Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph