One of the difficulties experienced by online learners is the lack of regular supervision as well as the need to provide instructions to support the learning process more effectively. The analysis of the learning data in the online courses is not only becoming increasingly important in forecasting learning outcomes but also providing effective instructional strategies for learners to help them get the best results. In this paper, we propose a forecast learning outcomes model based on learners’ interaction with online learning systems by providing learning analytics dashboard for both learners and teachers to monitor and orient online learners. This approach is mainly based on some machine learning and data mining techniques. This research aims to answer two research questions: (1) is it possible to accurately predict learners’ learning outcomes based on their interactive activities? (2) How to monitor and guide learners in an effective online learning environment? To answer these two questions, our model has been developed and tested by learners participating in the moodle lms system. The results show that 75% of students have outcomes close to the predicted results with an accuracy of over 50%. These positive results, though done on a small scale, can also be considered as suggestions for studies of using learning analytics in predicting learning outcomes of learners through learning activities. 2018 Association for computing machinery.