隨著全球經歷完Covid-19，許多活動轉換成遠距、線上並改變了人們的生活型態，在數位學習平台上也因此需求擴增，許多研究也指出台灣未來智慧學習產業將持續擴大，並且成人成為數位學習市場主要消費族群。然而，大眾對於購買數位平台課程之後，是否選擇到適合自身的課程並且轉化為知識，儼然成為一件課題。
本研究目的以服務設計流程劃分三個階段：第一階段-探索定義階段，以文獻探討整理出數位學習體驗、成人學習以及激勵學習的策略，並進行質性訪談12位受訪者近一步探討數位學習平台的服務缺口與需求。第二階段-設計執行階段，使用文獻探討中的ARCS激勵學習動機模式融入共創工作坊，並邀請相關利害關係人一同參與提出設計功能方案，並製作出設計原型。第三階段-成果評估階段，針對共創工作坊提出的十項新功能方案，回收有效問卷為60份，以問卷調查進行分眾測試評估，進一步探討每項功能對於不同族群的敏感度差異。

本研究發現：1. 成人學習動機主要源自工作角色轉變所需,亦即以解決具體問題為目標。2. 數位學習平台課程過多，使用者不容易辨別適合自身的課程為普遍痛點。3. ARCS 激勵學習動機模式，可應用於數位學習平台功能發想進而提高學習者平台 滿意度。4. 服務設計思維有助掌握使用者需求,共創可發掘創新方案。5. 不同背景的成人學習者對新功能需求有差異。綜上所述，本研究運用服務設計流程並將ARCS激勵動機模式融入共創工作坊，可提出符合利害關係人的新功能方案，進而提升學習動機。最後本研究利用Kano狩野模型分析結果，學生族群、低薪資族群、高職等族群對於新功能敏感度較高，並針對不同分眾族群提出優先新增的功能服務給數位學習平台業者做參考應用。

關鍵詞：數位學習平台、成人學習動機、ARCS激勵學習模式、服務設計、Kano分析

The global pandemic of Covid-19 has left an irreversible impact of an immense magnitude on the world. Interactions online have thus evolved and grown drastically, and online E-learning platforms have seen explosive increase in recent years, and will continue to be in an upward trend, as multiple researches indicate, with adult learners being the focus of the economy. How to identify the most suitable online courses for themselves and how they will help achieve their desired knowledge prove to be key to the decision-making process of these adult learners.
This research takes on these key questions and objectives through a “service design” approach, applying a three-stage process in hopes of shaping the most ideal E-learning platform model for the adult learners: Stage 1- Explore the needs Collect research findings and synthesize them into various hypothetical strategies which would improve adults’ learning experience on digital learning platforms, and examine them through qualitative interviews with 12 adult learners. Stage 2- Propose the design Taking inspiration from American psychologist John Keller's ARCS (Attention, Relevance, Confidence, and Satisfaction) Motivational Model, a co-creation process invited professionals in the E-learning industry to help develop and propose the design of an E-learning platform prototype for adults. Stage 3- Evaluate the results After this co-creation, 10 new proposals were made and believed to help drive adults’ motivation for online learning. A questionnaire survey was then conducted with 60 effective respondents to evaluate the performance and effectiveness of each proposal. These 60 respondents were further categorized by their backgrounds or status to help examine the sensitivity of various groups of adult learners to this survey.
The outcome and analysis show: 1. The main motivation and objective for adults to start new learning is “role change” in a working environment. 2. One of the most common “pain points” for online learners is there are an overly wide-range of courses to choose from, which makes it difficult for them to identify and decide what is the best form them. 3. ARCS Motivational Model is proved to be effective to assist in the design of an E-learning platform and increase users’ satisfaction. 4.The “service design”design-thinking process helps the platform developers to gain a better understanding of the real demands of learners, and to co-create new ideas and functions that appeal to their targeted groups of users. 5. Adult learners with different backgrounds or status have differentiated preferences over the newly-proposed platform concepts. In summary, this whole journey provides digital-learning platform developers with a clearer look of learners’ demands and more importantly, a comprehensive strategy to co-create an ideal concept which will improve learners’ motivation, via a service design process, led by the ARCS Motivational Model. In the very end, these new concepts to help engage E-learners are not just advised, but in a prioritized order, as results of Kano Model Analysis suggest. “Student”, “low-paid employee”, and “high-position employee” learners are three categories that stand out as the most alert and sensitive to new inventions.
Keywords: E-Learning platform, learning motivation for adults, ARCS model, service design, Kano analysis

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