Modeling　and　predicting　the　performance　of　students　in　collaborative　learning　paradigms　is　an　important　task.　Most　of　the　research　presented　in　literature　regarding　collaborative　learning　focuses　on　the　discussion　forums　and　social　learning　networks.　There　are　only　a　few　works　that　investigate　how　students　interact　with　each　other　in　team　projects　and　how　such　interactions　affect　their　academic　performance.　In　order　to　bridge　this　gap,　we　choose　a　software　engineering　course　as　the　study　subject.　The　students　who　participate　in　a　software　engineering　course　are　required　to　team　up　and　complete　a　software　project　together.　In　this　work,　we　construct　an　interaction　graph　based　on　the　activities　of　students　grouped　in　various　teams.　Based　on　this　student　interaction　graph,　we　present　an　extended　graph　transformer　framework　for　collaborative　learning　(CLGT)　for　evaluating　and　predicting　the　performance　of　students.　Moreover,　the　proposed　CLGT　contains　an　interpretation　module　that　explains　the　prediction　results　and　visualizes　the　student　interaction　patterns.　The　experimental　results　confirm　that　the　proposed　CLGT　outperforms　the　baseline　models　in　terms　of　performing　predictions　based　on　the　real-world　datasets.　Moreover,　the　proposed　CLGT　differentiates　the　students　with　poor　performance　in　the　collaborative　learning　paradigm　and　gives　teachers　early　warnings,　so　that　appropriate　assistance　can　be　provided.　Copyright　©　2023,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.