Title,Url,Last update date,Published at,First registered on,RCT_ID,DOI Number,Primary Investigator,Status,Start date,End date,Keywords,Country names,Other Primary Investigators,Jel code,Secondary IDs,Abstract,External Links,Sponsors,Partners,Intervention start date,Intervention end date,Intervention,Primary outcome end points,Primary outcome explanation,Secondary outcome end points,Secondary outcome explanation,Experimental design,Experimental design details,Randomization method,Randomization unit,Sample size number clusters,Sample size number observations,Sample size number arms,Minimum effect size,IRB,Analysis Plan Documents,Intervention completion date,Data collection completion,Data collection completion date,Number of clusters,Attrition correlated,Total number of observations,Treatment arms,Public data,Public data url,Program files,Program files url,Post trial documents csv,Relevant papers for csv Present Bias and Nudges - Field Evidence from a MOOC,http://www.socialscienceregistry.org/trials/1780,"November 10, 2016",2016-11-10 10:16:56 -0500,2016-11-10,AEARCTR-0001780,10.1257/rct.1780-1.0,Sylvi Rzepka rzepka@empwifo.uni-potsdam.de,on_going,2016-09-14,2017-07-31,"[""education"", ""Massive Open Online Courses"", ""time-inconsistency"", ""randomized controlled trial""]",Private,"Jan Renz (Jan.Renz@hpi.de) Hasso Plattner Institute, University of Potsdam; Katja Fels (Katja.Fels@rwi-essen.de) RWI - Leibniz Institute for Economic Research, Ruhr-University Bochum; Mark Andor (Mark.Andor@rwi-essen.de) RWI - Leibniz Institute for Economic Research","I21, I29, C93, D03","","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.","","","",2016-09-14,2017-07-31,"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.","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.","",,,"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.","",Randomization is done by a computer.,Individual,n/a,">= 1500 per treatment, same size for the control group",n/a,"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.","",Private,,,,"",,"","",,"",,"","",""