In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.
Description
Visualizing patterns of student engagement and performance in MOOCs
%0 Conference Paper
%1 Coffrin:2014:VPS:2567574.2567586
%A Coffrin, Carleton
%A Corrin, Linda
%A de Barba, Paula
%A Kennedy, Gregor
%B Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
%C New York, NY, USA
%D 2014
%I ACM
%K mooc moocs
%P 83--92
%R 10.1145/2567574.2567586
%T Visualizing Patterns of Student Engagement and Performance in MOOCs
%U http://doi.acm.org/10.1145/2567574.2567586
%X In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.
%@ 978-1-4503-2664-3
@inproceedings{Coffrin:2014:VPS:2567574.2567586,
abstract = {In the last five years, the world has seen a remarkable level of interest in Massive Open Online Courses, or MOOCs. A consistent message from universities participating in MOOC delivery is their eagerness to understand students' online learning processes. This paper reports on an exploratory investigation of students' learning processes in two MOOCs which have different curriculum and assessment designs. When viewed through the lens of common MOOC learning analytics, the high level of initial student interest and, ultimately, the high level of attrition, makes these two courses appear very similar to each other, and to MOOCs in general. With the goal of developing a greater understanding of students' patterns of learning behavior in these courses, we investigated alternative learning analytic approaches and visual representations of the output of these analyses. Using these approaches we were able to meaningfully classify student types and visualize patterns of student engagement which were previously unclear. The findings from this research contribute to the educational community's understanding of students' engagement and performance in MOOCs, and also provide the broader learning analytics community with suggestions of new ways to approach learning analytic data analysis and visualization.},
acmid = {2567586},
added-at = {2014-06-08T11:39:08.000+0200},
address = {New York, NY, USA},
author = {Coffrin, Carleton and Corrin, Linda and de Barba, Paula and Kennedy, Gregor},
biburl = {https://www.bibsonomy.org/bibtex/2018316d521c11f083646dcd8eaa04a4c/griesbau},
booktitle = {Proceedings of the Fourth International Conference on Learning Analytics And Knowledge},
description = {Visualizing patterns of student engagement and performance in MOOCs},
doi = {10.1145/2567574.2567586},
interhash = {236ef3633082e15174159245094a7027},
intrahash = {018316d521c11f083646dcd8eaa04a4c},
isbn = {978-1-4503-2664-3},
keywords = {mooc moocs},
location = {Indianapolis, Indiana},
numpages = {10},
pages = {83--92},
publisher = {ACM},
series = {LAK '14},
timestamp = {2014-06-08T11:39:08.000+0200},
title = {Visualizing Patterns of Student Engagement and Performance in MOOCs},
url = {http://doi.acm.org/10.1145/2567574.2567586},
year = 2014
}