Mining LMS data to develop an "early warning system" for educators: A proof of concept

By Leah P. Macfadyen, Shane Dawson. Comput. Educ., 2010

1,114 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Education

View Paper: https://www.semanticscholar.org/paper/66d9eafd07ea90dd1796f35ed439016ab0f5a389

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In Computer Science Applications

In Developmental and Educational Psychology

In Education

Citations in the corpus

In search for the most informative data for feedback generation: Learning analytics in a data-rich context (2015, methodology): “Recent studies in learning analytics (Agudo-Peregrina et al., 2014; Author A, 2013a; Macfadyen & Dawson, 2010; Wolff et al., 2013) seem to indicate that adding LMS user behaviour to these models can substantially improve the explained variance of academic performance.”
Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning (2014, background): “Furthermore, empirical data from almost 53,000 students showed a great difference between the four most used functionalities of a VLE – content pages, discussions, course organization and assessment – and the rest of activities (Macfadyen & Dawson, 2012).”
Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II (2019, positive): “Several Early Warning Systems (EWSs) have been developed that harness the predictive power of Learning Management System (LMS) and Student Information System (SIS) data to identify at-risk students and allow for more timely pedagogical interventions [1,4,15,16].”
Learning analytics: envisioning a research discipline and a domain of practice (2012, background): “Yet in spite of the uncertainty around analytics in education, a few considerations are important for researchers, practitioners, and vendors in order to position LA for long-term success.”
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, background): “…Management System (LMS) (e.g. Moodle, Sakai, or Desire2Learn) has attracted much attention among researchers, teachers and managers for its potential to address some of the major challenges confronting the education sector (Baer & Campbell, 2012; Macfadyen & Dawson, 2010; Siemens & Long, 2011).”
Models for early prediction of at-risk students in a course using standards-based grading (2016, methodology): “Use of an early warning system in a course, along with intervention guidelines, can increase students ’ success in a course (Arnold & Pistilli, 2012; Essa & Ayad, 2012; Macfadyen & Dawson, 2010).”
Translating Learning into Numbers: A Generic Framework for Learning Analytics (2012, methodology): “This specific example relates to conducting a social network analysis of students discussing in a forum using the SNAPP tool, based on the work by Dawson et al. (Dawson, 2008; Macfadyen & Dawson, 2010).”
Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan (2012, methodology): “Subsequent data analysis confirmed and extended our earlier reporting of significant correlation between student learning outcomes (as represented by student final grade in the relevant course) and their use of engagement tools (discussions, mail) in fully online courses (Macfadyen & Dawson, 2010).”
Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, methodology): “2 Total time online (min) [13], [28], [31], [32], [37], [39] 4,989 815 678 Number of course page views [32], [33] 4,989 208 144 Irregularity of study time [31], [42], [43] 4,989 1,926 993 Irregularity of study interval 4,989 309,000 252,000 Largest period of inactivity (min) 4,989 20,500 13,100 Time”
Educational data mining: A survey and a data mining-based analysis of recent works (2014, methodology): “ID Author of the EDM approach Discipline Model Task M: method A: algorithm T: technique E: equation F: frame 1 Macfadyen and Dawson (2010) Probability Predictive Regression M: social network, analysis (SNA) E: logistic regression, multiple regression T: logistic regression, multiple regression 2…”
Learner Engagement in Blended Learning Environments: A Conceptual Framework (2019, background): “Handelsman, Briggs, Sullivan, & Towler (2005) 4 Skills engagement: Skills practice, general learning strategies Emotional engagement: Emotional involvement with the class material Participation/ interaction engagement: Participation in class, interactions with instructors and classmates Performance ”
Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014, methodology): “VLEs / LMSs a Lin, Hsieh & Chuang, 2009; Lykourentzou et al., 2009a; Lykourentzou et al., 2009b; Macfadyen & Dawson, 2010; Merceron & Yacef, 2008; Romero et al., 2008; Romero-Zaldivar et al., 2012; Tanes et al., 2011 MOOC/social learning b Clow & Makriyiannis, 2011; Fournier et al., 2011; Kizilcec…”