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- All Subjects: Online Education
- Creators: Bitter, Gary
Description
Customized online education is a means of educating a large amount of users in a way that will change their behavior at a low incremental cost to the one providing the information. This thesis will examine several aspects of online education, but primarily focus on the presentation of the materials. It will examine how this is done through a consulting project I worked on in conjunction with the New Venture Group for Parenting Arizona. Parenting Arizona is a non-profit organization based in Arizona that offers classes for parents who are seeking better ways to manage their family responsibilities. The purpose of the consulting project was to take the instructional materials used in in-person group classes and modify it to be effectively used for instruction in an online environment. Parenting Arizona foresaw a number of benefits from this modification and migration of instructional materials for the web; first among these was the ability of people in remote areas or in situations that did not allow them to attend an on-ground class to gain access to instructional material. In addition, the broader availability of the material that would come from its presence on the web would expand the influence of good parenting instructions to a greater audience both inside and outside the State of Arizona, aiding even more families.
ContributorsAnderson, Kyle (Author) / Brooks, Dan (Thesis director) / Forss, Brennan (Committee member) / Rosen, Julie (Committee member) / Barrett, The Honors College (Contributor)
Created2012-05
Description
While predicting completion in Massive Open Online Courses (MOOCs) has been an active area of research in recent years, predicting completion in self-paced MOOCS, the fastest growing segment of open online courses, has largely been ignored. Using learning analytics and educational data mining techniques, this study examined data generated by over 4,600 individuals working in a self-paced, open enrollment college algebra MOOC over a period of eight months.
Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed).
The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.
Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed).
The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.
ContributorsCunningham, James Allan (Author) / Bitter, Gary (Thesis advisor) / Barber, Rebecca (Committee member) / Douglas, Ian (Committee member) / Arizona State University (Publisher)
Created2017