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Present Bias and Nudges - Field Evidence from a MOOC
Last registered on November 10, 2016

Pre-Trial

Trial Information
General Information
Title
Present Bias and Nudges - Field Evidence from a MOOC
RCT ID
AEARCTR-0001780
Initial registration date
November 10, 2016
Last updated
November 10, 2016 10:16 AM EST
Location(s)
Region
Primary Investigator
Affiliation
Potsdam University
Other Primary Investigator(s)
PI Affiliation
Hasso Plattner Institute, University of Potsdam
PI Affiliation
RWI - Leibniz Institute for Economic Research, Ruhr-University Bochum
PI Affiliation
RWI - Leibniz Institute for Economic Research
Additional Trial Information
Status
On going
Start date
2016-09-14
End date
2017-07-31
Secondary IDs
Abstract
Massive open online courses (MOOCs) have very low completion rates. Often not more than 15% of those signing up finish the course with a certificate. In this paper we argue that present bias helps explain this phenomenon. In a randomized field experiment we test whether prompting enrollees to schedule their next study sessions increases MOOC engagement and completion. Additionally, we elicit time-inconsistency and examine how awareness of it influences treatment effects.
External Link(s)
Registration Citation
Citation
Andor, Mark et al. 2016. "Present Bias and Nudges - Field Evidence from a MOOC." AEA RCT Registry. November 10. https://doi.org/10.1257/rct.1780-1.0.
Former Citation
Andor, Mark et al. 2016. "Present Bias and Nudges - Field Evidence from a MOOC." AEA RCT Registry. November 10. https://www.socialscienceregistry.org/trials/1780/history/11742.
Experimental Details
Interventions
Intervention(s)
We set up a randomized field experiment to test the following research questions empirically:
1. Does prompting individuals to plan ahead increase MOOC completion rates?
2. To what extent can time-inconsistent preferences explain low MOOC completion rates?
3. Which individuals are most strongly influenced by the planning tools?

We conduct a field experiment with openHPI and openSAP, MOOC providers in the field of internet technology, computer science and software usage and development. We test the effect of two different planning tools with which MOOC participants can schedule their next MOOC study session or set up a plan for the entire MOOC duration. Additionally, participants are reminded of their scheduled time via email.
Intervention Start Date
2016-09-14
Intervention End Date
2017-07-31
Primary Outcomes
Primary Outcomes (end points)
The main outcome variable is course completion, which we define as earning a certificate. We will especially focus on participants that have the intention to earn a certificate, because initial motivation of participants may vary (Koller et al. 2013).

In addition, we analyze the effect of our treatments on the course activity such as video visits, number of sessions, session duration, performance in quizzes, assignments, and the final exam.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The experiment consists of five treatments, which are all versions of a planning tool. These treatments are transmitted via additional pop-ups, which are embedded in the MOOC interface. This ensures that participants perceive the treatment as part of the course design rather than as an add-on. The control group views a pop-up with supportive feedback on their course progress. In addition to this supportive feedback, the treatment groups are exposed to one of two different planning tools. Treatment group 1, 2 and 3 will have the opportunity to schedule their next study session. Treatment group 4 and 5 can set up a workload schedule for the entire course duration.
Experimental Design Details
Randomization Method
Randomization is done by a computer.
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
n/a
Sample size: planned number of observations
>= 1500 per treatment, same size for the control group
Sample size (or number of clusters) by treatment arms
n/a
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable effect size is dependent on how many participants opt-out or opt-in of the treatment. Therefore, we cannot determine it ex-ante.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers