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 (4086)
- Displaying Videos in Web Surveys: Implications for Complete Viewing and Survey Responses; 2017; Mendelson, J.; Lee Gibson, J.; Romano Bergstrom, J. C.
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- Ideal and maximum length for a web survey; 2017; Revilla, M.; Ochoa, C.
- Social desirability bias in self-reported well-being measures: evidence from an online survey; 2017; Caputo, A.
- Web-Based Survey Methodology; 2017; Wright, K. B.
- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
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- Web Survey Gamification - Increasing Data Quality in Web Surveys by Using Game Design Elements; 2017; Schacht, S.; Keusch, F.; Bergmann, N.; Morana, S.
- Effects of sampling procedure on data quality in a web survey; 2017; Rimac, I.; Ogresta, J.
- Comparability of web and telephone surveys for the measurement of subjective well-being; 2017; Sarracino, F.; Riillo, C. F. A.; Mikucka, M.
- Achieving Strong Privacy in Online Survey; 2017; Zhou, Yo.; Zhou, Yi.; Chen, S.; Wu, S. S.
- A Meta-Analysis of the Effects of Incentives on Response Rate in Online Survey Studies; 2017; Mohammad Asire, A.
- Telephone versus Online Survey Modes for Election Studies: Comparing Canadian Public Opinion and Vote...; 2017; Breton, C.; Cutler, F.; Lachance, S.; Mierke-Zatwarnicki, A.
- Examining Factors Impacting Online Survey Response Ratesin Educational Research: Perceptions of Graduate...; 2017; Saleh, A.; Bista, K.
- Usability Testing for Survey Research; 2017; Geisen, E.; Romano Bergstrom, J. C.
- Paradata as an aide to questionnaire design: Improving quality and reducing burden; 2017; Timm, E.; Stewart, J.; Sidney, I.
- Fieldwork monitoring and managing with time-related paradata; 2017; Vandenplas, C.
- Interviewer effects on onliner and offliner participation in the German Internet Panel; 2017; Herzing, J. M. E.; Blom, A. G.; Meuleman, B.
- Interviewer Gender and Survey Responses: The Effects of Humanizing Cues Variations; 2017; Jablonski, W.; Krzewinska, A.; Grzeszkiewicz-Radulska, K.
- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
- Where, When, How and with What Do Panel Interviews Take Place and Is the Quality of Answers Affected...; 2017; Niebruegge, S.
- Comparing the same Questionnaire between five Online Panels: A Study of the Effect of Recruitment Strategy...; 2017; Schnell, R.; Panreck, L.
- Nonresponses as context-sensitive response behaviour of participants in online-surveys and their relevance...; 2017; Wetzlehuetter, D.
- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- Measuring Subjective Health and Life Satisfaction with U.S. Hispanics; 2017; Lee, S.; Davis, R.
- Humanizing Cues in Internet Surveys: Investigating Respondent Cognitive Processes; 2017; Jablonski, W.; Grzeszkiewicz-Radulska, K.; Krzewinska, A.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
- The Effect of Respondent Commitment on Response Quality in Two Online Surveys; 2017; Cibelli Hibben, K.
- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
- Push2web or less is more? Experimental evidence from a mixed-mode population survey at the community...; 2017; Neumann, R.; Haeder, M.; Brust, O.; Dittrich, E.; von Hermanni, H.
- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- Redirected Inbound Call Sampling (RICS); A New Methodology ; 2017; Krotki, K.; Bobashev, G.; Levine, B.; Richards, S.
- An Empirical Process for Using Non-probability Survey for Inference; 2017; Tortora, R.; Iachan, R.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Rates, Delays, and Completeness of General Practitioners’ Responses to a Postal Versus Web-Based...; 2017; Sebo, P.; Maisonneuve, H.; Cerutti, B.; Pascal Fournier, J.; Haller, D. M.
- Necessary but Insufficient: Why Measurement Invariance Tests Need Online Probing as a Complementary...; 2017; Meitinger, K.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Is There a Future for Surveys; 2017; Miller, P. V.
- Reducing speeding in web surveys by providing immediate feedback; 2017; Conrad, F.; Tourangeau, R.; Couper, M. P.; Zhang, C.
- Social Desirability and Undesirability Effects on Survey Response latencies; 2017; Andersen, H.; Mayerl, J.
- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.