Deep neural networks have recently demonstrated the state of the art and superior performance when applied to common pattern recognition problems, including image and speech recognition.
I'm wondering if you are familiar with the research that has applied deep neural networks in the fields of educational data mining or learning analytics?
Dear Andrii, it indeed seems as if there not yet is a lot of published research in this field. The embedded links to two brief searches may guide your thoughts somewhat. It may also be advisable to search on more appropriate search engines than Google Scholar (like I did).
https://scholar.google.co.za/scholar?hl=en&q=%22deep+neural+networks%22+and++%22educational+data+mining%22&btnG=&as_sdt=1%2C5&as_sdtp=
https://scholar.google.co.za/scholar?hl=en&q=%22deep+neural+networks%22+and+%22learning+analytics%22+and+education&btnG=&as_sdt=1%2C5&as_sdtp=
https://www.jisc.ac.uk/blog/ten-search-engines-for-researchers-that-go-beyond-google-11-jul-2013
Dear Andrii, it indeed seems as if there not yet is a lot of published research in this field. The embedded links to two brief searches may guide your thoughts somewhat. It may also be advisable to search on more appropriate search engines than Google Scholar (like I did).
https://scholar.google.co.za/scholar?hl=en&q=%22deep+neural+networks%22+and++%22educational+data+mining%22&btnG=&as_sdt=1%2C5&as_sdtp=
https://scholar.google.co.za/scholar?hl=en&q=%22deep+neural+networks%22+and+%22learning+analytics%22+and+education&btnG=&as_sdt=1%2C5&as_sdtp=
https://www.jisc.ac.uk/blog/ten-search-engines-for-researchers-that-go-beyond-google-11-jul-2013
Dear Leona, thanks for sharing the search engine links. I found several relevant papers:
1. Deep Neural Networks and How They Apply to Sequential Education Data by Steven Tang, Joshua C. Peterson and Zachary A. Pardos: http://dl.acm.org/citation.cfm?id=2893444
2. Deep Knowledge Tracing by Chris Piech et. al.: http://papers.nips.cc/paper/5654-deep-knowledge-tracing.pdf
Will keep on updating the list as I find more.
http://dl.acm.org/citation.cfm?id=2893444
http://papers.nips.cc/paper/5654-deep-knowledge-tracing.pdf
Hi Andrii,
I can show you somethings about Neural Networks by my publication.
Access the link below:
http://siaibib01.univali.br/pdf/Vital%20Pereira%20dos%20Santos%20Junior.pdf
Good research.
Dear Vital Pereira dos Santos Junior, thanks for sharing. The abstract looks relevant, but the problem is that I do not read Portuguese. It would be helpful if you could summarise in few words how exactly neural networks were applied.
Modelling Student Behavior using Granular Large Scale Action Data from a MOOC
ABSTRACT: Digital learning environments generate a precise record of the actions learners take as they interact with learning materials and complete exercises towards comprehension. With this high quantity of sequential data comes the potential to apply time series models to learn about underlying behavioral patterns and trends that characterize successful learning based on the granular record of student actions. There exist several methods for looking at longitudinal, sequential data like those recorded from learning environments. In the field of language modelling, traditional n-gram techniques and modern recurrent neural network (RNN) approaches have been applied to algorithmically find structure in language and predict the next word given the previous words in the sentence or paragraph as input. In this paper, we draw an analogy to this work by treating student sequences of resource views and interactions in a MOOC as the inputs and predicting students' next interaction as outputs. In this study, we train only on students who received a certificate of completion. In doing so, the model could potentially be used for recommendation of sequences eventually leading to success, as opposed to perpetuating unproductive behavior. Given that the MOOC used in our study had over 3,500 unique resources, predicting the exact resource that a student will interact with next might appear to be a difficult classification problem. We find that simply following the syllabus (built-in structure of the course) gives on average 23% accuracy in making this prediction, followed by the n-gram method with 70.4%, and RNN based methods with 72.2%. This research lays the ground work for recommendation in a MOOC and other digital learning environments where high volumes of sequential data exist.
https://arxiv.org/abs/1608.04789
Very recent (uploaded to ResearchGate on May 06, 2017):
Ghosh, S., Chollet, M., Laksana, E., Morency, L-P., Scherer, S. Affect-LM: A Neural Language Model for Customizable Affective Text Generation, 2017.
Full text available from arXiv or ResearchGate.
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