Creating Student Groups Using Demographic, Enrolment and Educational Data
It’s well documented that students can derive great benefits from working in groups. From increased individual achievement to enhanced communication and professional development skills, students with experience of Group Learning are likely to find themselves better equipped for whatever path they choose after graduation.
At the University of Adelaide we’ve seen that those active within study groups score about 3% higher than their peers and those extra few percent equate to a higher grade in about a third of cases. With so many students now studying remotely, group work also helps to engender a sense of collegiality. Understanding the advantages that this style of learning can offer it seems prudent to promote its use wherever possible. However, it can take some time to create student groups if the intention is to design them based on a mix of characteristics such as gender, residency or ATAR.
For a busy academic this is time that could be better used in any number of ways and so the Learning Analytics team developed a means of creating the groups quickly and efficiently. Several academics now send the LA team details of how they would like their groups to be built before the start of each teaching period. A key part of this is deciding on the hierarchy of factors used to assign students to groups. Up to three factors is ideal with there being limited room to allocate students to different groups much beyond that. Among the factors used to create groups so far have been:
- Year level
- Section enrolments
- Repeat enrolments
- Previous assignment marks
The categories you want to use are entirely up to you and anything for which data can be obtained is viable. In some instances, students may have valid reasons for not wanting to be placed with certain people and this can also be accommodated without compromising the demographic make-up of the groups.
If you would like assistance to create student groups in your course please email the Learning Analytics team and let us know how you’d like to go about it.
More from the Learning Analytics team
The above example is just one of the ways that the Learning Analytics team can work with academics to provide strategies and insights regarding student learning and engagement. Some other insights Learning Analytics have provided this semester include:
- Identifying at-risk students within programs and courses
- Predicting student outcomes based on early assessment
- Assessing the impact of student pre-requisite knowledge
- Comparing student engagement during online teaching periods
More information about some of the regular insights and reports is available on the Learning Analytics page. Teaching staff can also contact the Learning Analytics team with any other questions or requests for insights.