Z. Papamitsiou

Computer Science Applications · 2014

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Science Applications — 13 citations into it
• Information Systems — 9 citations into it
• Artificial Intelligence — 8 citations into it
• Developmental and Educational Psychology — 5 citations into it

Whom Z. Papamitsiou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Cristóbal Romero
2. Leah P. Macfadyen
3. Vasileios Terzis
4. Sebastián Ventura
5. Shane Dawson
6. Anastasios A. Economides
7. Paulo Blikstein
8. Alejandro Peña Ayala
9. Mykola Pechenizkiy
10. George Siemens

Who trusts Z. Papamitsiou overall

1. Katerina Mangaroska
2. Michail N. Giannakos
3. Luca Oneto
4. Atikah Shemshack
5. Dirk Ifenthaler

Papers from Z. Papamitsiou's perspective

Ordered by how strongly the researchers Z. Papamitsiou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Educational Data Mining: A Review of the State of the Art (IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews))
2. Educational data mining: A survey from 1995 to 2005 (Expert systems with applications)
3. Learning analytics and educational data mining: towards communication and collaboration (International Conference on Learning Analytics and Knowledge)
4. Predicting Students Drop Out: A Case Study (Educational Data Mining)
5. Mining LMS data to develop an "early warning system" for educators: A proof of concept (Comput. Educ.)
6. Data Mining Algorithms to Classify Students (Educational Data Mining)
7. Using learning analytics to assess students' behavior in open-ended programming tasks (International Conference on Learning Analytics and Knowledge)
8. Educational data mining: A survey and a data mining-based analysis of recent works (Expert systems with applications)
9. The acceptance and use of computer based assessment (Comput. Educ.)
10. Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models (Comput. Educ.)

Their papers

• Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014) — 672 citations