Zack Underwood, Daron Williams, Momiji Barlow, Hannah Libovicz, and Olivia Wolz, Virginia Tech
As the field of academic advising evolves, it is an appropriate time to consider the future of the field. Prognostication is a tricky scenario similar to forecasting the weather, but many fields attempt to predict trends for the future. Utilizing student opinions, instructional design perspectives, current emerging trend lists, and academic advising theory, this article attempts to bring attention to four trends that can steer or influence the field as a whole in the next five to ten years.
Identifying Emerging Trends of the Field
Academic advising is still relatively young. With significant changes occurring in technology, discussions, and student populations, emerging trends inform the advising community. Establishing trends can also establish the field of academic advising as a whole. McGill (2019) points out vital literature of advising starting in 1972 with Crookston and O’Banion. With such a young field, it is difficult to describe the field and occupation of advising to college stakeholders and individuals outside of the field (McGill, 2019). Creating popular trends of the field provide context for current and future practices.
Advising manuscripts and chapters of books attempt to predict future trends for advising practice (Sims, 2013), the advising field as a whole (Lowenstein, 2013), and the profession (McGill & Nutt, 2016). Journals and websites such as EDUCAUSE attempt to predict overarching trends in higher education and technology or teaching and learning (EDUCAUSE, 2021, 2022). EDUCAUSE is an organization more closely related to instructional design and these trend lists are an anticipated event in the instructional design realm. The first trend for expanding advising as a whole is for institutional groups, types of institutions, and organizations to create yearly emerging trends for the field of academic advising. This manuscript could stand as a short example of emerging trends. Identifying and discussing these trends could lead to campus-wide conversations or institutional comparisons towards establishing the modern advising experience.
Accepting Artificial Intelligence (AI) and Machine Learning
Advisors may read the title to this section and cringe; however students are excited for this trend. Artificial intelligence “leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind” (IBM Cloud Education, 2020, para. 1). “Machine learning is an application of artificial intelligence that helps machines learn rules themselves instead of only operating according to pre-programmed rules” (Saravanan, 2022). Connecting with students is a monumental task requiring advising infrastructure. Infrastructure includes coordinating faculty, staff, students, technology, and (most importantly) time. The task of advising can be difficult to manage along with students’ desire for instant gratification and immediate answers. AI and machine learning can solve these dilemmas.
“AI could identify opportunities for advisors to make personal connections with students, such as behaviors that may signal student attrition” (Varney & Dumeng, 2019, para. 6). Artificial intelligence and machine learning tools such as predictive analytics are becoming commonplace in institutions. At the University of South Florida, administrators were concerned AI would “tell them what to do. They quickly learned that predictive analytics would do no such thing” (Miller & Irvin, 2019, para. 4). Advisors worked hand in hand with artificial intelligence to improve outreach to students who were at-risk.
From a developmental viewpoint, AI sounds like the worst-case scenario. Robots giving prescriptive advice and pushing students towards graduation sounds like a bad science-fiction movie plot. However, chatbots are being used to convince and persuade low-income students to apply to college. A chatbot named Oli “can ask questions and communicate in ways that are great for students who don’t feel comfortable talking in person . . . and then tailor the content around that” (Whitmire, 2020, para. 26). If this technology exists in an educational plane for high schoolers, then advising is the next step. Students may also have experience with chatbots within other contexts such as the medical or business fields. From a prescriptive viewpoint, AI such as chat bots can give advisors more time, while also offering students a personalized answer in the here and now. In a time when students want answers instantly, technology meets student needs. Advising can occur twenty-four hours a day, thanks to AI and machine learning.
Personalizing Relationships and Development
Advising strives towards personalization to prevent students from feeling insignificant, like just another number. When creating the outline for this manuscript, this was the highest priority for student authors. Student authors ranged from sophomore year to senior year and all emphasized the need for personalizing the student experience. To veer away from the idea of being a number, current students stressed the importance of relationships and how relationships promote diversity, equity, and inclusion.
To achieve success, Felten and Lambert (2020) believe every student should be welcomed for a sense of belonging, be motivated to learn, and create a network of meaningful relationships. Students get the option of which professor they get to take, yet they are given academic advisors without open opportunities for input. A mathematics major might not engage best with an advisor who specialized in the field of forestry. That being said, if students were allowed to handpick their advisor, their options could be limited to a pool of faculty or professional advising staff members who specialize in their field of education, know the major best, and/or have successful experiences with students who have graduated from the same program. At many institutions, students are assigned to faculty or staff advisors based on caseloads (alphabetical, numerical, or by class) as opposed to interest. A single relationship with a faculty or staff member could alter the student experience. Offering the opportunity of choice beyond just being assigned an individual could create an immersive, personalized affiliation with an institution.
Colleges could even consider a combination of individuals towards success teams, including but not limited to advisors, faculty, student mentors, campus partners such as Cultural Centers, Recovery Communities, Mental Health professionals, AI, and more. This provides a team approach for students and gives opportunities for network growth. Students will feel less like they are being forced to fit a mold, but rather like their advisor is molding their higher education experience to best fit them. Just because a student brings in transfer credit and classifies as a junior by hours does not mean they are at the maturity level to solely work with one member of the institution as their advisor. As the saying goes, it takes a village to grow and, in this instance, succeed at an institution.
Connecting Technology for Student Success
Institutions strive to improve student success by adopting new technologies. As more technologies are adopted, confusion occurs due to the many roles of users ranging from administrators, faculty, students, and varying permissions at institutions. In the context of academic advising, collaborative efforts are key to keep everyone on the same page.
