2017 Proceeding- General Chairs:
- Alyssa Wise,
- Phillip H. Winne,
- Grace Lynch,
- Program Chairs:
- Xavier Ochoa,
- Inge Molenaar,
- Shane Dawson,
- Publications Chair:
- Marek Hatala
The theme for LAK'17 purposely focused on the transdisciplinary nature of research in learning analytics. This theme extends the work of prior conferences that sought to bring together the diversity of disciplinary fields that now comprise learning analytics. The great diversity of papers submitted for LAK'17 demonstrates that LA research has very much embraced the benefits that can be leveraged from a truly transdisciplinary model of research. While there are inherent complexities in such an approach, the research presented for LAK'17 brings much excitement and promise to the field through the application of novel methods, cutting-edge learning technologies, and actual impact on the learning process. Following this theme, the aim of the conference is to provide a forum for presentation, exchange and discussion of research and practices regarding the transdisciplinary field of Learning Analytics.
Cited By
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Lau E, Wijeratne V and Chai K (2022). Prediction of Students’ Performance Based on Their Learning Experiences and Assessments: Statistical and Neural Network Approaches Computer Supported Education, 10.1007/978-3-031-14756-2_2, (24-39),
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Huang C, Hou Z, Liu Y, Wu Y and Qin W (2021). Stock market prediction based on neural network 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 10.1117/12.2623098, 9781510650329, (80)
Index Terms
- Proceedings of the Seventh International Learning Analytics & Knowledge Conference