:async: Developing Augmented Reality Scenario-based Test items for an IoT MOOC Course based on ARCS model

Authors: Jian-Wei Tzeng, Nen-Fu Huang, Chia-An Lee, Yi-Hsien Chen, An-Chi Chuang
Institutions: Center for Teaching and Learning Development, National Tsing Hua University, Department of Computer Science, National Tsing Hua University, Department of Computer Science, National Tsing-Hua University
Country: Taiwan

Topic: Innovation through MOOCs practices
Sector: Higher Education
UNESCO Area of Focus: Sustainable OER
Session Format: Presentation


Massive Open Online Courses have become the highlight of higher education. Although MOOCs feature learning beyond time and space, there are still several challenges of evaluating students’ performance just in time. The difficulty of measuring students' learning effectiveness is more salient for skill-training courses. This study developed a set of items of AR-based test and applied them into the NTHU Introduction to IoT MOOC course. Our AR test, based on IoT sensor LinkIt 7697 and LinkIt one (by MEDIATEK), evaluates students’ performance after the video learning sessions under the condition that learners are incapable of interacting with the full function sets of the sensors. We administrated a survey of the ARCS model to measure learners’ motivation after the course using a five-point Likert scale, including Attention, Relevance, Confidence, and Satisfaction. The findings found that a total of 76 % of learners indicated that the AR-test drew their attention, and the test is relevant to the learning videos. A high percentage of learners thought they could earn the course certificate after the class (94%) and felt highly confident when knowing their scores (82%). This study provides learners with AR-based online IoT materials to solve the practical issues of MOOC courses.


Massive Open Online Course, ARCS model, Augmented Reality

1 Like