SXSW EDU 2020
Artificial Intelligence for Course Recommendation
Description:
With over 800 courses in a single platform, it becomes a challenge to decide what to study next. For a single student, topics are somewhat similar while their interests are diverse. Wasting time is a big worry.
Traditional recommender algorithms failed miserably in this case study, but recurrent neural networks, presented statistically significant results with students being more engaged, for a longer period of time and, most importantly, finishing more courses.
Related Media
Takeaways
- Understand how recommenders can help students
- Understand how to tackle that challenge with recurrent neural networks and how to measure its impact with students
- Interpret the results found in the case study, avoiding some biases while doing so
Speakers
- Guilherme Silveira, Head of Education, Alura
Organizer
Guilherme Silveira, Head Of Education, Alura
SXSW reserves the right to restrict access to or availability of comments related to PanelPicker proposals that it considers objectionable.
Add Comments