In Fall 2014 over 460,000 students enrolled in the 23-campus California State University system; unfortunately, more than 20,000 qualified applicants were denied admission due to capacity and budgetary constraints. In response to continued overcrowding, the Chancellor's Office and Board of Trustees are investigating "bottlenecks," defined as anything limiting students' ability to graduate in a timely manner. Blended learning, a pedagogy combining face-to-face and computer-mediated instruction, presents a potential solution to alleviate overcrowding and bottleneck problems.
In an effort to investigate the extent to which student demographics and performance analytics explain student success outcomes in a popular blended learning psychology course, an explanatory sequential design was used to study 18,254 students enrolled in the course between 2006 and 2014. In the initial quantitative part of the design, logistic regression and traditional regression analysis were used to determine the predictors of those who chose to drop the course, those who ultimately passed the course, and then to investigate why some students received higher grades than others. Results revealed that race, gender, age, socioeconomic status, and early course participation were key predictors of success.
Some of the most significant findings – which included the fact that Mexican American, African American, and Filipino students were less successful in the course than their White counterparts – were examined in more detail in the qualitative part of the study that followed. Specifically, students who self-identified within these race/ethnicities provided a nuanced look at their own course experiences by completing questionnaires and interviews for the study. Thematic findings revealed socioeconomic status, time management, parents' education, and students' campus community as factors contributing to course performance.
This study represents one of few large-scale analyses of a blended learning environment focused upon learner outcomes, and it serves to inform the evaluative work surrounding student success interventions, including the ability to predict and understand student risk characteristics for dropping, failing, or performing poorly within a blended learning environment. Understanding the many reasons students engage in less successful behavior may inform student success strategies and alleviate bottlenecks, especially as the prevalence of blended learning courses increases within the California State University system.
|Advisor:||Galloway, Fred J.|
|Commitee:||Hubbard, Lea, Kemerer, Frank R., Laumakis, Mark|
|School:||University of San Diego|
|School Location:||United States -- California|
|Source:||DAI-A 76/11(E), Dissertation Abstracts International|
|Subjects:||Educational technology, Higher education|
|Keywords:||Blended learning, California State University, Learning analytics, San Diego State University, Student success|
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