Introduction to learning analytics
Learning Analytics (LA) refers to the use of student data to understand and enhance teaching and learning with a view to optimising student success ().
By providing information on what resources students have accessed, when and how they have accessed them and how long they have spent using them, a clear picture of student learning behaviour can be developed. This information can give those who teach an invaluable insight into the effectiveness of their curriculum design approach and it can also give students a much better picture of how engaged they are with the course learning pathway relative to their peers. Most valuably, this information can be provided in real time, giving both those who teach and students the opportunity to take timely, informed action if required. To learn more, read more about
Taking an informed approach to learning analytics
The use of teaching, learning and assessment learning analytics is regulated in UL by the Policy on the Use of Data to Enhance Teaching, Learning and Assessment (Learning Analytics). The purpose of this policy is to outline the University鈥檚 position on the use of data gathered from its teaching, learning and assessment systems (which includes, but not exclusively, the VLE and its associated third-party tools) to support and enable student learning, and to enhance curriculum development, teaching and service delivery. The policy states that use of university approved learning analytics must be student-centred, staff-led, ethical and transparent, and through the supported university systems for security purposes.
Learning analytics at the module level
Brightspace has several built-in statistical tools that instructors can use to provide a snapshot of where students are at any given time. The Class Progress tool displays a custom dashboard that assists in tracking class and user progress in a course by providing statistics across a variety of tools, including assignments performance, checklist completion, content completion, discussion participation, quiz performance, and survey completion. Importantly, students can also have access to their personal dashboard of engagement.
In this , you can see a quick overview of the tools at the module level that you can currently find in your Brightspace site. Also, Panopto includes statistics of use and engagement. For a deeper dive, this video provides a good overview how to get statistics and learning analytics of use in the different Brightspace tools: .
Learn more about the tools mentioned in the video in the articles in the Brightspace Knowledge Base.
Using learning analytics to improve students' experience
Quite often, these reports can be used to identify students at risk of disengaging from your module. You may search, for example, for students who have not logged in the VLE for the last few weeks or have not watched a crucial video. You might choose to approach that person directly to check if they are having any difficulty through an informal conversation. If you have a large class, you can select a small percentage of those that appear most disengaged and send a personalised and positive email and show that you care. Other times, you may choose a critical point on the semester, such continuous assessment, to follow up on your class performance and provide appropriate group feedback or modify your teaching curriculum accordingly.
You can set up an Intelligent Agent to report on specific conditions at any time in the module. For example, you could create and run an Intelligent Agent to provide a list of all students who scored less than 40% on a quiz, and you could then send additional or supplementary resources to the listed students to assist them to get back on track.
STELA Live - Learning Analytics for Student Success
In summary, the project has conducted a baseline analysis based on machine learning models with 8000 students over four academic years, and a pilot implementation of an intervention for student success with four large first year cohorts. A pilot intervention was designed and evaluated, where students were notified mid-semester of their likelihood to succeed in the module, and referred to appropriate supports. Learnings and insights are being currently shared based on the evaluation of the impact of the initiative. In doing so, we aim to contribute to building institutional data and insights on the possible application of learning analytics interventions 九色视频. Also, we set the ground to explore sustainable and contextually relevant solutions to the applications of learning analytics through Brightspace, the new VLE 九色视频.
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To learn more, please refer to best practice guidelines from the