Data-informed practices can positively influence student success. This presentation will provide participants with insight into how adaptive technology, learning analytics, and intentional student contact work together to meet each learner’s needs. Participants will use example data to advise and improve the learning experience of a theoretical non-traditional student.
Despite rising numbers in college enrollment and postsecondary degree completion in the U.S. since 2001 (National Center for Education Statistics (NCES), 2014), significant gaps still persist in college access and degree attainment for adults 25 and older, racial and ethnic minorities, first-generation college and low-income students and other groups underrepresented in higher education. The social and economic imperatives of our 21st century knowledge-based economy require that many more people earn a college degree or other quality postsecondary credential to ensure the individual’s well-being in terms of increased earnings and employment as well as the nation’s productivity, prosperity and global competitiveness. With declining numbers of traditional age college students, achievement of these goals must include adult learners.
Bay Path University, with support from a First in the World FIPSE grant from the Department of Education, implemented a data-driven model to promote student success and degree completion among its non-traditional online population. Research shows that online learning has a 10 – 20% higher attrition rate than courses taught face-to-face (Angelino, Williams & Natvig, 2007). Possible factors that contribute to increased attrition include challenges with time management (Calvin & Freeburg, 2010; Hannum, Irvin, Lei & Farmer, 2008), lack of feedback from, and interaction with, instructors and peers (Jordan, 2009), and an overall lack of virtual student support services (Dare, Zapata & Thomas, 2005). The data-driven model implemented by Bay Path, which is called SOUL (Social Online Universal Learning), seeks to directly address the issues of limited feedback and lack of student support by fostering supportive mentoring relationships for students with peers, advisors, and faculty; immediate, constructive feedback on performance; and clearly delineated pathways to graduation.
One component of the SOUL model is our adaptive learning environment KnowledgePath. Like other adaptive technologies, KnowledgePath caters to the individual learner’s needs and enables students to take ownership of their education. The system continually learns about students’ strengths and makes recommendations for improvement by customizing their learning path and presents them with different content depending on their learning style.
Our students are not the only one’s who learn from the system. With the vast amount of actionable data that gets tracked, from time spent on an assignment to assessment metrics, our academic and instructional design teams can collaborate to make informed decisions when it comes to everything from working with subject matter experts hired to curate learning material to mapping entire program curricula. Faculty have data dashboards that provide a snapshot of individual and class-wide performance, empowering them to tailor interventions and feedback at a level of scale and specificity not otherwise obtainable.
Academic advising interventions are also based on actionable data. Through a series of early alert reports, data is gathered from Jenzabar, PowerFAIDS (student information systems), Canvas (learning management system), and the Civitas’ Illume Dashboard (dashboard of powerful predictors of success), among other sources, allowing the advising team to be proactive and intentional in their outreach. Data points on student performance such as grades less than 70 or a missed assignment are critical to a student’s success in an accelerated, six-week course. When the advising team is alerted to issues of this nature on a consistent basis, they are able to reach out to the students to provide appropriate support.
Data collection on students begins prior to their matriculation and continues on post-graduation. Data provides valuable information to support the success of an online learner, and to allow the institution to be proactive. Attendees will learn how to use powerful predictors to identify at-risk students and identify the most appropriate intervention. Administrators, academic technology support staff, and faculty can come away from this presentation with models for implementing and using adaptive technologies into online classroom. Attendees will also have a set of approaches for leveraging different types of data in service of improving the student learning experience.
Together, the attendees of this workshop will be presented with a student profile and will follow the student lifecycle of a non-traditional, online learner. Using data provided by the presenters, the attendees will make decisions on appropriate interventions and outreach. They will get to experience how Bay Path University leverages data to promote student success. We will also share some of the triumphs and tribulations we have experienced through the process of transitioning to a fully data-driven model. In addition to making informed intervention decisions, the attendees will also be presented with data from the KnowledgePath platform and will be asked to consider this data and make suggestions for potential course redesign.
This experience will allow participants to interact with different data sets and explore ways to make sense of the data and use it to support their practices. After their exploration, the presenters will share their own practices used with the attendees and will continue the discussion.
References
Angelino, L., Williams, F., Natvig, D. (2007). Strategies to Engage Online Students and Reduce
Attrition Rates. The Journal of Educators Online, 4(2).
Calvin, J., & Freeburg, B. W. (2010). Exploring adult learners' perceptions of technology
competence and retention in web-based courses. Quarterly Review of Distance Education, 11(2), 63-72.
Dare, L. A., and Zapata, L. and Thomas, A.G. (2005). Assessing the needs of distance learners: A student affairs perspective.
New Directions for Student Services, 112, 39-53.
Jordan, L. (2009). Transforming the Student Experience at a Distance: Designing for
collaborative online learning. Engineering Education, 4(2), 25-36.