588 research outputs found
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
Dropout Model Evaluation in MOOCs
The field of learning analytics needs to adopt a more rigorous approach for
predictive model evaluation that matches the complex practice of
model-building. In this work, we present a procedure to statistically test
hypotheses about model performance which goes beyond the state-of-the-practice
in the community to analyze both algorithms and feature extraction methods from
raw data. We apply this method to a series of algorithms and feature sets
derived from a large sample of Massive Open Online Courses (MOOCs). While a
complete comparison of all potential modeling approaches is beyond the scope of
this paper, we show that this approach reveals a large gap in dropout
prediction performance between forum-, assignment-, and clickstream-based
feature extraction methods, where the latter is significantly better than the
former two, which are in turn indistinguishable from one another. This work has
methodological implications for evaluating predictive or AI-based models of
student success, and practical implications for the design and targeting of
at-risk student models and interventions
Resource Mention Extraction for MOOC Discussion Forums
In discussions hosted on discussion forums for MOOCs, references to online
learning resources are often of central importance. They contextualize the
discussion, anchoring the discussion participants' presentation of the issues
and their understanding. However they are usually mentioned in free text,
without appropriate hyperlinking to their associated resource. Automated
learning resource mention hyperlinking and categorization will facilitate
discussion and searching within MOOC forums, and also benefit the
contextualization of such resources across disparate views. We propose the
novel problem of learning resource mention identification in MOOC forums. As
this is a novel task with no publicly available data, we first contribute a
large-scale labeled dataset, dubbed the Forum Resource Mention (FoRM) dataset,
to facilitate our current research and future research on this task. We then
formulate this task as a sequence tagging problem and investigate solution
architectures to address the problem. Importantly, we identify two major
challenges that hinder the application of sequence tagging models to the task:
(1) the diversity of resource mention expression, and (2) long-range contextual
dependencies. We address these challenges by incorporating character-level and
thread context information into a LSTM-CRF model. First, we incorporate a
character encoder to address the out-of-vocabulary problem caused by the
diversity of mention expressions. Second, to address the context dependency
challenge, we encode thread contexts using an RNN-based context encoder, and
apply the attention mechanism to selectively leverage useful context
information during sequence tagging. Experiments on FoRM show that the proposed
method improves the baseline deep sequence tagging models notably,
significantly bettering performance on instances that exemplify the two
challenges
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