## 8 Conclusions and Future Work  
This article has presented an automatic grading algorithm for learners' contributions in MOOC forums, and a web application that implements the algorithm, giving instructors the flexibility to adapt the algorithm to the characteristics of their MOOC and desired behavior of learners in the course forum. For example, there may be MOOCs that do not require any kind of interaction on the forums and where this algorithm does not need to be used. There may be MOOCs in which instructors decide to reward learners' contributions in the forum with some extra points (calculated with the proposed algorithm) that are added to learners' final grade. There may be MOOCs where the instructors' expressed desire is to encourage discussion and debate in the forum in order to create a learning community, and therefore, where the proposed algorithm and web application are helpful to automatically calculate learners' forum grade. The algorithm considers both the quantitative dimension and the relevance of learners' contributions and has been particularized for the case of the edX/Open edX MOOC platform. The web application runs the algorithm, allowing instructors to adjust the input parameters, and providing a visual and a numerical representation of the grade for each learner. An explorative study has also been carried out with data collected from a MOOC on programming, proposing three scenarios with different input parameters for the algorithm. The results show the existing moderate relationship between the forum grades obtained through the algorithm and the course grade obtained in the summative assessment activities of the MOOC, although forum grades did not turn out to be good predictors of the course grade. This work is not without its limitations. The first limitation is that the relevance of forum messages has been measured employing a series of direct quantifiable indicators. Some of these quantifiable indicators depend on the good judgment of other people. For example, it might happen that a meaningful comment receives few responses or upvotes because of disagreements with its author or that a not very relevant message has many followers because it was sent by a popular person. The next step would be to combine these quantifiable indicators with text mining techniques that would bring another perspective to the analysis of the relevance of each message posted in the MOOC forum. For example, the analysis of specific text in forum messages could provide important information about the knowledge acquired by learners, and even on the development of certain skills. The second limitation is that the algorithm is currently based on the message structure of the edX/Open edX platform. It would be possible to extend the algorithm to work with input data from other platforms, by analyzing their forum message structure and studying the possible generalization of the algorithm. The third limitation is the nature of the exploratory analysis conducted. So far, the analysis compares forum grades with course grades based on summative assessment activities with exercises, but these two types of activities (forums and exercises) can be different and can be evaluating different skills as they were not designed to evaluate the same skills. The fourth limitation is the MOOC used. It would be possible to extend the analysis to other MOOCs designed with a higher social component with activities specifically designed to discuss in the course forum. As future work, the evaluation could be extended. First, it would be possible to include instructors in the analysis so that these could validate the forum grade given to each learner by the algorithm. In addition, specific assessments based on exercises and others based on forums could be designed at specific moments of the MOOC to evaluate the knowledge of learners on the same topics (through both forums and exercises) and compare the results obtained about these topics. Conceptualization, S.G.-M. and C.A.-H.; methodology S.G.-M., C.A.-H., P.M.M.-M., P.J.M.-M., and I.E.-A.