NIPS 2016
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Machine Learning for Education

Richard Baraniuk · Jiquan Ngiam · Christoph Studer · Phillip Grimaldi · Andrew Lan

Room 129 + 130

In recent years, we have seen a rise in the amount of education data available through the digitization of education. Schools are starting to use technology in classrooms to create personalized learning experiences. Massive open online courses (MOOCs) have attracted millions of learners and present an opportunity for us to apply and develop machine learning methods towards improving student learning outcomes, leveraging the data collected.

However, development in student data analysis remains limited, and education largely follows a one-size-fits-all approach today. We have an opportunity to have a significant impact in revolutionizing the way (human) learning can work.

The goal of this workshop is to foster discussion and spur research between machine learning experts and researchers in education fields that can solve fundamental problems in education.

For this year's workshop, we are highlighting the following areas of interest:

-- Assessments and grading
Assessments are core in adaptive learning, formative learning, and summative evaluation. However, the creation and grading of quality assessments remains a difficult task for instructors. Machine learning methods can be applied to self-, peer-, auto-grading paradigms to both improve the quality of assessments and reduce the burden on instructors and students. These methods can also leverage the multimodal nature of learner data (i.e., textual/programming/mathematical open-form responses, demographic information, student interaction in discussion forums, video and audio recording of the class), posing challenges of how to effectively and efficiently fuse these different forms of data so that we can better understand learners.

-- Content augmentation and understanding:
Learning content is rich and multimodal (e.g., programming code, video, text, audio). There has been a growth of online educational resources in the past years, and we have an opportunity to leverage them further. Recent advances in natural language understanding can be applied to understand learning materials better and connect different sources together to create better learning experiences. This can help learners by providing them with more relevant resources and instructors in the creation of content.

-- Personalized learning and active interventions:
Personalized learning through custom feedback and interventions can make learning much more efficient, especially when we cater to the individual's background, goals, state of understanding, and learning context. Methods such as Markov Decision Processes and Multi-armed Bandits are applicable in these context.

-- Human-interpretability:
In education applications, transparency and interpretability is important as it can help learners better understand their learning state. Interpretability can provide instructors with insights to better guide their activities with students. It can also help education researchers better understand the foundations of human learning. This can also be especially critical where models are deployed in processes that grade students, as evaluation needs to demonstrate a degree of fairness.

This workshop will lead to new research directions in machine learning-driven educational research and also inspire the development of novel machine learning algorithms and theories that can extend to a large number of other applications that study human data.

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Timezone: America/Los_Angeles


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