Data mining in course management systems: Moodle case study and tutorial

By Cristóbal Romero, Sebastián Ventura, Enrique Alfonso Carmona García. Comput. Educ., 2008

1,180 citations recorded by the source corpus.

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/b1f3f74ec504fe53477b215c76335502f21a384b

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Citations in the corpus

Web usage mining for predicting final marks of students that use Moodle courses (2013, methodology): “• Assignments are a tool for collecting student work [79].”
Learning analytics: drivers, developments and challenges (2012, background): “Each day, their systems amass ever-increasing amounts of interaction data, personal data, systems information and academic information (Mazza and Milani, 2004; Romero et al., 2008).”
Design and Implementation of a Learning Analytics Toolkit for Teachers (2012, background): “Most of the current data mining tools are too complex for educators to use and their features go well beyond the scope of what an educator might require " (Romero et al., 2007, p. 369).”
Social media competitive analysis and text mining: A case study in the pizza industry (2013, positive): “Text mining is an emerging technology that attempts to extract meaningful information from unstructured textual data Text mining is an extension of data mining to textual data (Ananiadou, 2008; Liu, Cao, & He, 2011; Romero & Ventura, 2010; Zafra & Ventura, 2009; Zeng et al., 2012b .”
A Systematic Review on Educational Data Mining (2017, neutral): “…affordability [78] Educational Data Mining (EDM)- Review & Survey [79], [6], [1], [11] Educational Data Mining (EDM) & Clustering [79], [47], [80], [81], [82], [46], [83], [11], [84], [85], [86], [87], [50], [56], [88], [89], [90], [26], [91], [68], [56], [92], [93], [48], [94], [95], [96],…”
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, neutral): “…learning environments (Agudo ‐ Peregrina, Iglesias ‐ Pradas, Conde ‐ González, & Hernández ‐ García, 2014; Romero, López, Luna, & Ventura, 2013; Romero, Ventura, & García, 2008; Zafra, Romero, & Ventura, 2011); and III. combinations of data sources described under i) and ii) (Alstete &…”