ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Educational technology
- Creators: Bitter, Gary
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.
The study consisted of four conditions: (a) an attitudinal and demographic pre-survey, (b) five mobile instructional modules, (c) mobile quizzes, and (d) an attitudinal post-survey. A total of 311 participants in higher education were enrolled in the study. One hundred thirty-seven participants completed all four conditions of the study. Participants were randomly assigned to experimental conditions in a 2 x 2 factorial design. The levels of the first factor, distribution of instructional content, were: once-per-day and once-per-week. The levels of the second factor, testing, were: a quiz after each module plus a comprehensive quiz and a single comprehensive quiz after all instruction. The dependent variable was learning outcomes in the form of quiz-score results. Attitudinal survey results were analyzed using Principal Axis Factoring to reveal three components, (a) student perceptions about the use of mobile devices in education,
(b) student perceptions about instructors’ beliefs for mobile devices for learning, and (c) student perceptions about the use of mobile devices post-instruction.
The results revealed several findings. There was no significant effect for type of delivery of instruction in a one-way ANOVA. There was a significant effect for testing in a one-way ANOVA There were no main effects of delivery and testing in a 2 x 2 factorial design and there was no main interaction effect, and there was a significant effect of testing on final quiz scores controlling for technical beliefs in a 2 x 2 ANCOVA. The significant difference in testing was contradictory to some literature.
Ownership of personal mobile devices in persons aged 18–29 is practically all-inclusive. Thus, future research on student attitudes and the implementation of personal smartphones for microlearning and testing is still needed to develop and integrate mobile-ready content for higher education.