472 research outputs found
Massive Open Online Courses Temporal Profiling for Dropout Prediction
Massive Open Online Courses (MOOCs) are attracting the attention of people
all over the world. Regardless the platform, numbers of registrants for online
courses are impressive but in the same time, completion rates are
disappointing. Understanding the mechanisms of dropping out based on the
learner profile arises as a crucial task in MOOCs, since it will allow
intervening at the right moment in order to assist the learner in completing
the course. In this paper, the dropout behaviour of learners in a MOOC is
thoroughly studied by first extracting features that describe the behavior of
learners within the course and then by comparing three classifiers (Logistic
Regression, Random Forest and AdaBoost) in two tasks: predicting which users
will have dropped out by a certain week and predicting which users will drop
out on a specific week. The former has showed to be considerably easier, with
all three classifiers performing equally well. However, the accuracy for the
second task is lower, and Logistic Regression tends to perform slightly better
than the other two algorithms. We found that features that reflect an active
attitude of the user towards the MOOC, such as submitting their assignment,
posting on the Forum and filling their Profile, are strong indicators of
persistence.Comment: 8 pages, ICTAI1
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Investigating Variation in Learning Processes in a FutureLearn MOOC
Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining (EPM) in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals
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
Online Learning for Mixture of Multivariate Hawkes Processes
Online learning of Hawkes processes has received increasing attention in the
last couple of years especially for modeling a network of actors. However,
these works typically either model the rich interaction between the events or
the latent cluster of the actors or the network structure between the actors.
We propose to model the latent structure of the network of actors as well as
their rich interaction across events for real-world settings of medical and
financial applications. Experimental results on both synthetic and real-world
data showcase the efficacy of our approach.Comment: 12 pages, 6 figures, 3 table
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