Web Survey Bibliography
Title Engagement patterns of nontraditional students in the Questionnaire Design for Social Surveys Coursera MOOC
Author Samoilova, E.; Keusch, F.; Kreuter, F.
Year 2016
Access date 29.04.2016
Abstract
Relevance & Research Question:
While the popularity of Massive Open Online Courses (MOOCs) is increasing, relatively low completion rates are often mentioned as key points of criticism. There is a growing body of research, however, calling for the better understanding of the heterogeneity of the learners. Working professionals as a subpopulation of MOOC participants are of special interest due to their overproportionate MOOC enrollment, MOOC potential in professional training, and limited empirical evidence in the area. This study investigates online learning behaviors and additional characteristics of nontraditional students as well as how they differ from traditional full-time students by answering the two following questions:
- Given the heterogeneity of learners, what are the main patterns of interaction with MOOC components (videos, quizzes, and readings assignments for each week)?
- What are the differences in the distribution of survey demographics, intentions for enrolling, perceived learning outcomes, forum activity, and watching styles (including types of devices used for streaming) across the indicated typologies of the engagement?
The Questionnaire Design MOOC has been offered on Coursera since July 2014. Overall, 58.141 participants have enrolled in the course.
Methods & Data:
The project uses secondary data collected by Coursera including activity logs, clickstream data, text data, assessment results as well as available survey data. First, we replicate the k-means cluster analysis as applied in Kizilcec et al. (2013). ANOVA and log linear models are then used to compare the identified groups based on learner characteristics as well as more detailed information on learners’ interactions with course elements. The analyses are undertaken separately for traditional and non-traditional students. Due to the potential non-response bias in the course surveys, learners’ behavioral data are investigated as potential covariates for non-response adjustments.
Added Value:
Although a number of authors outlined the importance of MOOCs for professional work, to the best of our knowledge, there is no investigation of engagement of working professionals when compared to tradition learners and measured as a record of learning activities on the Web.
While the popularity of Massive Open Online Courses (MOOCs) is increasing, relatively low completion rates are often mentioned as key points of criticism. There is a growing body of research, however, calling for the better understanding of the heterogeneity of the learners. Working professionals as a subpopulation of MOOC participants are of special interest due to their overproportionate MOOC enrollment, MOOC potential in professional training, and limited empirical evidence in the area. This study investigates online learning behaviors and additional characteristics of nontraditional students as well as how they differ from traditional full-time students by answering the two following questions:
- Given the heterogeneity of learners, what are the main patterns of interaction with MOOC components (videos, quizzes, and readings assignments for each week)?
- What are the differences in the distribution of survey demographics, intentions for enrolling, perceived learning outcomes, forum activity, and watching styles (including types of devices used for streaming) across the indicated typologies of the engagement?
The Questionnaire Design MOOC has been offered on Coursera since July 2014. Overall, 58.141 participants have enrolled in the course.
Methods & Data:
The project uses secondary data collected by Coursera including activity logs, clickstream data, text data, assessment results as well as available survey data. First, we replicate the k-means cluster analysis as applied in Kizilcec et al. (2013). ANOVA and log linear models are then used to compare the identified groups based on learner characteristics as well as more detailed information on learners’ interactions with course elements. The analyses are undertaken separately for traditional and non-traditional students. Due to the potential non-response bias in the course surveys, learners’ behavioral data are investigated as potential covariates for non-response adjustments.
Added Value:
Although a number of authors outlined the importance of MOOCs for professional work, to the best of our knowledge, there is no investigation of engagement of working professionals when compared to tradition learners and measured as a record of learning activities on the Web.
Access/Direct link Conference Homepage (presentation)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - 2016 (264)
- Web Health Monitoring Survey: A New Approach to Enhance the Effectiveness of Telemedicine Systems; 2017; Romano, M. F.; Sardella, M. V.; Alboni, F.
- Socially Desirable Responding in Web-Based Questionnaires: A Meta-Analytic Review of the Candor Hypothesis...; 2016; Gnambs, T.; Kaspar, K.
- Dynamic Question Ordering in Online Surveys; 2016; Early, K.; Mankoff, J.; Fienberg, S. E.
- How to use online surveys to understand human behaviour concerning window opening in terms of building...; 2016; Fabbri, K.
- Impact of satisficing behavior in online surveys on consumer preference and welfare estimates; 2016; Gao, Z.; House, L. A.; Bi, X.
- Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers; 2016; Guillory, J.; Kim, A.; Murphy, J.; Bradfield, B.; Nonnemaker, J.; Hsieh, Y. P.
- Influence of Importance Statements and Box Size on Response Rate and Response Quality of Open-Ended...; 2016; Kumar Chaudhary, A.; Israel, G. D.
- Web based health surveys: Using a Two Step Heckman model to examine their potential for population health...; 2016; Morrissey, K.; Kinderman, P.; Pontin, E.; Tai, S.; Schwannauer, M.
- “Better do not touch” and other superstitions concerning melanoma: the cross-sectional web...; 2016; Gajda, M.; Kamiñska-Winciorek, G.; Wydmañski, J.; Tukiendorf, A.
- Methods for Evaluating Respondent Attrition in Web-Based Surveys; 2016; Hochheimer, C. J.; Sabo, R. T.; Krist, A. H.; Day, T.; Cyrus, J.; Woolf, S. H.
- The Low Response Score (LRS): A Metric to Locate, Predict, and Manage Hard-to-Survey Populations; 2016; Erdman, C.; Bates, N.
