Sanako is a Finnish Educational Tech company helping schools and language teachers to improve language teaching efficiency and results.
Developments in creating and analyzing large data sets are paving the way for a new kind of pedagogy based on assessing each student’s progress and shortfalls in the learning process. Several educational institutions are experimenting with the application of big data analysis to make administrative procedures more efficient. Simultaneously, reporting data about students’ performance through learning analytics creates new opportunities for developing tailored, highly personalized teaching approaches.
The traces we leave behind: a definition of learning analytics
With a leap of imagination, we can compare a student's journey over the academic year to a hiking trip. The path is not always linear; sinkholes and uphill sections may follow one another unevenly. At times the student's pace will be quicker and more confident, at others uncertain.
As the student goes on, his walking does not go unnoticed. Learning analytics tracks all the relevant traces that the learner has left behind, such as spikes or drop in performances, behaviour during different stages of the journey, etc.
Now, let’s put aside the hiking metaphor, and let’s get the basics straight. The University of Berkeley provides a straight-to-the-point definition of learning analytics, which is identified as “the measurement, collection, analysis and reporting about learners and their context, for purposes of understanding and optimizing learning and the environments in which it occurs.”
Analytics for enhanced education
In the context of educational environments, learning analytics usually collects data regarding:
- academic records;
- student’s engagement in individual courses;
- socio-economic background.
These insights are critical indicators for policies aimed at improving the overall effectiveness of the education process.
Therefore, learning analytics is a powerful instrument for school authorities to identify strengths and weaknesses in the curricula and teaching techniques adopted to develop educational offerings that consider students’ needs.
Some analysts regard this shift towards personalized education as the next big thing in educational policies. According to a report by the US Department of Education:
“Education is getting very close to a time when personalization will become commonplace in learning […] Rather than requiring all students to listen to the same lectures and complete the same homework in the same sequence and at the same pace, the instructor points students toward a rich set of resources […] Thus, students learn the required material by building and following their learning maps.”
Learning analytics also empowers educators and school managers to make data-informed decisions. Data-driven decision-making can guide school administrators in shaping curricula and allocating budget resources where they are needed the most.
Applying analytics to language education
As with many other subjects, learning analytics can improve language classes’ design by identifying what teaching approach works best with the classroom and sorting out the main shortfalls in the language acquisition process.
However, as it has been noted, learning analytics:
“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, or whether particular groups of students struggle with certain grammatical features.”
Understanding students’ behavior is crucial in language learning since language skills maturation can be impaired by several elements, including psychological factors. The latter, for instance, can hinder students in the acquisition of adequate speaking skills.
Language learning also requires the development of specific skills (oral, written, reading) that, although related, can be trained in different ways.
Therefore, having a dataset that monitors students’ progress in these three areas helps design balanced classroom activities that avoid repetitive tasks and address students’ actual shortcomings.
Using learning analytics to improve education policy is an ongoing process that will enrich the way school curricula are designed in the future.
How Sanako solutions support learning analytics?
We at Sanako have tried to anticipate future trends in language education by developing language learning software that allows the teacher to monitor each student’s learning path and develop tailored activities based on their needs.
For example, Sanako’s content-driven platform Reactored allows the learner a great deal of autonomy in their study path, allowing them to choose the pace of their learning and their preferred type of content (audio, video, text) best suited to consolidate their strengths or fill in any gaps.
The personalization of the learning path is balanced, from the teacher's point of view, by the possibility of easily monitoring and evaluating the results of the class. Similar analytics features are also included in Sanako’s virtual classroom solution, Sanako Connect, as we outlined in a previous post:
Sanako Connect, for example, provides educators with highly flexible solutions for online student-testing. Educators can easily upload a wide variety of test material for students - any combination of PDFs, presentations, sound files, videos, and web pages can, for example, be easily attached to the test specifications. In response, students are able to complete multiple-choice quizzes / single answer tests and upload a piece of their own verbal or written work.