Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief

By Marie A. Bienkowski, Mingyu Feng, B. Means. 2012

612 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8d9824692c95bb92ea5a03605aefb88157d84d5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Z. Papamitsiou
2. Sandra G. Nunn
3. Mutlu Cukurova
4. John T. Avella
5. R. Luckin
6. Therese Kanai
7. Mansureh Kebritchi
8. Samantha Hautea

Citations in the corpus

• Designing educational technologies in the age of AI: A learning sciences-driven approach (2019, background): “For example, for presenting visual representations of data, developing profiles of learners based on similar characteristics and performance, and giving teachers the opportunity to continually develop and improve their practice based on evidence (Bienkowski et al, 2012).”
• Educational data mining and learning analytics: An updated survey (2020, background): “Data pre-processing is a hard and complicated task, and sometimes the data pre-processing itself takes up more than half of the total time spent solving the data mining problem (Bienkowski et al. 2012).”
• Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. (2016, methodology): “Learning analytics uses various approaches including visual data analysis techniques, social network analysis, semantic, and educational data mining to analyze the data (Bienkowski et al., 2012; Dawson & Siemens, 2014).”
• Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014, background): “Analogous attempts were presented by Ferguson (2012) and Bienkowski et al. (2012) in the state of LA in 2012 and in an issue brief respectively.”
• Youth Perspectives on Critical Data Literacies (2017, background): “1 In the field of digital learning systems, there is broad interest in new possibilities for digital data about youth, including using data for “personalizing instruction, improving student learning, improving student engagement, and providing rapid feedback to learners” [17, 2].”
• Data mining in education (2013, unknown): “10”
• Learning Analytics (2013, background): “” Given the wide range of information that may be included in such models, there is a recognized danger of potential bias and oversimplification (Bienkowski et al., 2012; Campbell et al., 2007; May, 2011).”