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Project

Academic advising analytics dashboards: Towards artificial intelligence for student success

Academic advising is a growing professional practice with a developing scholarly field within Higher Education. Academic advising refers to “the intentional interactions between representatives from institutions of higher education (advisors) and students (advisees), and the ways by which advisors provide guidance and support for students on issues relating to the student's personal growth, academic studies, career, and future aspirations” (Chan et al., 2019, p. 59). It can be offered centralised (professional advisors in a central unit), decentralised (faculty-based advisors), or shared (a combination of professional and faculty advisors) (He & Hutson, 2016).

Analytics dashboards (also sometimes called learning analytics dashboards) are found within the domain of Learning Analytics which is about “collecting traces that learners leave behind and using those traces to improve learning” (Erik Duval in De Laet et al., 2020, p. 1004). Analytics dashboards in academic advising are about a visual representation of these educational traces “often supplemented with outcomes of educational data mining or predictive analytics” with the goal of identifying students at risk and also recently to “support advising dialogues” (De Laet et al., 2020, p. 1004). The scholarship in this field often comes from more technical disciplines like computer science or human-computer interaction as the development and understanding of the predictive machine learning algorithms that are often used to create academic advising dashboards situates the origin of study in those disciplines. The focus is usually on the evaluation of these systems from a user-design or technical perspective.

Towards is essential in this study as the inquiry into the use of academic advising analytics dashboards (AAAD) forms an organic part of a continuum of technology use in academic advising. The spectrum ranges from using more general communication technologies to using emerging artificial intelligence-informed predictive advising systems, or even autonomous EduBots (educational chatbots) that could potentially assist or even replace human advisors on the far end of the spectrum. The mixed methodology (QUAL + quan[1]) nature of the study creates a framework for studying the current as well as future academic advising technologies and the role they play in the academic advising process. “Towards” is used very carefully, and I want to point out from the outset that there is no inherent techno-utopianism in my thinking. It is rather an indication of an awareness of the broader pressures and technological developments and market-driven utopias around especially artificial intelligence (think e.g., the hype around the so-called Fourth Industrial Revolution (4IR) – see Fataar 2020) in almost all industries in general and academic advising within Higher Education in particular (how small and hidden it might seem).

Artificial intelligence, “theories and techniques developed to allow computer systems to perform tasks normally requiring human or biological intelligence” (Torres Torres et al., 2019, p. 5),  is used deliberately as it points towards the current (and almost certainly future) growth of academic advising systems. The systems, like AAADs, often rely on machine learning algorithms and have developed from producing descriptive analytics to providing predictive analytics. The bigger AI developments in the field of predictive analytics dashboards can also be seen in the experimental AAAD space, with a focus on explainable artificial intelligence (so-called XAI) systems to try and build more human trust in the AI systems’ predictions (Bodria et al., 2021), or even EduBots as (assistant) academic advisors that are built on recent developments in chatbots that use AI technology like natural language understanding models in the process of dialogue with humans (Khare et al., 2018).

For student success is important as it builds the whole study around the focal point of understanding what role academic advising, analytics dashboards, and emerging artificial intelligence systems play in improving the success of more (and hopefully potentially all) higher education students. The concept of student success is drawn from the idea of academic thriving, which thinks of student success as “the degree to which students obtain learning outcomes, pass grades, and manage to simultaneously balance the other life domains during their time in college, e.g., student employment, well-being, and health” (Dekker, 2022, p. 7-8). This focus returns our inquiry to the higher education space.

 

Date:8 Dec 2021 →  Today
Keywords:EduBots, Higher Education, Artificial intelligence, Knowledge building
Disciplines:Science, technology and engineering curriculum and pedagogics
Project type:PhD project