Riiid's AI study on Session Dropout Prediction in a Mobile Learning Environment has been accepted at CSEDU
SEOUL, South Korea, Feb. 26, 2020 /PRNewswire/ -- AI Tutor now predicts when each user will drop out of a study session. Riiid, a global AI tutor solution provider and a member company of the Born2Global Centre, announced today that its AI research paper, entitled "Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment", has been accepted as a full paper at CSEDU, one of the top-tier International Conferences on Computer Supported Education.
The world-renowned conference dedicated to education technology brings experts from around the world to share and discuss computer-based education technologies such as AI, domain applications, learning-assistance information technologies, and learning and pedagogical methodologies.
"We are excited that Riiid's own research has been accepted as a full paper at a highly-recognized international conference in the AI Education field" said Youngnam Lee, Riiid's researcher and the lead author. "Riiid has been addressing various tasks in the education fields such as the correctness of responses and score prediction, using a deep-learning architecture transformer, which is mainly used for natural language processing (NLP) research. In the process, the firm has developed architecture modifications suitable for the education domain and achieved lessons and know-hows. The research paper was the result of such efforts and attempts."
Notably, the study has gained high recognition as it was the world's first attempt to investigate user session dropout in a mobile learning environment. Although digitalization has driven the education sector's shift to mobile, the difficulty in controlling external variables that can distract learning was viewed as the limitation of mobile learning. Riiid researched deep learning technologies to overcome such barriers, and the study findings suggest a pathway which can maximize learning effects by taking into account dropout probability of individual users. Thus far, domestic and foreign literature on student dropout research have mainly been on school dropouts and course dropouts within off-line environments, which have relatively long learning durations.
In the research paper, Riiid defined user dropout problems in a mobile learning environment and proposed a Deep Attentive Study Session Dropout Prediction (DAS), a novel Transformer based model, which accurately predicts dropout probability by deep attentive computations capturing complex relations among dynamic student interactions. Through several experiments, Riiid presented an optimal combination of input features and an analytical data set to increase the accuracy of dropout prediction.
The dataset used for the study involves a total of 13.8 million interactions from 210,000 users released at EdNet (github.com/riiid/ednet), an open-source AI-education database unveiled by Riiid early this year. Riiid's research team trained its prediction model with this large-scaled dataset and conducted experiments with different combinations of input features.
According to the study, the results from sequence size of five and the combination of nine variables—question ID, category, starting time, position in input sequence, position in session, response correctness, elapsed time, and on-time response— showed the best AUC (Area Under Curve), which means the highest prediction accuracy. The empirical studies based on this setting showed that the DAS model outperformed baseline models — LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) — by 12.2% in the area under the AUC.
Riiid's research findings have already been incorporated into its AI-engine, santA.Inside, which powers SANTA TOEIC's question recommendation algorithm. Riiid will expand the application beyond TOEIC to include its soon-to-be-released SAT and ACT solutions. That is, if there are questions to recommend with a similar predicted score gains, the algorithm recommends the question with a low dropout probability to induce users to continue.
"This study marks a meaningful milestone for Riiid as an AI education startup delivered concrete research results that would provide focused, in-depth, and applicable solutions to issues in the AI-enabled learning field," said Young-Jun Jang, CEO at Riiid. "Going forward, Riiid will further solidify its technology leadership by continuing research on AI education on both theoretical and practical levels."
Meanwhile, the study was invited to the oral session for a few outstanding full papers. Riiid's research team will present the study at the CSEDU 2020 Conference scheduled to take place in Prague, Czech in May 2020.
For more information on Riiid, visit https://riiid.co/en/main.
About Riiid!
Established in 2014, Riiid (https://riiid.co/en/main) is a global leading AI Tutor solution provider delivering creative disruption to the education market through its cutting-edge AI technology. Driving the democratization and equalization of educational opportunities, the company launched Santa TOEIC in 2017, the firm's first commercialized AI Tutor solution based on deep learning algorithm, which has attracted over one million users in South Korea alone. Riiid's AI Tutor Solution aims at replacing hard copy textbooks and traditional lectures with a personalized AI tutor that far outpaces human tutors in learning efficacy at a fraction of the cost. Based on its highly scalable and proven AI technology, Riiid is aggressively expanding its business to new test areas as well as into new geographical markets. It currently operates Santa TOEIC in Korea and Japan; and has launched Santa SAT in Vietnam.
About Born2Global Centre
Born2Global Centre (http://www.born2global.com) is a full-cycle service platform for global expansion. Since inception in 2013, Born2Global has been setting the standard for successful startup ecosystem as the main Korean government agency under the Ministry of Science and ICT. Born2Global has expanded and transformed startups to be engaged, equipped and be connected with the global market.
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