97,387 research outputs found
Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]
Scalable probabilistic modeling and prediction in high dimensional
multivariate time-series is a challenging problem, particularly for systems
with hidden sources of dependence and/or homogeneity. Examples of such problems
include dynamic social networks with co-evolving nodes and edges and dynamic
student learning in online courses. Here, we address these problems through the
discovery of hierarchical latent groups. We introduce a family of Conditional
Latent Tree Models (CLTM), in which tree-structured latent variables
incorporate the unknown groups. The latent tree itself is conditioned on
observed covariates such as seasonality, historical activity, and node
attributes. We propose a statistically efficient framework for learning both
the hierarchical tree structure and the parameters of the CLTM. We demonstrate
competitive performance in multiple real world datasets from different domains.
These include a dataset on students' attempts at answering questions in a
psychology MOOC, Twitter users participating in an emergency management
discussion and interacting with one another, and windsurfers interacting on a
beach in Southern California. In addition, our modeling framework provides
valuable and interpretable information about the hidden group structures and
their effect on the evolution of the time series
From participation to dropout
The academic e-learning practice has to deal with various participation patterns and types of online learners with different support needs. The online instructors are challenged to recognize these and react accordingly. Among the participation patterns, special attention is requested by dropouts, which can perturbate online collaboration. Therefore we are in search of a method of early identification of participation patterns and prediction of dropouts. To do this, we use a quantitative view of participation that takes into account only observable variables. On this background we identify in a field study the participation indicators that are relevant for the course completion, i.e. produce significant differences between the completion and dropout sub-groups. Further we identify through cluster analysis four participation patterns with different support needs. One of them is the dropout cluster that could be predicted with an accuracy of nearly 80%. As a practical consequence, this study recommends a simple, easy-to-implement prediction method for dropouts, which can improve online teaching. As a theoretical consequence, we underline the role of the course didactics for the definition of participation, and call for refining previous attrition models
The engagement of mature distance students
This is an Accepted Manuscript of an article published by Taylor & Francis in Higher Education Research and Development in 2013, available online: http://www.tandfonline.com/10.1080/07294360.2013.777036.Publishe
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Modelling factors for predicting student learning outcomes in higher education
This paper presents a heuristic model of student leaning as a means to understanding the scope of factors to be considered in making predictions about student learning. It is underpinned by a review of a wide body of literature. The model is drawn from Price and Richardson’s 4P model (2004) that considered factors in improving student learning and argues that the same issues apply to predicting student learning outcomes. It builds upon existing research into learning and teaching. It is an articulation and an extension of Dunkin and Biddle’s (1974) model, the Biggs (1985) original Presage-Process-Product model and research by Prosser and Trigwell (1999). The model has four main groups of factors: presage, perceptions, process and product. The presage group contains personological and situational factors such as context. Perceptions include how students conceive learning, how teachers conceive teaching, and the context. The process group of factors incorporates approaches to learning in students and teachers approaches to teaching. The model is presented as a basis for engaging in future research in a holistic manner that may bear further fruit in predicting student learning
Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners
This work is an attempt to discover hidden structural configurations in
learning activity sequences of students in Massive Open Online Courses (MOOCs).
Leveraging combined representations of video clickstream interactions and forum
activities, we seek to fundamentally understand traits that are predictive of
decreasing engagement over time. Grounded in the interdisciplinary field of
network science, we follow a graph based approach to successfully extract
indicators of active and passive MOOC participation that reflect persistence
and regularity in the overall interaction footprint. Using these rich
educational semantics, we focus on the problem of predicting student attrition,
one of the major highlights of MOOC literature in the recent years. Our results
indicate an improvement over a baseline ngram based approach in capturing
"attrition intensifying" features from the learning activities that MOOC
learners engage in. Implications for some compelling future research are
discussed.Comment: "Shared Task" submission for EMNLP 2014 Workshop on Modeling Large
Scale Social Interaction in Massively Open Online Course
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