4,468 research outputs found
Analyzing collaborative learning processes automatically
In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in
Early Churn Prediction from Large Scale User-Product Interaction Time Series
User churn, characterized by customers ending their relationship with a
business, has profound economic consequences across various
Business-to-Customer scenarios. For numerous system-to-user actions, such as
promotional discounts and retention campaigns, predicting potential churners
stands as a primary objective. In volatile sectors like fantasy sports,
unpredictable factors such as international sports events can influence even
regular spending habits. Consequently, while transaction history and
user-product interaction are valuable in predicting churn, they demand deep
domain knowledge and intricate feature engineering. Additionally, feature
development for churn prediction systems can be resource-intensive,
particularly in production settings serving 200m+ users, where inference
pipelines largely focus on feature engineering. This paper conducts an
exhaustive study on predicting user churn using historical data. We aim to
create a model forecasting customer churn likelihood, facilitating businesses
in comprehending attrition trends and formulating effective retention plans.
Our approach treats churn prediction as multivariate time series
classification, demonstrating that combining user activity and deep neural
networks yields remarkable results for churn prediction in complex
business-to-customer contexts.Comment: 12 pages, 3 tables, 8 figures, Accepted in ICML
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Most current studies on survey analysis and risk tolerance modelling lack
professional knowledge and domain-specific models. Given the effectiveness of
generative adversarial learning in cross-domain information, we design an
Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale
inequality. ADGAN utilizes the information-sufficient domain to provide extra
information to improve the representation learning on the
information-insufficient domain via domain alignment. We provide data analysis
and user model on two data sources: Consumer Consumption Information and Survey
Information. We further test ADGAN on a real-world dataset with view embedding
structures and show ADGAN can better deal with the class imbalance and
unqualified data space than state-of-the-art, demonstrating the effectiveness
of leveraging asymmetrical domain information
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