Learning Analytics: The Tide That Lifts Both Language Institutions and Learners’ Boats

In a study published in the International Journal of Educational Technology in Higher Education, Susnjak, Ramaswami, and Mathrani (2022) discussed how data collection in educational research traditionally relied on student self-reported data. However, with the advent of large-scale online learning environments and technology-assisted autonomous learning, the limitations of this approach became apparent. Learning Analytics (LA) offers a solution by providing methods to measure, collect, analyse, and report big data about learners, thereby optimising both the learning process and the learning environment.

The study further suggests that Learning Analytics Dashboards (LADs), a key innovation in data collection, can benefit learners significantly. These dashboards offer visual representations of students’ progress, highlighting patterns in their academic and engagement levels. Learners, therefore, can independently evaluate their learning behaviour and progress, as well as compare their profiles with the usage patterns of their peers and the recommended material usage by instructors.

Likewise, another study conducted by researchers at Colorado State University in 2019 showed that analytics tools can provide students with insights that trigger a reflection process that might not occur otherwise. These reflections are believed to induce positive behavioural changes, helping learners optimise learning outcomes, complete courses, and achieve successful self-regulated learning.

Ultimately, Learning Analytics (LA) not only aims to improve learning outcomes but also enhances course design, as suggested by Persico and Pozzi (2015). For instance, in online student portfolios, data from learner dashboards can serve as indicators of achievement for educators who design the lessons. This allows them to gain dynamic, real-time insights into students’ progress, predict which students may be at risk of dropping out or failing, and identify course components that may need additional feedback or present challenges. Such reliable, real-time feedback enables educators to introduce new activities or redesign course content as needed to ensure the learning environment is as effective as possible.

Leveraging Learning Analytics for Effective Language Instruction

A special issue on language learning and learning analytics published by Computer Assisted Language Learning (CALL) has uncovered a range of themes where LA has been effectively used in both online and offline language teaching. This edition highlights how LA can help identify struggling learners and provide targeted feedback to get them back on track. It also demonstrates how online engagement can be significantly enhanced by tailoring weekly activities based on learners’ dashboards.

Based on my experience as an English language instructor, embracing big data and monitoring learners’ styles is essential for language institutions. Customising the learning process to meet the specific needs of different student groups can greatly benefit both learners and institutions. When teachers use data collected through Learning Analytics to plan lessons, it leads to improved engagement and learning outcomes. This objective data helps in understanding students’ learning behaviours, anticipating those who may struggle, and identifying challenging course components. It’s time for CALL researchers and practitioners to apply these insights effectively in our language teaching practices.