Data mining in education

By Cristóbal Romero, Sebastián Ventura. 2013

1,314 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Social Sciences & Humanities

How researchers see this paper

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

In Artificial Intelligence

1. Tao Yu
2. Luke S. Zettlemoyer
3. Daniel Fried
4. Ruiqi Zhong
5. Scott Yih
6. Anastasios A. Economides
7. Z. Papamitsiou
8. Raheela Asif

In Computer Science Applications

1. Dirk T. Tempelaar
2. Anastasios A. Economides
3. Bas Giesbers
4. Luke S. Zettlemoyer
5. Z. Papamitsiou
6. Raheela Asif
7. Bart Rienties
8. Agathe Merceron

In Information Systems

1. Daniel Fried
2. Anastasios A. Economides
3. Hussain Ahmad Madni
4. Luke S. Zettlemoyer
5. Z. Papamitsiou
6. Zahid Anwar
7. Raheela Asif
8. Agathe Merceron

In Social Sciences & Humanities

1. Anastasios A. Economides
2. Z. Papamitsiou

Citations in the corpus

• DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation (2022, background): “Data science is important in many areas (Romero & Ventura, 2013; Bolyen et al., 2019; Faghmous & Kumar, 2014), but requires programming proficiency in specialized libraries, thus posing substantial barriers to lay users.”
• Data mining techniques and applications — A decade review (2017, background): “Relationship between variables must satisfy two things: interestingness and significance [17].”
• Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014, methodology): “Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.”
• Analyzing undergraduate students' performance using educational data mining (2017, background): “Distillation of data for human judgment accords with what others call overview statistics and visualizations (Baker, 2010).”
• In search for the most informative data for feedback generation: Learning analytics in a data-rich context (2015, background): “…at least five of these objectives of applying learning analytics(as described in Narciss & Huth, 2006)(as described in Narciss & Huth, 2006), we will focus in this contribution on the first objective: predictive modelling of performance and learning behaviour (Baker, 2010; Sao Pedro et al., 2013).”
• Educational data mining and learning analytics for 21st century higher education: A review and synthesis (2019, neutral): “…computer-supported predictive analytics (CSPA), computer-supported behavioral analytics (CSBA), and computer-supported visualization analytics (CSVA): that are widely used in previous works (Baker, 2010; Perera et al., 2009; Romero and Ventura, 2007; Romero and Ventura, 2010; Romero et al., 2008).”