Learning Analytics Community of Practice

What is Learning Analytics

When one reads the term ‘Learning Analytics (LA)’, it seems complicated and technologically advance. However, there are many instances where we use LA as academics without realising it. While LA initially started as the process of systematically collecting and analysing large data sets from online sources to improve learning (Brown, 2012), however the shift to blended learning (BL), virtual/enhanced learning, mobile learning environments, and game learning as newer types of educational environments has enabled collection of different forms of data Romero & Ventura, 2020).

Results of a study that was conducted in South Africa (SA), indicated that data about students’ performance is collected at different intervals via surveys and Learning Management Systems (LMS), however, the main barrier is the data silo effect (Lemmens and Henn, 2016). Valuable data is often kept in different platforms and includes educational data (collected during students’ learning endeavours), demographic data (for example: gender, age, language preferences), and data on student affectivity (e.g., motivation, emotional states) (Agudo-Peregrina, et al, 2014; Romero & Ventura, 2020). LA data types also include information on student attendance, student performance (i.e., assessment data), curriculum goals, class schedules (Wolff et al., 2013) and student admission data (e.g. accounts of prior education) (Arbaugh, 2014).

The field of LA is still growing, giving academics & professionals from different fields a chance to contribute to students’ success. LA has potential to enhance educational outcomes, improve institutional efficiency, including identification of areas for curriculum enhancement, and support student success, therefore it’s essential that we share examples of good practice as academics.

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