- Targeted Appeals for Participation in Letters to Panel Survey Members; 2016; Lynn, P.
- Can we assess representativeness of cross-national surveys using the education variable?; 2016; Ortmanns, V.; Schneider, S.
- Methodological Aspects of Central Left-Right Scale Placement in a Cross-national Perspective; 2016; Scholz, E.; Zuell, C.
- Fieldwork Effort, Response Rate, and the Distribution of Survey Outcomes: A Multilevel Meta-analysis; 2016; Sturgis, P.; Williams, Jo.; Brunton-Smith, I.; Moore, J.
- Mobile-only web survey respondents; 2016; Lugtig, P. J.; Toepoel, V.; Amin, A.
- Comparison of Face-to-Face and Web Surveys on the Topic of Homosexual Rights; 2016; Liu, M.; Wang, Yic.
- Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated...; 2016; Lee, S.; McClain, C.; Webster, N.; Han, S.
- Web-Based Statistical Sampling and Analysis; 2016; Quinn, A.; Larson, K.
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2016; 2016
- Using Visual Analogue Scales in eHealth: Non-Response Effects in a Lifestyle Intervention; 2016; Kuhlmann, T.; Reips, U.-D.; Wienert, J.; Lippke, S.
- Development and Pilot Test of a Mobile Application for Field Data Collection; 2016; Chiappetta, L.; Kerr, M. M.
- Statistical Design for Online Experiments Across Desktops, Tablets, Smartphones (and Maybe Wearable...; 2016; Qian, P.; Sadeghi, S.; Arora, N. K.
- A Case Study on the Use of Propensity Score Adjustments with Web Survey Data; 2016; Parsons, V.
- Motivated Misreporting in Web Panels; 2016; Bach, R.; Eckman, S.
- Are Initial Respondents Different from the Nonresponse Follow-Up Cases? A Study of Probability-Based...; 2016; Zeng, W.; Dennis, J. M.
- Using official surveys to reduce bias of estimates from nonrandom samples collected by web surveys; 2016; Beresovsky, V.; Dorfman, A.; Rumcheva, P.
- Predicting and Preventing Break-Offs in Web Surveys; 2016; Mittereder, F.
- A Feasibility Study of Recruiting and Maintaining a Web Panel of People with Disabilities; 2016; Chandler, J.
- Exploration of Methods for Blending Unconventional Samples with Traditional Probability Samples; 2016; Gellar, J.; Zhou, H.; D.; Sinclair, M. D.
- Ratio of Vector Lengths as an Indicator of Sample Representativeness ; 2016; Shin, H. C.
- Design of Sample Surveys That Complement Observational Data to Achieve Population Coverage; 2016; Slud, E.; Ashmead, R.
- Inferences from Internet Panel Studies and Comparisons with Probability Samples; 2016; Lachan, R.; Boyle, J.; Harding, R.
- Exploring the Gig Economy Using a Web-Based Survey: Measuring the Online 'and' Offline Side...; 2016; Robles, B. J.; McGee, M.
- Comparing data quality between online panel and intercept samples; 2016; Liu, M.
- Effect of a Pre-Paid Incentive on Response Rates to an Address-Based Sampling (ABS) Web-Mail Survey; 2016; Suzer-Gurtekin, Z.; Elkasabi, M.; Liu, Me.; Lepkowski, J. M.; Curtin, R.; McBee, R.
- Response Behavior in a Video-Web Survey: A Mode Comparison Study; 2016; Haan, M.; Ongena, Y. P.; Vannieuwenhuyze, J. T. A.; de Glopper, K.
- Standard Definitions Final Dispositions of Case Codes and Outcome Rates for Surveys; 2016
- Integration of a phone-based household travel survey and a web-based student travel survey; 2016; Verreault, H.; Morency, C.
- Evaluation of mode equivalence of the MSKCC Bowel Function Instrument, LASA Quality of Life, and Subjective...; 2016; Bennett, A. V.; Keenoy, K.; Shouery, M.; Basch, E.; Temple, L. K.
- Making use of Internet interactivity to propose a dynamic presentation of web questionnaires; 2016; Revilla, M.; Ochoa, C.; Turbina, A.
- A streamlined approach to online linguistic surveys; 2016; Erlewine, M. Y.; Kotek, H.
- Du kommst hier nicht rein: Türsteherfragen identifizieren nachlässige Teilnehmer in Online-Umfragen; 2016; Merkle, B.; Kaczmirek, L.; Hellwig, O.
- Incorporating eye tracking into cognitive interviewing to pretest survey questions; 2016; Neuert, C.; Lenzner, T.
- Population Survey Features and Response Rates: A Randomized Experiment; 2016; Guo, Y.; Kopec, J.; Cibere, J.; Li, L. C.; Goldsmith, C. H.
- Mode Effect and Response Rate Issues in Mixed-Mode Survey Research: Implications for Recreational Fisheries...; 2016; Wallen, K. E.; Landon, A. C.; Kyle, G. T.; Schuett, M. A.; Leitz, J.; Kurzawski, K.
- A measure of survey mode differences; 2016; Homola, J.; Jackson, N. M.; Gill, Je.
- Web Health Monitoring Survey: A New Approach to Enhance the Effectiveness of Telemedicine Systems ; 2016; Romano, M. F.; Sardella, M. V.; Alboni, F.
- Smartphones vs PCs: Does the Device Affect the Web Survey Experience and the Measurement Error for...; 2016; Toninelli, D.; Revilla, M.
- Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated...; 2016; Lee, S.; McClain, C.; Webster, N.; Han, S.