows explaining a wide range of phenomena.

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Evaluation of STEAM Project-Based Learning (STEAM PBL) Instructional Designs from the STEM Practices Perspective

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Currently, there is a wide diversity of project-based learning instructional designs presented as "STEAM projects". However, it is essential to evaluate if all these STEAM projects align their learning objectives and activities with the intended STEAM competences.

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คำที่ใช้ในการค้น “Designing STEAM Activities in Primary Education”

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STEAM in Early Childhood: An Analysis Towards Teachers’ and Children’s Perspectives - Early Childhood Education Journal

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This research examines teachers’ and children’s perspectives regarding STEAM (science, technology, engineering, art and math) in early childhood education. The participants of the study consisted of 35 teachers and 73 children selected from the children in the preschool classrooms where the teachers worked. Semi-structured interview forms prepared in line with the relevant literature and expert opinions were used as data collection tools in the research. The data obtained was analyzed using the content analysis technique. The themes obtained from the analysis of teachers’ perspectives and teachers’ knowledge about STEAM, materials used, activity types, activity resources, teacher competence, and solutions. The themes regarding the perspectives of children participants were determined as the topics children are curious about and the activities they enjoy about STEAM. Results showed that teachers did not have sufficient knowledge about STEAM, gave little place to STEAM activities in their classrooms, and had problems finding resources while preparing these activities and in the activity planning process. They also stated that teachers felt inadequate in implementing STEAM activities. According to the other results obtained from the research, the subjects that the children were most curious about in STEAM were science and engineering-related subjects, and the activities they enjoyed participating in the most were experiments, art, and games. The results were discussed per the relevant literature, and suggestions for STEAM applications in early childhood education were presented.

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so02.tci-thaijo.org

2.พิมพ์ ออกแบบกิจกรรม STEAM เอาไปแปลเป็นภาษาอังกฤษ DESIGN STEAM ACTIVITIES แล้วค้นหา

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ผลของกิจกรรมการ์ดวงจรไฟฟ้าตามแนวคิดสะตีมศึกษาที่มีต่อความเข้าใจมโนมติทางวิทยาศาสตร์ ความคิดสร้างสรรค์ และเจตคติทางวิทยาศาสตร์ของนักเรียนประถมศึกษา | วารสารวิทยาศาสตร์และวิทยาศาสตร์ศึกษา (JSSE)

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การวิจัยนี้มีวัตถุประสงค์เพื่อศึกษาผลของกิจกรรมการ์ดวงจรไฟฟ้าแบบบูรณาการ STEAM ที่มีต่อความเข้าใจมโนมติทางวิทยาศาสตร์เรื่องวงจรไฟฟ้า ความคิดสร้างสรรค์ และเจตคติทางวิทยาศาสตร์ของนักเรียนประถมศึกษา กลุ่มตัวอย่างคือ นักเรียนชั้นประถมศึกษาปีที่ 6 โรงเรียนบ้านแก่งยาง จังหวัดอุบลราชธานี จำนวน 16 คน ใช้รูปแบบการวิจัยกึ่งทดลองแบบกลุ่มเดียววัดผลก่อนและหลัง เครื่องมือวิจัยประกอบด้วยแบบทดสอบ DIRECT 1.0 แบบวัดเจตคติทางวิทยาศาสตร์ที่ดัดแปลงจาก TOSRA และรูบริกประเมินความคิดสร้างสรรค์ ผลการวิจัยพบว่า ความเข้าใจมโนมติทางวิทยาศาสตร์เพิ่มขึ้นอย่างมีนัยสำคัญทางสถิติ (t(15) = 10.77, p<.001) โดยมีค่าความก้าวหน้า (normalized gain) เท่ากับ 0.32 ซึ่งอยู่ในระดับปานกลาง ความคิดสร้างสรรค์ของนักเรียนอยู่ในระดับสูง (M = 4.52, SD = 0.39) โดยเฉพาะด้านความสวยงามของการออกแบบและความถูกต้องของวงจรไฟฟ้า ขณะที่เจตคติทางวิทยาศาสตร์อยู่ในระดับมาก (M = 4.39, SD = 0.82) โดยมีความโดดเด่นด้านความอยากรู้อยากเห็นและการมีส่วนร่วม ผลลัพธ์ชี้ให้เห็นว่ากิจกรรมการ์ดวงจรไฟฟ้าแบบบูรณาการ STEAM สามารถเสริมสร้างความเข้าใจ ความสนใจ และความคิดสร้างสรรค์ของนักเรียนได้อย่างมีประสิทธิภาพบนพื้นฐานแนวคิด Maker-based Learning และการเรียนรู้เชิงสร้างสรรค์

