行動學習在醫學教育的應用趨勢：1998～2019年的文獻分析

Mobile Learning in Medical Education: Trends Analysis from 1998 to 2019

在過去幾十年中，全球廣泛討論及探討與行動學習有關的議題。然而，行動學習在醫學教育中的應用發展趨勢仍缺乏系統分析。本研究針對1998∼2019年發表在國際期刊的行動學習於醫學教育的研究論文進行系統回顧，分析其應用領域、科目、採用的學習策略及探討的議題。從回顧研究結果發現，行動科技的進步使得在醫學教育中行動學習研究的數量不斷增加，研究議題也愈來愈多樣性；同時，行動學習主要應用於培養基礎醫學知識與技能的學習，以及輔助醫療決策。此外，本研究亦發現，醫學教育中很少廣泛採用行動學習策略，如探索式學習、情境行動學習、電腦心智圖、遊戲學習和同步分享。此亦反映大多數研究集中在技能培訓和基礎知識理解上，而在培養學習者的高層次思考能力的領域卻很少；另一方面，發現近年來使用問卷調查的研究數量有所增加，大多數研究報告以問卷調查分析學習者的認知表現和觀念，而學習行為很少被分析。因此，本研究提出的行動學習在醫學教育現階段的研究趨勢和潛在研究問題發現，可作為研究人員、醫學教育教師和研究者結合臨床實務培訓的參考。

In the past decade, mobile learning (ML) has been a widely discussed issue in the globe. However, the ML there is a lack of systematic review on the trend of mobile learning in medical education (ME). In this paper, a systematic review was conducted to analyze the ML in ME articles published in international journals from 1998 to 2019 from the aspects of application domains, subjects, adopted learning strategies and research foci. The review results reveal that the advancement of mobile technology has led to the increment of ML in ME studies; moreover, the research foci of the studies have become more and more diverse. It is also found that ML is mainly used for basic medical knowledge and skill training as well as supporting medical decision making. In addition, several ML strategies, such as inquiry-based learning, situated mobile learning, computers as Mindtools, game-based learning, and synchronized knowledge sharing, are rarely adopted in ME. This implies that most research focused on skills training and basic knowledge delivering, while learners’ high-order thinking skills were not sufficiently taken into account. On the other hand, it is found that the number of studies using questionnaires for issue investigating is increased in recent years. Furthermore, most studies reported learners’ cognitive performances and learning perceptions, while their learning behaviors were rarely investigated. Accordingly, this study proposes the research trends and potential research issues of ML in ME, which can be a reference of researchers, instructors, and policy makers of ME.