Recommender Systems in Technology Enhanced Learning

By Nikos Manouselis, Hendrik Drachsler, Riina Vuorikari, Hans G. K. Hummel, Rob Koper. Recommender Systems Handbook, 2011

462 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Information Systems

View Paper: https://www.semanticscholar.org/paper/2e1f6aef2bbb4c00faf4671ea2b60f12050c5d5f

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. Hendrik Drachsler
2. Katrien Verbert
3. Paul A. Kirschner
4. Chin-Chung Tsai
5. Wolfgang Greller
6. Erik Duval

In Developmental and Educational Psychology

1. Hendrik Drachsler
2. Paul A. Kirschner
3. Ghulam Mustafa
4. Juanjuan Chen
5. Minhong Wang
6. Chin-Chung Tsai
7. Wolfgang Greller
8. Zhendong Niu

In Information Systems

1. Ghulam Mustafa
2. Zhendong Niu
3. John K. Tarus
4. Katrien Verbert
5. Hendrik Drachsler
6. Paul A. Kirschner
7. Nikos Manouselis
8. Chin-Chung Tsai

Citations in the corpus

• Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012, background): “In contrast to earlier surveys on recommender systems for learning [70], this paper focuses specifically on a particular set of recommender systems that incorporate contextual information in the recommendation process.”
• Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, methodology): “…studies focusedmore on conventional recommendation techniques like collaborative filtering and content-based recommendation (Bobadilla et al. 2013; Manouselis et al. 2011), hybrid recommendation (Burke 2007) and other emerging techniques such as context-aware (CA) recommenders (Abbas et al. 2015;…”
• Translating Learning into Numbers: A Generic Framework for Learning Analytics (2012, methodology): “dataTEL challenge—The first dataTEL challenge was launched as part of the first workshop on Recommender Systems for TEL (Manouselis et al., 2010), jointly organized by the 4th ACM Conference on Recommender Systems and the 5th European Conference on Technology Enhanced Learning (EC-TEL 2010) in Septe”
• Attention please!: learning analytics for visualization and recommendation (2011, background): “By collecting data about user behavior, learning analytics can also be mined for recommendations, of resources, activities or people [17].”
• The Role of Collaboration, Computer Use, Learning Environments, and Supporting Strategies in CSCL: A Meta-Analysis (2018, background): “…apply to both individual learning and CL. Personalization can be achieved by adaptive hypermedia and recommender systems (Brusilovsky & Henze, 2007; Manouselis, Drachsler, Vuorikari, Hummel, & Koper, 2011; Wang, Yuan, Kirschner, Kushniruk, & Peng, 2018), which enable users to access learning…”