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มธุรดา001 ในไทย

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มธุรดา001 ในไทย

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คำที่ใช้ในการสืบค้น “การออกแบบกิจกรรม STEAM ในระดับประถมศึกษา” ตรงตัวเลยค่ะ

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ผู้แต่ง พิชญา โข่พิมาย คณะครุศาสตร์ มหาวิทยาลัยราชภัฏนครราชสีมา วาสนา กีรติจำเริญ คณะครุศาสตร์ มหาวิทยาลัยราชภัฏนครราชสีมา

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การศึกษาผลการเรียนรู้ หน่วยการเรียนรู้ สร้างสรรค์สิ่งประดิษฐ์ เรียนรู้ชีวิตมนุษย์และสัตว์ และการคิดแก้ปัญหาเชิงสร้างสรรค์ ของนักเรียน ชั้นประถมศึกษาปีที่ 3 จากการจัดการเรียนรู้ตามแนวคิดสะตีมศึกษา | ปัญญา

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บทความวิจัยนี้มีวัตถุประสงค์เพื่อ 1) ศึกษาผลการเรียนรู้ของนักเรียนชั้นประถมศึกษาปีที่ 3 ดังนี้ 1.1) เปรียบเทียบผลสัมฤทธิ์ทางการเรียน ก่อนและหลังเรียน 1.2) เปรียบเทียบผลสัมฤทธิ์ทางการเรียน หลังเรียน กับเกณฑ์ร้อยละ 70 1.3) ศึกษาคุณภาพชิ้นงานจากการจัดการเรียนรู้ตามแนวคิดสะตีมศึกษา 2) เปรียบเทียบการคิดแก้ปัญหาเชิงสร้างสรรค์ ก่อนและหลังเรียน กลุ่มตัวอย่าง คือ นักเรียนชั้นประถมศึกษาปีที่ 3 จำนวน 11 คน โรงเรียนบ้านหนองใหญ่ โดยการสุ่มแบบกลุ่ม จำนวน 3 กลุ่ม กลุ่มละ 4, 4 และ 3 คน เครื่องมือที่ใช้ในการวิจัย ประกอบด้วย แผนการจัดการเรียนรู้ตามแนวคิดสะตีมศึกษา จำนวน 3 แผน รวม 12 ชั่วโมง มีการประเมินความสอดคล้องและเหมาะสมของแผนการจัดการเรียนรู้ มีค่าเฉลี่ย 4.87 หมายถึง มีความเหมาะสมในระดับมากที่สุด แบบทดสอบวัดผลสัมฤทธิ์ทางการเรียน มีค่าความเชื่อมั่น 0.90 แบบประเมินคุณภาพชิ้นงานความเชื่อมั่น 0.73 และแบบประเมินการคิดแก้ปัญหาเชิงสร้างสรรค์มีค่าความเชื่อมั่น 0.81 การวิจัยครั้งนี้เป็นการวิจัยเชิงทดลองเบื้องต้น โดยใช้รูปแบบการทดลองแบบกลุ่มเดียวมีการทดสอบก่อนและหลังเรียน วิเคราะห์ข้อมูลสถิติ ได้แก่ ร้อยละ ค่าเฉลี่ย ส่วนเบี่ยงเบนมาตรฐาน การทดสอบค่าที (t-test)
ผลการวิจัยพบว่า ผลการเรียนรู้ จากการจัดการเรียนรู้ตามแนวคิดสะตีมศึกษา ในประเด็นดังต่อไปนี้ ผลสัมฤทธิ์ทางการเรียน หลังเรียนสูงกว่าก่อนเรียน อย่างมีนัยสำคัญทางสถิติที่ระดับ .05
ผลสัมฤทธิ์ทางการเรียนหลังเรียนสูงกว่าเกณฑ์ร้อยละ70 อย่างมีนัยสำคัญทางสถิติที่ระดับ .05
คุณภาพชิ้นงาน คะแนนในภาพรวมชิ้นงานอยู่ในระดับดีมาก
การคิดแก้ปัญหาเชิงสร้างสรรค์ หลังเรียนสูงกว่าก่อนเรียน อย่างมีนัยสำคัญทางสถิติที่ระดับ .05

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วิชญาพร 002 (งานไทย)

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วิชญาพร 002 (งานไทย)

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วิธีการค้นหา: การออกแบบ STEAM ในระดับประถมศึกษา ในไทย

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พนิตตา วงษ์พานิช, กฤษณะ มุขแก้ว, วรินทร จันทรศารทูล

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ผลของการจัดการเรียนรู้คณิตศาสตร์แบบบูรณาการผ่านกิจกรรม “นักออกแบบอาหาร” โดยใช้กระบวนการออกแบบเชิงวิศวกรรมที่มีต่อความคิดสร้างสรรค์ของนักเรียนชั้นประถมศึกษาปีที่ 6 | วารสารวิชาการศึกษาศาสตร์ ศรีนครินทรวิโรฒ

