## 3 Conclusions
First, the study adds to the existing but scant literature that addresses the description of processes related to the extraction and transformation of educational data, with a special focus on peer assessment activities; the study also performs a comprehensive review of the software tools to support peer assessment and their functionalities. Second, from a methodological approach, the research explores the use of web services as a superior alternative to other means of LMS data collection, such as the use of SQL queries. Third, from a practical perspective, the software design detailed in this study addresses the instructors' demands for some missing functionalities of the Moodle platform (which have remained unresolved for years [64; 65]), by facilitating the extraction and analysis of data from Moodle Workshops. As a consequence of the specific scope of the research, the study is not exempt from limitations. As mentioned above, the initial validation only included a reduced and selected group of learning analytics experts; future experimentation and analysis with a larger sample of Moodle Workshop users and non-users is required. Related to this, the study does not analyze optimal configurations of the data visualizations, which should also be tested and confirmed by large-scale analysis, in a similar fashion to [66]. The reader may also find that the study does not include the analysis of actual data from peer assessment activities (even though the data used for testing MWDEX was taken from a real course, after anonymization). This decision is completely intentional for the following two reasons: first, because of manuscript length limitations; and second, and following our initial argument, literature on learning analytics has been primarily centered on the application of mining algorithms and data analysis, whereas data extraction and processing have been only tangentially addressed in most studies. Therefore, it was the authors' intention to offer an alternative view where this unbalance would be reverted. This study does provide the necessary tools to perform these analysis, and follow-up research should take advantage of the functionalities provided by MWDEX and perform the analysis of peer-assessed activities (including, but not limited to, rater reliability, natural language processing of feedback comments, or research on skill development based on rubric aspects). Finally, a very promising line of research that should be explored in further studies is how to combine the peer assessment data returned by MWDEX with other educational data (activity from LMS logs, academic data, informal learning data, among others) through interoperable and open systems to achieve the full potential of learning analytics (in line with the idea of open learning analytics [21]) and facilitating the transition toward true learning services-based technological ecosystems [67]. Conceptualization, investigation, writing--original draft preparation, J.C.-P. and E.A.-N.; formal analysis, J.C.-P. and S.I.-P.; validation, writing--review and editing, S.I.-P. and F.J.R.-S.; resources and funding acquisition, F.J.R.-S. and E.A.-N.; methodology and supervision, [PERSON]-P.; software and data curation, S.I.-P.; visualization, F.J.R.-S.; project administration, E.A.-N. All authors have read and agreed to the published version of the manuscript. This research was partly funded by Universidad Politecnica de Madrid, grant number IE1819.0904. This paper is supported by the ROBOSTEAM Erasmus+ KA201 Project with reference 2018-1-ES01-KA201-050939. The authors thank [PERSON] and [PERSON] for their support in the development of MWDEX. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.