Can AI prediction dashboards help online groups?
Combining AI performance prediction with learning analytics reshaped analysis of online graduate groups' collaboration in an engineering course.
Source
Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course
Study at a glance
- Design
- Other — AI performance prediction and learning analytics applied to online graduate group work
- N
- N=62 · 62 graduate students in 15 groups over an 8-week course
- Population
- Online graduate engineering students in collaborative writing groups
- Outcome
- Collaboration patterns and reflections under AI-supported performance prediction
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Can AI prediction dashboards help online groups?
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62 graduate students (43 master, 19 doctoral)
Participants were 62 graduate students (43 master students and 19 doctoral students), majoring in ocean engineering at Ocean College from the university.
15 groups of four to five students
The students were assigned into 15 groups (four to five students per group), mixed with master and doctoral students in each group.
What they did
In an eight-week online graduate course, sixty-two students worked in fifteen groups on weekly collaborative writing. Researchers gathered discussion logs, applied AI-supported performance prediction and analytics, and analysed social-network measures plus students' end-of-course reflections.
What they found
Most social-network metrics differed significantly across analytic comparisons, including density. The integrated approach is framed as a way to identify at-risk students and support instructional decision-making during online collaborative work.
The limits
What it doesn't show
Findings come from one graduate engineering course rather than a multi-institution randomised trial. Predictive analytics are decision aids, not guarantees of later achievement for every learner.
Key terms
- AI performance prediction
- Models that estimate learners' likely performance to flag risk and guide support.
- Learning analytics
- Collecting and analysing learner data to understand and improve learning processes.
- Social network analysis (SNA)
- Methods that quantify relationships and interaction structure within groups.
- Collaborative writing
- Joint text production where groups plan, draft, and revise together.
- At-risk students
- Learners identified as likely to struggle without timely instructional support.
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How many graduate students participated?
Common questions
How many students participated?
62 graduate students (43 master's and 19 doctoral).
How were they organised?
Into 15 mixed groups of four to five.
How long was the course?
Eight weeks online in summer 2022.
What process data were analysed?
Among other sources, 105 group discussion files across seven weeks.
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