Learning at Scale

By Ido Roll, Daniel M. Russell, Dragan Gasevic. 2018

59 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Education, ML Systems & Hardware

How researchers see this paper

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

In Computer Science Applications

1. Dietmar Jannach
2. Malte Ludewig
3. Yoshua Bengio
4. Alexandra Sasha Luccioni
5. Ido Roll
6. Konrad Paul Kording

In Developmental and Educational Psychology

1. Dietmar Jannach
2. Malte Ludewig
3. Yoshua Bengio
4. Ido Roll
5. Konrad Paul Kording
6. Demis Hassabis

In Education

1. Dietmar Jannach
2. Malte Ludewig
3. Ido Roll

In ML Systems & Hardware

1. Dietmar Jannach
2. Malte Ludewig
3. Yoshua Bengio
4. Alexandra Sasha Luccioni
5. Konrad Paul Kording
6. Alexandre Lacoste

Citations in the corpus

• Evaluation of session-based recommendation algorithms (2018, methodology): “Personalized transition cube, adapted from [Rendle et al. 2009].”
• Tackling Climate Change with Machine Learning (2019, background): “AIED also has the potential to improve educational outcomes in developing countries: for instance, creating scalable, adaptive online courses could give hundreds of thousands of learners in the developing world access to learning resources that they would not usually have in their local educational ”
• The impact of artificial intelligence on learner–instructor interaction in online learning (2021, background): “The opportunities for artificial intelligence (AI) in online learning and teaching are broad (Anderson et al., 1985; Baker, 2016; Roll et al., 2018; Seo et al., 2020b; VanLehn, 2011), ranging from personalized learning for students and automation of instructors’ routine tasks to AI-powered…”