Learning analytics and educational data mining: towards communication and collaboration

By George Siemens, Ryan Shaun Joazeiro de Baker. International Conference on Learning Analytics and Knowledge, 2012

1,124 citations recorded by the source corpus.

Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/83225ccd559500a1afbe71e68114416067f50e61

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Citations in the corpus

Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory (2015, background): “Unfortunately, traditional prediction models (e.g. support vector machines, neural networks) require a sophisticated understanding of computation that most teachers do not possess (Romero & Ventura, 2010; Siemens & Baker, 2012).”
A Systematic Review on Educational Data Mining (2017, background): “…student behaviors while interacting with the system Classroom decoration [65], [66], [49] Collaborative learning [42], [67], [43], [60], [68] Data clustering – Review & Survey [69], [70], [71], [72], [73], [74], [75], [76], [77] Education affordability [78] Educational Data Mining…”
Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief (2012, background): “Educational data mining generally emphasizes reducing learning into small components that can be analyzed and then influenced by software that adapts to the student (Siemens and Baker 2012).”
Educational data mining and learning analytics: An updated survey (2020, background): “The differences between EDM and LA are deal in other highly cited review (Siemens and Baker, 2012).”
Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, neutral): “The analysis of LMS data is often referred to as learning analytics [4], defined as “the measurement, collection, analysis and reporting of data about learners and their context, for purposes of understanding and optimizing learning and the environments in which it occurs” [5].”
Seven HCI Grand Challenges (2019, background): “…and in particular the availability of very large data sets from students ’ interactions with educational software and online learning, constitutes the basis for the evolution of two research fields, namely learning analytics and educational data mining (Konomi et al., 2018; Siemens & Baker, 2012).”
E-learning, M-learning and D-learning: Conceptual definition and comparative analysis (2018, neutral): “, 2013) Realistic situation (Laouris and Eteokleous, 2005; Sharma and Kitchens, 2004) Learning analytics (Siemens and Baker, 2012; Slade, 2013)”
A review of clustering techniques and developments (2017, methodology): “Sequence analysis [140], human genetic clustering [141], social network analysis [142], search result grouping [143], software evolution [144, 145], recommender systems [146], educational data mining [147-149], Climatology [150], Field Robotics [151] etc.”
The AI Revolution in Education: Will AI Replace or Assist Teachers in Higher Education? (2023, positive): “Siemens and Baker (2012) found that a combination of human and machine intelligence resulted in more effective learning outcomes than working alone.”
Data mining in education (2013, methodology): “…and EDM can share many attributes and have some similar goals and interests, the next key differences can be distinguished between both communities 9 : • Techniques : In LA, the most used techniques are statistics, visualization, SNA, sentiment analysis, inﬂuence analytics, discourse analysis,…”
Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative (2014, background): “Siemens and Baker (2012) chronicle the evolution of these research communities, their similarities and differences, and call for more collaboration and integration, given the overlaps in research interests, methodological approaches, and technologies between both communities.”
Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014, background): “Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.”