At a Glance
Researchers at Colorado State University have developed U-Behavior™ – an application and process built to engage students in understanding their cognition and how to engage in learning. Other learning systems force students through a prescribed curriculum, with little awareness as to why they are doing it. The U-Behavior™ system has been designed by educators and learning scientists with the intention of forming healthy habits and shaping students into highly skilled learners.
Even the most basic learning analytics (LA) accessible to instructors and administrators through learning management systems (LMS) offer both students and faculty the opportunity to better understand student interactions and intentions. Log data can provide information to instructors, institutions, and students, allowing them to make data-driven decisions regarding best practices in course design and improved student success (Dietz-Uhler & Hurn, 2013). In learning environments that rely on self-regulation, such as online classes or online class activities, “making effective choices and adaptation of learning strategies in response to the emerging needs from the learning environment are critical features of effective self-regulated learning” (Gasevic et al., 2017).
Learning analytics, specifically those presented in the visual-form, can provide information that supports learners’ reflection and guides them to the necessary changes that lead to successful self-regulated learning. Albeit learning analytics from the data within learning management systems can assist with teaching and learning practices, often this data is difficult to interpret and implement.
U-Behavior™ was created and designed to extract students attempt data on the retrieval practice activities, which were presented to students as opportunities to study the course content rather than as evaluations of understanding. Upon completion of the retrieval practice activities, learners were presented with their personalized learning analytics in visual-form and prompted to reflect on their learning. Visual-form learning analytics create opportunities for feedback and critical reflection for both instructors and learners and improve student learning. Analysis of the visual-form learning analytics and corresponding reflections highlighted learners’ understanding of high-impact learning practices, the realization of intended study behaviors versus engrained behaviors, high score orientation, and a focus on comparisons.
U-Behavior™ is a software application and method for using LMS quizzes to increase desirable (empirically supported) learning behaviors within classrooms. The software collects data from an LMS (e.g. Canvas), extracts and analyzes that data, and generates an individual graph of how a student used the quizzes over a period of time (e.g. semester). A key component of U-Behavior™ is the process of implementing a certain type of quizzing into the class and the teaching and instruction that goes along with the use of the software application. This process has been designed to inform students and to have them actively change their learning behaviors. Unlike other teaching/learning systems U-Behavior™ affords each student free choice and uses persuasive design to help them to make an informed choice within their existing course and emphasizes the long-term effects of this type of learning.
- Engages students in understanding their cognition and how to engage learning
- Studies showed learners had a deeper understanding of high-impact learning practices
- Can be used by instructors as a tool for feedback
- Provides learners a powerful tool for critical reflection
- Contributes to overall improved learning strategies (for instructors and learners)
- System is designed by educators and learning scientists
- Learning Management Systems
- Multi-University Consortium
Harindranathan, P. & Folkestad, J. (2019). Learning Analytics to Inform the Learning Design: Supporting Instructor’s Inquiry into Student Learning in Unsupervised Technology-Enhanced Platforms. Online Learning Journal, 23(3), 34-55.
McKenna, K., Moraes, M., & Folkestad, J. (2019, March). Reflections of visual-form learning analytics spaced retrieval practice activity. Learning Analytics and Knowledge (LAK) Conference. *Nominated for best practitioner paper.
Last updated: August 2022