Analytics that Inform the University: Using Data You Already Have

Dziuban, Charles D.; Moskal, Patsy; Cavanagh, Thomas B.; Watts, Andre

doi:10.24059/olj.v16i3.276

Cited by 17 publications

(18 citation statements)

References 4 publications

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“…The university administrators can draw from this information to alter admissions standards that can affect the likelihood of success in course enrollments, and in turn impact the graduation and retention rates (Dziuban et al, 2012). If the university chooses to grow enrollments, they would lower entrance requirements.…”

Section: Implications Of the Resultsmentioning

confidence: 99%

“…Data are used to describe the student body as a whole as well as subpopulations within the university. Administrators can identify admissions prospects and predict the likelihood of their success (Dziuban et al, 2012).…”

Section: Uses Of Datamentioning

confidence: 99%

“…Analytics is the science of logical data analysis (Dziuban, Moskal, Cavanagh & Watts, 2012). The use of analytics is popular in business to predict customer choices.…”

Section: Learning Analyticsmentioning

confidence: 99%

“…Learning analytics study results can be useful to instructional designers when creating online courses (Lockyer et al, 2013). Department level staff can use data to inform personnel decisions including teaching assignments and training needs (Berger & Lyon, 2005;Dziuban et al, 2012;Shelton et al, 2015). University staff that provides supplemental student resources benefits from learning analytics results to refine the timing and location of various services (Becker, 2013;.…”

Section: Uses Of Datamentioning

confidence: 99%

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Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

Berry¹

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This learning analytics study looked at the various student characteristics of all on-campus students who were enrolled in 100 and 200 level courses that were offered in both online and face-to-face formats during a two-year period. There is a perception that online education is either not as successful as face-to-face instruction, or it is more difficult for students. The results of this study show this is not the case.The goal of this study was to complete an in-depth analysis of student profiles addressing a variety of demographic categories as well as several academic and course related variables to reveal any patterns for student success in either online or face-to-face courses as measured by final grade. There were large enough differences within different demographic and academic categories to be considered significant for the study population, but overwhelmingly, the most significant predictor of success was found to be past educational success, as reflected in a student's cumulative grade point average.Further analysis was completed on students who declared high school credit as their primary major based on significantly different levels of success. These students were concurrent enrollment students or those who completed college courses for both high school and university credit. Since most of these students were new to the university, they did not have a cumulative GPA, so other predictive factors were explored. The study concludes with recommendations for action based on the logistic regression prediction tool that resulted from the data analysis.

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Section: Implications Of the Resultsmentioning

confidence: 99%

Section: Uses Of Datamentioning

confidence: 99%

“…Analytics is the science of logical data analysis (Dziuban, Moskal, Cavanagh & Watts, 2012). The use of analytics is popular in business to predict customer choices.…”

Section: Learning Analyticsmentioning

confidence: 99%

Section: Uses Of Datamentioning

confidence: 99%

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Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

Berry¹

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“…LA holds potential application for a range of stakeholders in higher education including instructors, researchers, curriculum developers, learning environment designers, and university policy makers. LA is utilized at many levels within academic institutions, but a common application is at the course level (Dziuban, Moskal, Cavanagh, & Watts, 2012). Data within the learning management system (LMS) regarding student activity can be tracked and analyzed to monitor student progress, predict student success or failure, or inform instructional design.…”

Section: Introduction Backgroundmentioning

confidence: 99%

An Instructor Learning Analytics Implementation Model

McKee

2017

OLJ

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Abstract:With the widespread use of learning analytics (LA) tools, there is a need to explore how these technologies can be used to enhance teaching and learning. Little research has been conducted on what human processes are necessary to facilitate meaningful adoption of LA. The research problem is that there is a lack of evidence-based guidance on how instructors can effectively implement LA to support students. The goal of the study was to develop and validate a model to guide instructors in the implementation of LA tools. Using design and development research methods, an implementation model was constructed and validated internally. Themes emerged falling into the categories of adoption and caution with six themes falling under adoption including:• LA as evidence,• reaching out, • frequency, • early identification/intervention, • self-reflection, and • align LA with pedagogical intent. Three themes emerged falling under the category of caution including:• skepticism, • fear of overdependence, and • question of usefulness. The model should enhance instructors' use of LA by enabling them to better take advantage of available technologies to support teaching and learning in online and blended learning environments. Researchers can further validate the model by studying its usability (i.e., usefulness, effectiveness, efficiency, and learnability), as well as, how instructors' use of this model to implement LA in their courses affects retention, persistence, and performance.

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Student Engagement in Online Learning: What Works and Why

Meyer¹

2014

ASHE Higher Education Report

201

107

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OverviewW ITH PRESSURES TO INCREASE ACCESS to higher education, colleges and universities have focused on increasing the number of online courses and programs offered. Higher education is also being criticized for its retention and graduation rates, and pressure is building to find solutions. To ensure that online learning can help address these problems, professionals dedicated to online learning are under pressure to explore and evaluate strategies for getting students engaged in their online studies. These pressures are the genesis and reason for this monograph.By applying the theories and techniques for student engagement in online learning, instructors and designers of online courses can improve and increase student engagement and help higher education produce graduates who can contribute to their families, communities, and the economy. The theories and research reviewed in this monograph provide important clues as to how to help students learn, stay enrolled, and finish a degree.Engagement techniques may be one key to making online learning productive for the institution but, more importantly, ensuring that students are successful as they pursue a college degree. In fact, achieving student engagement in online courses may be more important than it is in on-campus courses because online students have fewer ways to be engaged with the institution and perhaps greater demands on their time and attention as well. In other Student Engagement OnlineStudent Engagement Online

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Analytics that Inform the University: Using Data You Already Have

Cited by 17 publications

References 4 publications

Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

Using Learning Analytics to Predict Academic Success in Online and Face-to-Face Learning Environments

An Instructor Learning Analytics Implementation Model

Student Engagement in Online Learning: What Works and Why

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