This work proposes a novel approach - personalized forecasting - to take into account the sequential effect in predicting student performance (PSP). Instead of using all historical data as other methods in PSP, the proposed methods only use the information of the individual students for forecasting his/her own performance. Moreover, these methods also encode the "student effect" (e.g. how good/clever a student is, in performing the tasks) and "task effect" (e.g. how difficult/easy the task is) into the models. Experimental results show that the proposed methods perform nicely and much faster than the other state-of-the-art methods in PSP.