=Paper=
{{Paper
|id=Vol-1137/LAK14CLA_submission_8
|storemode=property
|title=Fostering a Learning Community in MOOCs
|pdfUrl=https://ceur-ws.org/Vol-1137/LAK14CLA_submission_8.pdf
|volume=Vol-1137
|dblpUrl=https://dblp.org/rec/conf/lak/Hmelo-SilverRL14
}}
==Fostering a Learning Community in MOOCs==
https://ceur-ws.org/Vol-1137/LAK14CLA_submission_8.pdf
Fostering a Learning Community in MOOCs
Cindy E. Hmelo-Silver Carolyn P. Rosé Jeff Levy
Indiana University Carnegie Mellon OfficeHours
543 Eigenmann Hall University5000 Forbes Avenue 817 W. Peachtree St
Bloomington IN 47406 Pittsburgh, PA 15217 Atlanta GA 30308
1. Introduction • E4. Self-directed study
A key hurdle preventing MOOCs from reaching their • E5. Evaluate their learning and performance.
transformative potential is that they fail to provide a social Performance goals for facilitator
environment that is conducive to sustained collaborative • P1. Keep all students active in the learning process
engagement and learning. Correlational analyses from existing • P2. Keep the learning process on track
MOOCs demonstrate a reliable connection between social • P3. Make the student’s thoughts and their depth of
integration into the threaded discussions and course retention. The understanding apparent
best evidence so far suggests an important connection between • P4. Encourage student reliance on selves and peers
social support and retention consistent with findings in other types for direction and information.
of online communities. If we can engineer MOOCs to achieve Strategies that can support these goals are shown in Table 1.
greater success at providing affordances for sustained social Table 1. Example Strategies (adapted from [3])
engagement and learning, the potential for impact is immense.
2. Facilitating Small Groups
Introducing facilitation into large scale threaded discussion
in MOOCs requires extensions to existing wisdom on facilitation
of small groups [1; 2]. We seek to understand how better to
facilitate student-centered learning and student agency (and deep
engagement) on a grander scale in online environments. We
know a lot about strategies that can be used to support students in
collaborative projects and problem based learning [3] but these
have generally only been used on a small scale because they
requires intense monitoring of the collaborative discourse for both
collaboration and content (what we might term productive
collaboration). Engineering facilitation requires attention at two
levels: supporting the collaboration among learners, but also
supporting facilitation for teaching assistants or other tutors.
To accomplish larger scale facilitation, there are two
important aspects to consider. One is identify markers of both
collaborative activities and disciplinary content and/or
practices. Some of these might include indicators such as making
contributions, asking responding to questions, social network
analysis, or indicators of transactivity. Learning analytics (LA)
techniques are important to be able to accomplish this [4; 5]. The
markers of disciplinary content and practices may be more
difficult to measure. The second aspect is acting on what is For TA’s or any kind of automated agents to use these
learned through LA. A first step might be a dashboard that might strategies, it will be important to have indicators so that whatever
alert instructors as to where they might need to intervene as well the facilitation strategy needed, there is some way to first indicate
as suggesting appropriate strategies. that attention is needed in a group. Some of this might be timed--
for example, having a group summarize where they are could be
Research on these massive networked communities should
done at intervals based on the ebb and flow of course assignments
build on research on best practices in CSCL and problem-based
learning (PBL; e.g., [3; 6-10]). This research suggests that certain and content difficulty. Some of the social prompts could be based
instructional goals can support productive collaboration, and that on shallow indicators like participation and length of
there are particular cognitive, metacognitive, and social strategies posts. Others would need more semantically oriented
that can best serve particular kinds of goals. For example, in some indicators. For example, the first time that someone mentions or
groups such as a PBL group, goals might include: proposes some complex idea, it would be reasonable to ask the
group if they understand. Semantic indicators could compare what
Educational Goals for students: is being discussed with key course objectives and past
• E1. Construct causal explanations discussion. To the extent that these indicators can be identified,
• E2. Employ effective reasoning and argumentation. these are other places where simple intervention might be quite
productive. An area ripe for research is identifying these
• E3. Identify knowledge limitations
indicators, understanding what kinds of strategies might be
generally useful and the extent to which these indicators are cognitive engagement. Conversely, measures of relationship loss
amenable to computational solutions. predict higher dropout. Similarly, membership in subgroups
where there is high attrition also predicts higher attrition. To
In our computational work, we have been developing model relationship formation over time, we are experimenting
techniques for extracting indicators within messages that tell us with probabilistic graphical models that combine text, social
something about a student’s orientation at a time point (i.e., a
network, and thread structure representation in order to identify
week of participation within the course) towards the course, which coordinated group behavior that may indicate the formation of a
will enable us to make a prediction about how likely it is that the subcommunity [13]. Beyond measures at the student level that
student will drop out of the course on the next time point. The pick out students who are at risk and may need extra attention, we
relevant levels of analysis are post, week, and trajectory within the use structural equation modeling techniques to identify the factors
course. First, we extract indicators from individual that affect whether a thread that is started by a student who is
messages. Then we aggregate messages within a week in order to reaching out for help will get a satisfactory response. In order to
construct an indicator for a week. We then use a survival analysis reduce the load on instructors, we are developing matrix
to model the probability that the student with drop out on the next
factorization techniques to identify community members whose
time point given the value of the variable. In this way, we can effort we may be able to enlist to respond to at risk threads [14].
identify risk factors that instructors should be aware of so that
they can focus their efforts to support those students. Indicators Recent research in the learning sciences has begun to
we have explored include social network analysis measures such address how to use LA productively [15]. At the same time,
as authority and hub scores [2], indicators of social subgraph MOOCs can provide transformative opportunities for learners if
membership [1] measures of motivation and cognitive we can identify and support learning communities within the
engagement [11], and indicators of relationship formation and larger communities. Bringing the LA and learning sciences
measures of loss of relationship due to attrition of other students communities together for discussion and joint efforts can provide
[12]. Overall, we find that measures of high authority and hub opportunities to better understand how to facilitate learning
scores as well as high numbers of formed relationships predict communities in this frontier.
lower attrition, as do measures of high motivation and high
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