84 research outputs found
Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses
Massive Open Online Courses (MOOCs) offer a new scalable paradigm for
e-learning by providing students with global exposure and opportunities for
connecting and interacting with millions of people all around the world. Very
often, students work as teams to effectively accomplish course related tasks.
However, due to lack of face to face interaction, it becomes difficult for MOOC
students to collaborate. Additionally, the instructor also faces challenges in
manually organizing students into teams because students flock to these MOOCs
in huge numbers. Thus, the proposed research is aimed at developing a robust
methodology for dynamic team formation in MOOCs, the theoretical framework for
which is grounded at the confluence of organizational team theory, social
network analysis and machine learning. A prerequisite for such an undertaking
is that we understand the fact that, each and every informal tie established
among students offers the opportunities to influence and be influenced.
Therefore, we aim to extract value from the inherent connectedness of students
in the MOOC. These connections carry with them radical implications for the way
students understand each other in the networked learning community. Our
approach will enable course instructors to automatically group students in
teams that have fairly balanced social connections with their peers, well
defined in terms of appropriately selected qualitative and quantitative network
metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of
Digital Information & Web Technologies (ICADIWT), India, February 2014 (6
pages, 3 figures
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
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
A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Small Group Learning
Curiosity is a vital metacognitive skill in educational contexts, leading to
creativity, and a love of learning. And while many school systems increasingly
undercut curiosity by teaching to the test, teachers are increasingly
interested in how to evoke curiosity in their students to prepare them for a
world in which lifelong learning and reskilling will be more and more
important. One aspect of curiosity that has received little attention, however,
is the role of peers in eliciting curiosity. We present what we believe to be
the first theoretical framework that articulates an integrated socio-cognitive
account of curiosity that ties observable behaviors in peers to underlying
curiosity states. We make a bipartite distinction between individual and
interpersonal functions that contribute to curiosity, and multimodal behaviors
that fulfill these functions. We validate the proposed framework by leveraging
a longitudinal latent variable modeling approach. Findings confirm a positive
predictive relationship between the latent variables of individual and
interpersonal functions and curiosity, with the interpersonal functions
exercising a comparatively stronger influence. Prominent behavioral
realizations of these functions are also discovered in a data-driven manner. We
instantiate the proposed theoretical framework in a set of strategies and
tactics that can be incorporated into learning technologies to indicate, evoke,
and scaffold curiosity. This work is a step towards designing learning
technologies that can recognize and evoke moment-by-moment curiosity during
learning in social contexts and towards a more complete multimodal learning
analytics. The underlying rationale is applicable more generally for developing
computer support for other metacognitive and socio-emotional skills.Comment: arXiv admin note: text overlap with arXiv:1704.0748
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