3,447 research outputs found
Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network
Personalized recommender systems rely on each user's personal usage data in
the system, in order to assist in decision making. However, privacy policies
protecting users' rights prevent these highly personal data from being publicly
available to a wider researcher audience. In this work, we propose a memory
biased random walk model on multilayer sequence network, as a generator of
synthetic sequential data for recommender systems. We demonstrate the
applicability of the synthetic data in training recommender system models for
cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape
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
Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions
In this work, we explore video lecture interaction in Massive Open Online
Courses (MOOCs), which is central to student learning experience on these
educational platforms. As a research contribution, we operationalize video
lecture clickstreams of students into cognitively plausible higher level
behaviors, and construct a quantitative information processing index, which can
aid instructors to better understand MOOC hurdles and reason about
unsatisfactory learning outcomes. Our results illustrate how such a metric
inspired by cognitive psychology can help answer critical questions regarding
students' engagement, their future click interactions and participation
trajectories that lead to in-video & course dropouts. Implications for research
and practice are discusse
The Metabolism and Growth of Web Forums
We view web forums as virtual living organisms feeding on user's attention
and investigate how these organisms grow at the expense of collective
attention. We find that the "body mass" () and "energy consumption" ()
of the studied forums exhibits the allometric growth property, i.e., . This implies that within a forum, the network transporting
attention flow between threads has a structure invariant of time, despite of
the continuously changing of the nodes (threads) and edges (clickstreams). The
observed time-invariant topology allows us to explain the dynamics of networks
by the behavior of threads. In particular, we describe the clickstream
dissipation on threads using the function , in which
is the clickstreams to node and is the clickstream dissipated
from . It turns out that , an indicator for dissipation efficiency,
is negatively correlated with and sets the lower boundary
for . Our findings have practical consequences. For example,
can be used as a measure of the "stickiness" of forums, because it quantifies
the stable ability of forums to convert into , i.e., to remain users
"lock-in" the forum. Meanwhile, the correlation between and
provides a convenient method to evaluate the `stickiness" of forums. Finally,
we discuss an optimized "body mass" of forums at around that minimizes
and maximizes .Comment: 6 figure
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