Embedded learning analytics (LA) offer significant promise for ESL teaching and assessment. When integrated into digital learning environments, LA tools can provide real-time insights into students’ performance, such as vocabulary development, grammar accuracy, reading comprehension, and time spent on tasks. This data allows teachers to personalize learning, track progress over time, and identify struggling learners early. In addition, it enhances formative assessment by making student performance visible and actionable.
However, the successful use of embedded analytics also presents challenges. One major concern is data overload—teachers may receive large amounts of information without clear guidance on how to use it effectively. Another issue is data privacy and ethics, especially in multilingual, multicultural settings. Moreover, many ESL instructors lack the technical training to interpret learning data or the institutional support to embed analytics meaningfully in instruction. Finally, access to the necessary digital tools and infrastructure can be uneven across institutions.

Learning Analytics in Moodle and Duolingo

Two notable examples of platforms that incorporate learning analytics into ESL education are Moodle and Duolingo for Schools.

Moodle, a popular learning management system in higher education, provides customizable reports and predictive analytics tools. Teachers can track student logins, assignment submissions, quiz scores, forum contributions, and time-on-task. These insights allow instructors to identify at-risk students and adjust instruction or offer timely support. In ESL courses, Moodle enables educators to embed custom materials, monitor engagement with language activities, and make informed decisions about language input and practice needs.

On the other hand, Duolingo for Schools is more accessible for K–12 or beginner ESL learners. It tracks learner accuracy, completion rates, time spent, and types of errors. The teacher dashboard offers a visual overview of class and individual progress, making it easy to assign reinforcement activities. The gamified environment also keeps students motivated while providing instant feedback on each response.

Both platforms illustrate how embedded analytics can support formative assessment, increase learner engagement, and personalize instruction. The choice between them depends on the educational context, learner age, institutional infrastructure, and instructional goals.

References

Dawson, S., Gašević, D., Siemens, G., & Joksimović, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. Journal of Learning Analytics, 1(1), 23–47. https://doi.org/10.18608/jla.2014.11.3

Duolingo. (n.d.). Duolingo for Schools. Retrieved from https://schools.duolingo.com/

MoodleDocs. (n.d.). Learning analytics in Moodle. Moodle. Retrieved from https://docs.moodle.org/402/en/Learning_analytics

Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961–1990. https://doi.org/10.1007/s11423-020-09788-z

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152