Call for Papers: Special Issue

Call for Papers

Special Issue of The Mentor: Innovative Scholarship on Academic Advising

Data Analytics and Machine Learning in Academic Advising


Aims and Scope

The world is increasingly data driven. The amount of data that is being collected and analyzed is growing exponentially. 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.

The Mentor invites any submission focused on the relationship between academic advising and data analytics, machine learning, or related information technologies. Submissions may explore how these tools could improve academic advising or be potentially harmful. We especially welcome manuscripts analyzing:

  • Ethical challenges, 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).
  • Student learning as a particular outcome of interest (Steele, 2018). For instance, manuscripts may examine steps advisers can take to prevent predictive analytics from creating a self-fulfilling prophecy in student outcomes. As another example, 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.
  • Diversity, equity, inclusion, and belonging. For instance, how might advisers’ unique knowledge be used to help reduce algorithmic bias? How should data be used in everyday advising contexts to ensure equitable educational outcomes? What kinds of analytic tools can be developed to improve diversity, equity, inclusion, and belonging for either students or advising staff?
  • 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. For instance, 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?

Submissions may include critical commentary/reflections, case studies, policy analyses, and empirical research articles. The Mentor accepts manuscripts of any length and encourages experimental forms of scholarship and non-traditional styles of writing or presenting ideas.

Notes for Prospective Authors

Prospective authors may email Dr. Junhow Wei, Editor, at with questions or to receive initial feedback on proposals. Please submit proposals by December 15, 2022. Proposals are optional but encouraged and should be less than two pages in length.

Deadline for submission of full manuscripts is May 1, 2023.

Please follow the journal’s Author Guidelines, submit your paper via the journal website (, and indicate in your cover letter that the submission is intended for this special issue. Submitted papers should present original work relevant to one or more of the topics of the special issue and should not have been submitted or published elsewhere.

All submitted papers will be peer reviewed and evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation. To facilitate the peer review process, you may be asked to peer review one or two other authors’ submissions. Detailed information on the peer review process will be provided.


Jones, K. M. L. (2019). Advising the whole student: eAdvising analytics and the contextual suppression of advisor values. Education and Information Technologies, 24, 437–458.

Lowenstein, M. (2008). Ethical foundations of academic advising. In V. A. Gordon, W. R. Habley, & T. J. Grites (Eds.), Academic advising: A comprehensive handbook (2nd ed. pp. 36–49). Jossey-Bass.

Lowenstein, M. (2021). Learning and its tokens: A fallacy and its danger for advising. The Mentor: Innovative Scholarship on Academic Advising, 23, 40–56.

Pelletier, K. (2022). The changing relationship between advising and technology. New Directions for Higher Education, 2021, 35–50.

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.

Steele, G. (2018). Student success: Academic advising, student learning data, and technology. New Directions for Higher Education, 2018, 59–68.

Steele, G., & White, E. R. (2019). Leadership in higher education: Insights from academic advisers. The Mentor: Innovative Scholarship on Academic Advising, 21, 1–10.

Swaak, T. (2022). The puzzle of student data: Key strategies for using critical information, responsibly. (Free Report). The Chronicle of Higher Education.