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This research investigated the effects of integrated mathematics learning through the “Food Designer” activity based on the Engineering Design Process (EDP) on creative thinking of Grade 6 students. Thirty students from Srinakharinwirot University : Prasarnmit Demonstration School (Elementary) were purposively selected as participants. The research instruments comprised EDP based learning modules integrating mathematics, science, and real-life applications through three main activities: the “Food Designer” project, cost-profit calculations, and interdisciplinary problem-solving tasks. Data were analyzed using descriptive statistics. Results revealed that students demonstrated high levels of creative thinking across four dimensions, with mean scores ranked from highest to lowest as follows: fluency, divergent thinking, flexibility, and originality. These findings suggest that EDP integrated mathematics learning effectively enhances multiple dimensions of creative thinking in elementary students.

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search : Designing STEAM activities at primary level

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ค้นหา : การออกแบบกิจกรรม STEAM ในระดับประถมศึกษา ภาษาอังกฤษ

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ต่างชาติ

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Weaving STEAM with threads: teacher professional development with e-textile projects - Educational technology research and development

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The impact of educational e-textiles on student outcomes in STEAM education programs is a growing area of interest. Despite the potential of e-textile projects to enhance STEAM education, their effect on K-12 teachers’ professional development remains underexplored. This study investigates middle school teachers’ perceptions of their experiences with wearable e-textile-supported STEAM projects, their ability to translate those experiences into classroom practices, and their attitudes toward integrating these projects into the curriculum. Mixed-methods research with a convergent design was employed in two e-textile-supported STEAM teacher training camps, involving 20 and 19 in-service science teachers, respectively. Qualitative and quantitative data were collected using post-intervention surveys, semi-structured interviews, and a follow-up survey. The findings revealed that the program increased teachers’ confidence in STEAM project design, fostered creativity and interdisciplinary learning, and improved STEAM knowledge and skills. In addition, teachers reported being well-equipped to integrate the learned practices in their classrooms, with a positive shift in their attitudes toward integrating such interdisciplinary approaches into the curriculum. The findings also revealed high teacher satisfaction with the program and a sustained interest in STEAM practices. This research provides valuable insights for future efforts to support the adoption of e-textiles in STEAM science classrooms by highlighting their role in teacher professional development.

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ทำให้รู้ว่าเรามีข้อดีอะไร ข้อเสียอะไร เป้าหมาย และสิ่งที่อยากรู้

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ชอบงานนี้เพราะเป็นการเรียนรู้คณิตศาสตร์ผ่านการสำรวจสิ่งรอบตัวเเละการลงมือทำจริง ทำให้เข้าใจเรื่องรูปเรขาคณิตได้ง่าย สนุก และได้ฝึกทักษะการสังเกต การคิดวิเคราะห์ และการทำงานร่วมกับผู้อื่น

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มธุรดา สุวรรณลังกา 001

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มธุรดา สุวรรณลังกา 001

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ที่ชอบเพราะเป็นเรื่องเกี่ยวกับการเรียนเศษส่วน ที่สามารถปรับการสอนให้น่าสนใจมากขึ้น โดยกิจกรรมให้นักเรียนได้ระบายสีตามโจทย์จากใบงาน เช่น ในใบงานจะมีช่อง 2 ช่อง ให้นักเรียน ระบายสี 2 สีลงไป แล้วผลลัพธ์จะออกมาเป็นสีอะไร

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insKru - เศษส่วนสีผสม

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เศษส่วนสีผสม - NIIFmath

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เมื่อในคาบเรียนป.4 มีเด็กที่ชอบศิลปะและเกลียดคณิตศาสตร์ กับเด็กที่ชอบคณิตศาตร์แต่เกลียดศิลปะอยู่ด้วยกัน ถ้านำศาสตร์ทั้งสองวิชามาอยู่ด้วยกันจะเป็นยังไง!? เราเลยนำเสนอเป็นกิจกรรม “เศษส่วนสีผสม” ให้ผู้เรียนได้ใช้คณิตศาสตร์เรื่องการบวกลบเศษส่วนมาใช้แก้ปัญหาการผสมสีแล้วไม่ได้สีเดิม

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ไอเดียกิจกรรมนี้คือ : ใช้รูปหัวใจแทนการรักตัวเอง ให้ค้นหาข้อดีและคุณค่าของตนเอง เพื่อสร้างความภาคภูมิใจและความมั่นใจในตนเอง เริ่มจากการพับกระดาษเป็นรูปหัวใจ ตกแต่งให้สวยงาม แล้วเขียนข้อดี ความสามารถ และสิ่งที่ตนเองภูมิใจลงในหัวใจ