Syracuse’s Director for Retention, Kalpana Srivnivas states, “higher education has been collecting data on students for decades, especially via student information systems, most of that data has not been used to its full potential” (Grush, 2018, para. 10). Platforms such as enterprise systems or even common degree tracking software can alter student success for the positive. “Technology can support educational reform as a foundation for college advisers and staff to change how they interact with and support students, ultimately improving the student experience” (Miller et al., 2020). Disconnected software or non-collaborative efforts lead to frustration.
As a Director for Advising, creating a story of a student’s experiences is a vital step towards identifying student success. If systems are decentralized or set in silos, then creating a singular history of a student’s experiences with campus partners such as Career Services, Tutoring, Counseling, Dean of Students, and others is tough. Fragmentation of data or data held by only certain individuals leads to incomplete stories and less informed academic advisors.
Beyond just giving access to software for data-driven decisions, collaborations for technology include keeping everyone up to date on training. Empowering students and advisors to use new or upcoming software removes the anxiety of the unknown. This could also be described as just-in-time (JIT) training or simplifying the process for the end user. JIT training is a common term in the instructional design field. Technology is a powerful tool, but it is the people who are using the advising technologies who matter.
Next Steps
Adoption and diffusion of these four trends can position academic advising to help future students thrive. AI and machine learning can help the process run smoother for students and decrease the burden on advisors, but it cannot replace—only aid—the personalized advising process that many students need. Relationships and personalization are key elements for success while technology end-user experiences can shape the way training needs to occur for new technologies and keep advisors informed for data-driven decisions.
Zack Underwood Director of University Studies & Scholarship Support Virginia Tech [email protected]
Daron Williams Director of Instructional Design Virginia Tech [email protected]
Momiji Barlow Student Virginia Tech [email protected]
Hannah Libovicz Student Virginia Tech [email protected]
Olivia Wolz Student Virginia Tech [email protected]
References
EDUCAUSE. (2021). 2021 EDUCAUSE horizon report: Teaching and learning edition. https://library.educause.edu/resources/2021/4/2021-educause-horizon-report-teaching-and-learning-edition
EDUCAUSE. (2022). 2022 top 10 IT issues: The higher education we deserve. https://www.educause.edu/research-and-publications/research/top-10-it-issues-technologies-and-trends/2022
Felten, P., & Lambert, L. (2020). Relationship-rich education: How human connections drive success in college. John Hopkins University Press.
Grush, M. (2018, August). Data analytics and student advising: Creating a culture shift on campus: A q&a with Kalpana (Kal) Srinivas. Campus Technology. https://campustechnology.com/Articles/2018/08/13/Data-Analytics-and-Student-Advising-Creating-a-Culture-Shift-on-Campus.aspx?Page=1
IBM Cloud Education (2020, June 3). Artificial intelligence. IBM Cloud Learn Hub. https://www.ibm.com/cloud/learn/what-is-artificial-intelligence
Lowenstein, M. (2013). Envisioning the future. In J. Drake, P. Jordan, & M. Miller (Eds.), Academic advising approaches: Strategies that teach students to make the most of college (pp. 243–258). NACADA.
McGill, C. (2019). The professionalization of academic advising: A structured literature review. NACADA Journal, 39(1), 89–100. https://doi.org/10.12930/NACADA-18-015
McGill, C., & Nutt, C. (2016). Challenges for the future: Developing as a profession, field, and discipline. In T. Grites, M. Miller, & J. Voller (Eds.), Beyond foundations: Developing as a master academic advisor (pp. 351–362). NACADA.
Miller, C., Cohen, B., Yang, E., & Pellegrino. L. (2020, December). Using technology to redesign college advising and student support: Findings and lessons from three colleges’ efforts to build on the iPass initiative. MDRC, Community College Resource Center. https://files.eric.ed.gov/fulltext/ED610065.pdf
Miller, T., & Irvin, M. (2019, December 9). Using artificial intelligence with human intelligence for student success. EDUCAUSE Review. https://er.educause.edu/articles/2019/12/using-artificial-intelligence-with-human-intelligence-for-student-success
Saravanan, R. (2022). Machine learning can change the way institutions operate. Ellucian. https://www.ellucian.com/blog/machine-learning-can-change-way-institutions-operate
Sims, A. (2013, March). Academic advising for the 21st century: Using principles of conflict resolution to promote student success and build relationships. Academic Advising Today, 36(1). https://nacada.ksu.edu/Resources/Academic-Advising-Today/View-Articles/Academic-Advising-for-the-21st-Century-Using-Principles-of-Conflict-Resolution-to-Promote-Student-Success-and-Build-Relationships.aspx
Varney, J., & Dumeng, C. (2019, August). Can a machine imitate an academic adviser? The impact of artificial intelligence on higher education. The Evolllution. https://evolllution.com/attracting-students/retention/can-a-machine-imitate-an-academic-adviser-the-impact-of-artificial-intelligence-on-higher-education/#:~:text=AI%20may%20also%20help%20advisors,into%20a%20system%20of%20nudging
Whitmire, R. (2020, September). How the common app, the college advising corps, and an AI chatbot are saving the college dreams of low-income students during the pandemic. The 74. https://www.the74million.org/article/how-the-common-app-the-college-advising-corps-and-an-ai-chatbot-are-saving-the-college-dreams-of-low-income-students-during-the-pandemic/
Cite this article using APA style as: Underwood, Z., Williams, D., Barlow, M., Libovicz, H., & Wolz, O. (2022, September). Trendsetting in academic advising. Academic Advising Today, 45(3). [insert url here]
