以micro:bit物聯網實作提升生命科學背景學生程式語言之學習動機

Abstract

物聯網近年來發展蓬勃，其產品已深入生活中各個層面，在水產養殖相關產業中也具有高度的需求，因此養殖系的學生不缺乏應用層面的創意，但是對於這些資訊產業以外的初學者而言，物聯網仍有極高的門檻。因應此新的需求與挑戰，全面納入資訊科學進入教育體系中。然而若直接移植電機資訊領域科系的教學進入非相關專長之科系，首要的挑戰便是訓練時數不足，其次較缺乏相關性而造成動機低落。為解決此一困境，本教學實踐計畫提出以Micro:bit物聯網套件做為生命科學背景之程式語言初學者的硬體教具，利用Blockly程式語言與物聯網套件的對於程式設計初學者友善的特性，設計與本科系專長相關之程式設計專題，來提升學習動機，將同學導入自主學習，將學到的程式能力立即投入實用。透過micro:bit物聯網實作教學後，以前後測進行比較發現受測者學習動機顯著增加，主要在工作價值、自我效能與尋求協助三個面向有所提升。另一方面透過機器學習分析教學輔助平台之活動數據可找出學習成就之預測性指標，發現相關線上教材的使用次數與時間與學習成就為正相關。本研究之發現可作為利用物聯網實作進行跨領域程式設計教學之參考。

The Internet of Things (IoT) has developed vigorously in recent years, and its commercial products have penetrated into all aspects of our life. There is also a high demand in the aquaculture-related fields. Therefore, students in the aquaculture department are not lacking in application-level creativity. For the beginners outside the electrical engineering and computer science (EECS) professional area, IoT technology still has a very high threshold. In response to this new demand and challenge, the government has set up a new curriculum structure to fully integrate computer science into the education system. However, if the teaching in the non-EECS-related department directly integrated from the EECS curriculums, the primary challenge would be the lack of training time and the lack of professional relevance. In order to solve this dilemma, this teaching practice project proposes using the Micro:bit IoT suite as a new teaching aid for beginners in programming languages. With the advantages of beginner-friendly features from the Blockly programming language and the Micro:bit IoT suite, this project will design related group topics, and the students will be introduced into self-learning process. Students would be able to apply computer programming ability sooner into practical use. In the present study, it was found that the subjects' learning motivation increased significantly in mainly in three aspects: work value; self-efficacy and seeking assistance after utilizing the micro:bit IoT teaching strategy. On the other hand, by clustering the students’ backend log from TronClass e-learning platform by machine learning, the predictive indicators of learning achievement can be found, and it is found that the number of times and using duration to the relevant online material are positively correlated with the learning achievement. The discoveries of this study can be used as a good reference for interdisciplinary computer programming courses with IoT practical.