The world is increasingly data driven. Innovations in data analytics and machine learning are shaping actions and decisions in various fields. Within higher education, there are growing calls to invest in learning analytics and related information technologies to help resolve persistent challenges (Pelletier, 2022; Swaak, 2022). For example, administrators hope that predictive analytics forecasting students' future grades and tools that can instantaneously map out individualized degree plans can improve retention and degree completion. Such tools have the potential to impact students, faculty, and staff in yet unknown ways. Data scientists, software engineers, and other researchers have examined technical, mathematical, and procedural issues in analyzing data. However, there has been less scholarly conversation around the application and implementation of these tools and methods in particular contexts like academic advising.
Advisers should be at the forefront of conversations about whether and how data should be collected, accessed, interpreted, used, and distributed in higher education. Broadly, scholars have argued that academic advisers offer a unique perspective that can help guide higher education leadership in matters of policy and practice (Steele and White, 2019). Advisers have firsthand knowledge of the everyday challenges that such tools are meant to address. However, information science scholar Kyle Jones has argued that when educational institutions introduce data analytics and data mining tools to advising, they run the risk of “contextual suppression,” subordinating advisers' values, norms, and goals (Jones, 2019). For instance, advisers may view academic success and advising relationships differently from administrators focused on improving the institution's status, financial standing, or reportable metrics.
Thus, academic advising scholarship on data analytics, machine learning, and related information technologies is needed. Advising scholarship can engage with these topics in at least two ways. First, scholars can explore ethical challenges or principles, including those related to “the location and interpretation of data; informed consent, privacy, and deidentification of data; and classification and management of data” (Slade & Prinsloo, 2013) and how these may relate to ethical principles in academic advising (Lowenstein, 2008). Scholarship might illuminate or enumerate ethical standards or develop steps towards resolving challenges. For instance, advisers serve diverse student populations and are concerned with educational equity (NACADA 2017). Advisers thus have a role in developing, interpreting, and using data and technological tools in ways that are mindful of diversity, equity, inclusion, and belonging.
Academic advisers are also concerned with promoting learning as a central ethical standard and professional concern (Lowenstein, 2008; NACADA, 2006). Thus, how technology and data can be used to support student learning is also of scholarly interest (Steele, 2018). For instance, reducing learning down to data points like GPA, graduation rates, or velocity may encourage the “token fallacy,” mistaking grades and other tokens of learning for learning itself (Lowenstein, 2021). Critical analyses may tease out how the token fallacy shapes the value and investment higher education makes in analytics or how it shapes the uses and interpretation of educational data in advising.
Second, as data analytics and machine learning are transforming work and labor across the knowledge economy, advising scholarship should be attuned to how these technologies may impact the work and labor of advising. New information technologies and sources of data may streamline advising processes or even replace core functions traditionally associated with advising. How would such changes shape the definition of advising as a unique field, practice, or profession? Moreover, developing, implementing, and using new tools may require advisers to take on new roles. What challenges exist in collaborating with data scientists and other relevant stakeholders, and how can advisers navigate them? What role should advisers play in helping students understand and interpret data?
This special issue of The Mentor begins to address some of these concerns. Venable et al. introduce the term panopticon advising to describe "a philosophy and approach to advising characterized by intensive surveillance, intrusive outreach, and pursuit of retention above all other goals." Their article links broader societal concerns around surveillance technologies to the rise of the completion agenda, a movement in which degree completion is valued above learning in higher education. In describing the growing trend of using technology and data systems to monitor degree progress, they signal a warning and pose the question of whether a focus on transactional relationships and the singular goal of retention is where we want the practice and profession of advising to continue developing into the future.
Williams et al. describe the development of and advisers' reactions to working with LIFT, a new predictive analytic tool that was developed at Penn State University. LIFT relies on institutional data and machine learning to predict students' future grades, with potential benefits such as providing additional insights to guide course selection and allowing advisers to proactively recommend learning support. Advisers participated in a pilot study during which they used the newly developed tool and met regularly with the study authors to provide feedback. Advisers in the focus group expressed skepticism and ethical concerns, such as whether insights would incentivize students to fixate on grades over learning and the extent to which predicted grades might reinforce stereotypes for marginalized students. More broadly, the pilot demonstrated that learning analytics require human beings to interpret output in responsible ways. However, the article also highlights how doing so may not always be easy or straightforward in practice, even when academic advisers have deep institutional knowledge, professional values, and expertise working with students.
Advisers have used information technologies, such as learning management systems and institutional databases, for many years. Many questions are relevant to both previous technologies as well as those that we have not yet imagined: Who should have access to this data? What can be done with it? Who is interpreting it and with what consequences? Still, the increasing use of data and technological sophistication that we are witnessing in higher education—from predictive analytics to chatbots—demand that these and related questions receive renewed attention and deeper engagement now. This special issue aims to bring attention to these issues in academic advising and spur even more conversation and scholarship on these topics.