According to literature, learning is most meaningful when it is deliberately applied in real-life contexts by incorporating real-life contexts that facilitate active, constructive, intentional, and collaborative engagement into learning. This will help learners to achieve longer retention of what they have learned compared to memorising and rote learning. In Nanyang Polytechnic, Singapore, a Learning Experience Design (LXD) Methodology was developed to guide and support educators in the design of good learning experiences that are meaningful to learners and to achieve our goal of engaging and effective teaching and learning. This paper explains how we developed the meaningful lessons for an engineering module, focusing on application, problem-solving and collaboration. To examine whether the learners perceived the learning to be more meaningful after attending the lessons that are redesigned using the LXD methodology, a comparison study was conducted involving about 90 learners in the Diploma of Robotics & Mechatronics. The results indicated that learners in the experimental group perceived more meaningful learning and scored higher in the post-course test than the control group. In addition, the reflection on our experiences in going through the four recommended processes in the LXD Methodology, namely learner discovery, supporting learners in attaining learning outcomes, designing the learning experience, and evaluating for improvement will also be discussed. These reflections can be used as a case study to share with other educators who would like to design meaningful lesson to achieve a more engaging experience for learners. The last part of the paper highlights the challenges faced and provides improvement to further fine-tune and streamline the on-going implementation effort.
DEVELOPMENT OF MEANINGFUL LESSON USING LXD METHODOLOGY FOR AN ENGINEERING MODULE
Reference Text
Proceedings of the 19th International CDIO Conference, NTNU, Trondheim, Norway, June 26-29 2023 Year
2023 Authors
Affiliations
Pages
273-282 Abstract
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Document
CDIO 2023 Proceedings (58).pdf
(288.58 KB)