เลือกกิจกรรมนี้ : เพราะได้ทบทวนข้อดีของตนเอง เห็นคุณค่าในตัวเอง และสร้างกำลังใจในการพัฒนาตนเองไปเรื่อยๆ

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ภิญญ์ชาฎา ทะก๋า 008

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ภิญญ์ชาฎา ทะก๋า 008

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ชอบไอเดียนี้ : เพื่อช่วยส่งเสริมให้นักเรียนอ่านออกเขียนได้ ผ่านกิจกรรมที่สนุกและเหมาะสมกับวัย เพื่อพัฒนาทักษะภาษาไทยและสร้างนิสัยรักการอ่าน

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insKru - 3 กลยุทธ์ จุดประกายเด็ก ๆ ให้อ่านออกเขียนได้ (ปฐมวัย-ประถม)

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3 กลยุทธ์ จุดประกายเด็ก ๆ ให้อ่านออกเขียนได้ (ปฐมวัย-ประถม) - insKru

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3 กลยุทธ์ จุดประกายเด็ก ๆ ให้อ่านออกเขียนได้ (ปฐมวัย-ประถม)

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บอร์ดเกมผจญภัยโจรสลัดพิชิตคำเป็น-คำตาย (ภัทรวดี013)

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บอร์ดเกมผจญภัยโจรสลัดพิชิตคำเป็น-คำตาย (ภัทรวดี013)

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ที่เลือกงานชิ้นนี้ ส่วนตัวเป็นคนชอบเล่นบอร์ดเกมค่ะ บอร์ดเกมที่เลือกมาจะเกี่ยวกับคำเป็น-คำตายมีเนื้อเรื่องที่นำโจรสลัดมาดึงดูดความสนใจและเปลี่ยนเนื้อหาเรื่องคำเป็น-คำตายที่ยากให้เป็นเรื่องที่สนุกค่ะ

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วิชญาพร พูลเหลือ 002

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วิชญาพร พูลเหลือ 002

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ไอเดีย : Lucky Seat โต๊ะนี้มีโชค

ชอบไอเดียนี้ เพราะเปลี่ยนการจัดที่นั่งที่ปกติเป็นเรื่องน่าเบื่อ ให้กลายเป็นกิจกรรมที่สนุกและน่าตื่นเต้น นักเรียนได้ลุ้น ได้เปลี่ยนบรรยากาศ และมีโอกาสรู้จักเพื่อนใหม่มากขึ้น อีกทั้งยังช่วยฝึกการปรับตัว การเข้าสังคม และสร้างบรรยากาศการเรียนรู้ที่เป็นบวก ทำให้ทุกคนรู้สึกมีส่วนร่วมตั้งแต่เริ่มคาบเรียน

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Lucky Seat โต๊ะนี้มีโชค - Easy Funny Thai by Kru P’Eve

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Lucky Seat โต๊ะนี้มีโชค

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ชอบเพราะเป็นการฝึกการอ่านที่ไม่น่าเบื่อ มีการสังเกตคำในบทความนิทาน และมีการเล่นเกมเติมตัวสะกด ทำให้ผู้เรียนได้ฝึกทักษะการอ่านการสะกดคำและการสังเกตตัวสะกดที่ถูกต้อง

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สิ่งที่ชอบ : เพราะว่าเด็กๆอาจจะมีเรื่องอยู่ในใจแต่ไม่กล้าบอกใคร และเราสามารถทำให้เด็กบอกผ่านกระดาษโน๊ตได้ โดยไม่ระบุตัวตน อย่างน้อยเด็กๆก็ได้บอกสิ่งที่อยู่ในใจของเค้าค่ะ

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1. ก่อนเรียนวันนี้ ท่านมีมุมมองต่อขยะอาหารอย่างไร

2. หลังเรียนวันนี้ ท่านมีมุมมองต่อขยะอาหารเปลี่ยนไปอย่างไร

3. ท่านคิดว่าปัญหาขยะสามารถนำมาเป็นกิจกรรมการเรียนรู้ได้หรือไม่ เพราะเหตุใด

4. ท่านคิดว่าครูมีบทบาทอย่างไรในการสร้างการเรียนรู้จากปัญหาจริงในสังคม

# data[].attributes.body
หากเราต้องการเปลี่ยน Food Waste ให้เป็นกิจกรรม STEAM สำหรับเด็กประถม เราจำเป็นต้องเรียนรู้อะไรเพิ่มเติม

# data[].attributes.headline
3. จากการสืบค้นบทความวิจัย กิจกรรม STEAM มีผลต่อการเรียนรู้ของผู้เรียนด้านใดบ้าง

# data[].attributes.subject
3. จากการสืบค้นบทความวิจัย กิจกรรม STEAM มีผลต่อการเรียนรู้ของผู้เรียนด้านใดบ้าง