問題導向學習策略已被廣泛運用在機器人程式設計等跨領域課程，但加上常見但可能阻礙創意發展的範例引導是否更具成效，是常被關切的課題。本研究以準實驗法，探究「範例引導與問題導向混合學習」（實驗組）以及「一般問題導向學習」（控制組）兩種不同學習策略的成效差異，針對95名國民小學五年級學生，進行兩組各14節的機器人程式設計課程。課程實施前後以自編關鍵能力量表評量學生的自主學習、合作學習、問題解決、批判思考及創造創新等五種能力，並以「認知負荷量表」測量學生學習機器人程式設計的認知負荷。課程實施後另以國際運算思維測驗（Bebras test）評量學生運算思維。研究結果顯示這種混合型學習策略因為有適切的鷹架範例導入，較一般問題導向學習策略更能：提升國小學生的自主學習、合作學習、問題解決以及批判思考等能力，並在學習機器人程式設計上有較好的學習成就，同時提升學生在機器人程式設計的運算思維、降低學生的認知負荷。

Problem-Based Learning (PBL) strategy has been widely applied to interdisciplinary curricula, including robotics programming. However, whether incorporating the guidance of examples into this strategy can increase learning effectiveness is frequently discussed. This study explores the effectiveness of two different but commonly used learning strategies, that is, "PBL combined with example guidance" and "general PBL", through a quasi-experimental method. A 14-period robotics programming course was implemented for 95 elementary school fifth-graders. A self-designed key competency scale was used to assess the students' competencies before and after the course, including autonomous learning, cooperative learning, problem solving, critical thinking, and creativity and innovation. Following the assessment, the Bebras test was administered to evaluate the computational thinking of the students, and a cognitive load assessment was implemented to evaluate the cognitive load of students when learning robotics programming through the two different learning strategies. Consequently, the example-guided and problem-based combined learning strategy can enhance elementary school students' autonomous learning, cooperative learning, problem solving, and critical thinking skills, achieve better learning achievement effectiveness in robotics programming because of the incorporation of appropriate scaffolding examples. In addition, the mixed learning strategy can also improve the students' computational thinking in robotics programming and reduce their cognitive load.

STEM跨領域教育 ； 問題導向學習 ； 程式設計教學策略 ； 範例引導學習 ； 機器人教育

Interdisciplinary STEM Education ； Problem-Based Learning ； Programming Learning Strategy ； Example-Guided Learning ； Robotic Education