Learning Analytics is an important area in education, with limited evidence that it improves student outcomes and supports learning and teaching needs (Viberg et. al, 2018). In a creative school, the use of data to understand students’ needs is even less straightforward because many of our learning goals are more subjective and not as easily quantified. We pioneered an approach of blending the use of data with differentiated instructional (DI) strategies in a creative education setting. First, data is collected on students’ abilities or needs through a quiz, survey or diagnostic task on the school’s learning management system. When students come to class, the lecturer employs a DI strategy like "tiering" (Tomlinson, 2017) to better support their learning needs or abilities. Finally, a post-class survey is conducted to check on students’ perceptions, which allows the lecturer to continually monitor and adapt his/her lesson activities. Survey results (n=108) from our pilot suggest that our model of using data and differentiated activities are well-received by students from different creative disciplines, with 70% to 74% of students reporting a high level of perceived competency after the lesson activities. The school has continued to pilot different ways to assess students’ needs and more ways to differentiate lesson activities (e.g. compacting, flexible groupings). We hope to share this model and our experience with other teaching practitioners to move beyond a “one-size-fits-all” approach to teaching and learning.

Authors:
Clarice Sim, Singapore Polytechnic, Singapore
Lucas Chiam, Singapore Poytechnic, Singapore
C. James Sethmohan, Singapore Polytechnic, Singapore

About the Presenter(s)
Ms Clarice Sim has been at Singapore Polytechnic, School of Media, Arts, and Design for 9 years. A key initiative she is spearheading now is about using data to understand students and customising lessons that better suit their needs.

Connect on Linkedin
https://sg.linkedin.com/in/clarice-sim-6a2114a2

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