Learning analytics is a tool for gathering, analysing, and interpreting data related to student’s learning preference, engagement and performance, for the purposes of understanding and optimising learning and the environments. The outcomes could be utilised to predict student retention, understand their learning behaviour, and improve their learning via providing personalised feedback and support. The development involves data collection, storage, data cleaning, integration, analysis, representation and visualisation, and action. A multi-disciplinary team effort is required to successfully complete this process. The state of development of LA varies in different countries depending on the cultural context, educational technology, ethical factors, and intention of strategic drivers. Globally, there is an increase in awareness of the potential of LA to support self-regulated learning and personalized learning in recent years, and this is reflected in the intensive interest around this topic in academic conferences and education technology exhibitions. In this workshop, the theoretical frameworks that guide the design of a learning analytics system are introduced. During the interactive session, the participants will identify the stakeholders needs and types of data that are relevant to the development of learning analytics system. Issues related to data quality and ethics will be discussed, and the participants will be guided to propose feasible solutions using a systematic approach. The workshop objectives are to identify data that provide insights about students’ learning behaviour, progress and performance and discuss the impact of data integrity and ethical issues in implementation of learning analytics.

Authors:
Hui Meng Er, IMU University, Malaysia

About the Presenter(s)
Professor Dr Hui-Meng Er is the Pro Vice-Chancellor (Education) at the IMU University, Malaysia.

See this presentation on the full schedule – Thursday Schedule

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