This research study explored how instructors can effectively implement learning analytic tools to support academically at-risk students with the purpose of improving learning outcomes. Using design and development research methods, an implementation model was developed and validated internally.
With the widespread use of learning analytics 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 learning analytics. The research problem is that there is a lack of evidence-based guidance on how instructors can effectively implement learning analytics to support academically at-risk students with the purpose of improving learning outcomes. The goal was to develop and validate a model to guide instructors in the implementation of learning analytics tools to support academically at-risk students with the purpose of improving learning outcomes. 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 and three themes falling under the category of caution including: skepticism, fear of overdependence, and question of usefulness. The model should enhance instructors’ use of learning analytics 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.
This topic was presented as a work-in-progress study at OLC Innovate 2016. This session will expand upon the information presented there and summarize the end product of this study which is an internally validated model to guide instructors in the use of learning analytic tools. This will be an excellent opportunity to present this research and elicit feedback from faculty on the usefulness and applicability of this model whose purpose is to enable them to more effectively implement learning analytics in the online classroom.