###### Abstract
The assessment of learners' knowledge and skills in MOOCs is often undertaken using traditional summative assessment activities, such as questions and problems (which in many cases can be automatically graded) or peer-assessment activities. However, the forum interactions and messages posted might also be used as part of this assessment, as learners may post relevant questions and provide comprehensive answers to help their peers. This is important, as there might be courses where discussion forums contain the main evidence of learners' work, or where discussion forums could serve to complement the grade obtained by learners through summative assessment activities. In any case, it is important to keep in mind that the ultimate decision on whether to use the forums as part of the learners' grade and the weight the use of the forums will have on learners' final grade, corresponds to the MOOC instructors. This decision may cause side effects, such as more intensive use of the forum by the learners (since they will know that their final grade also depends on their interactions in the MOOC forum), which could be understood as something positive or negative depending on the course context. These types of behaviors could be seen as \"gaming the system\" [17]. Nevertheless, \"gaming the system\" is not only an issue related to assigning grades based on the use of the forum; several ways of \"gaming the system\" (or cheating) have also been found in traditional summative assessment activities in MOOCs (e.g., [18]). In this context, this work aims to present an algorithm that allows teachers to automatically and individually grade MOOC learners based on their interactions in the course forum, as well as a tool that implements the algorithm and represents the results graphically. This grading algorithm shall take into account both the quantitative dimension and the relevance of the messages posted, using direct quantifiable indicators to assess this relevance, but not text mining techniques, in order to perform fast calculations in a high-volume interaction environment as is the case in MOOCs. The grades calculated by the algorithm could be used by the instructors as part of the final grade of MOOC learners, combining them with other possible grades, such as those obtained from summative as sessment activities. This grading algorithm is validated with a concrete MOOC example on which two research questions (RQs) are posed:  
**RQ1:** What is the relationship between the grades obtained by the learners through the grading algorithm for forum interactions and the summative assessment activities of the MOOC? **RQ2:** What is the explanatory power of the grading algorithm for forum interactions through predictive models of learners' grades? This paper is structured as follows. Section 2 introduces the literature on social learning analytics and MOOC forums. Section 3 presents the automatic grading algorithm from learners' interactions in the MOOC forum. Section 4 presents the tool that implements the algorithm and provides a visual representation of the results. Section 5 presents the materials and methods. Section 6 summarizes the main results. Section 7 discusses the results obtained and answers the RQs. Conclusions and future work are set out in Section 8.