If only there was a way for us to know how our students are doing at any given moment…Who is paying attention, and who is falling asleep? Who understands the lesson and who has no clue what is going on? And wouldn’t it be great if we knew who is motivated and who is ready to drop out of the course? Professor Hayo Reinders discusses how learning analytics (LA) can be used to identify potential problems early on.
Learning analytics (LA) involves using often large amounts of data for monitoring learner participation, engagement and comprehension. It can be used as a way to identify potential problems early on in a course and even make predictions about future cohorts.
LA aims ‘to build better pedagogies, empower active learning, target at risk student populations, and assess factors affecting completion and student success’ (NMC Horizon Project, 2016).
Learning analytics in language learning
Learning analytics can be applied to any educational domain. In language education it can be used to monitor general indicators, such as attendance and performance on tests. It can also be used to monitor specific language related issues, such as whether students achieve a certain number of target vocabulary items in a certain period of time, or whether particular groups of students struggle with certain grammatical features.
LA can help to answer everyday pedagogical questions that teachers of almost any level of technical skill can apply. It can also help to answer broader questions, for example about education systems as a whole. It can be used by educational policy analysts, ministries of education, and so forth.
Although large-scale studies with sometimes enormous data sets (think for example of national test scores) are out of reach for most teachers, most of us already have access to a lot of information about our students that we can ‘mine’ and analyse.
Think of your school’s Learning Management System (LMS; such as Moodle). It records attendance, completion of activities, grades and a lot more. Such data can be used to look at patterns. For example, noticing that students who do not complete certain activities tend to do worse than those who do, can help the teacher to identify ‘keystone’ tasks that they must ensure everyone engages in before moving on to another topic.
Similarly, test scores may correlate with attendance and this can help to identify at what point in a course intervention may be needed. Most LMS have easy tools for analysing data or can export data to use with Excel for simple statistical queries.
Beyond the LMS
Nowadays, available data goes well beyond LMS. A lot of teachers use classroom interaction and management programs like Socrative, ClassDojo and Seesaw. These record various types of learner engagement, by assigning points for completing activities, or by letting the teacher (or the students themselves) award points or badges for active participation.
The teacher can (either during the lesson or afterwards) identify students who communicate more or less, who more actively participate in group work, or those who do not do well on some of the exercises. This can help with early identification of possible problems.
Of course, such data does not have to be restricted to the teacher, it can be successfully shared with learners. A number of studies have shown that when learners can see what they have done and how they compare with others. This can be a great motivator. It also helps to give students a greater sense of control and responsibility for their learning.
There are also advantages for professional learning and teacher collaboration. By comparing data across classes, similar patterns and outliers can easily be identified. These can benefit the entire school and help with building communities of active teacher learning.
Although Learning analytics is an entire scientific field in and of itself, the many types of data and the practical tools available make it entirely feasible for teachers to more deeply analyse their classes. As a result, teachers get to know their learners better.
Professor Hayo Reinders, is one of our keynote speakers at our 2019 Better Learning Conference in July. He will be talking further about education data mining and learning analytics for language learning.
Read more from Professor Hayo Reinders on his three-part series of posts, on Understanding and encouraging willingness to communicate in the language classroom.