Supporting the Encouragement of Forum Participation

We analyze the forum contribution rates during forty offerings of 12 college courses reaching back to 2011. The goal is to identify moments during the quarter when encouragement for passive students might be timely. We started with a social graph model of each forum archive. From those models we computed weighted out degrees, and page ranks for each student. The out degrees reflect the number of posts a student contributes. We performed change point analyses through bootstrap procedures over the CUSUM data of these post contributions through each quarter. We thereby identified significant week by week changes in the rate at which the top ten percent of forum contributors post messages. We hypothesize that such change points might be appropriate encouragement opportunities, and we find that sudden rate shifts do occur along regular patterns, primarily in science and engineering courses. We propose and demonstrate the use of control charts to monitor forum traffic, and show how the historic data can be used to provide personalized encouragement messages.

[1]  W. A. Taylor.  Change-Point Analysis : A Powerful New Tool For Detecting Changes , 2000 .

[2]  Anirban Dasgupta,et al.  Superposter behavior in MOOC forums , 2014, L@S.

[3]  Zhenming Liu,et al.  Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model , 2013, IEEE Transactions on Learning Technologies.

[4]  Muhammad Abulaish,et al.  Modeling a Web Forum Ecosystem into an Enriched Social Graph , 2012, MSM/MUSE.

[5]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[6]  S. Dawson,et al.  Harnessing ICT potential: The adoption and analysis of ICT systems for enhancing the student learning experience , 2010 .

[7]  Daniel A. McFarland,et al.  Encouraging Forum Participation in Online Courses with Collectivist, Individualist and Neutral Motivational Framings , 2014 .

[8]  References , 1971 .

[9]  Tanya Elias,et al.  Learning Analytics: Definitions, Processes and Potential , 2011 .

[10]  James J. Filliben,et al.  NIST/SEMATECH e-Handbook of Statistical Methods; Chapter 1: Exploratory Data Analysis , 2003 .

[11]  Carolyn Penstein Rosé,et al.  “ Turn on , Tune in , Drop out ” : Anticipating student dropouts in Massive Open Online Courses , 2013 .

[12]  Jure Leskovec,et al.  Engaging with massive online courses , 2014, WWW.

[13]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[14]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[15]  Justin Reich,et al.  Planning prompts increase and forecast course completion in massive open online courses , 2017, LAK.

[16]  Stefan Savage,et al.  An analysis of underground forums , 2011, IMC '11.

[17]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[18]  Carolyn Penstein Rosé,et al.  Peer Influence on Attrition in Massively Open Online Courses , 2014, EDM.

[19]  Adriana Wilde.  Understanding persuasive technologies to improve completion rates in MOOCs , 2016 .