Improving Advising Using Technology and Data Analytics

Phillips, Elizabeth

doi:10.1080/00091383.2013.749151

Cited by 26 publications

(26 citation statements)

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“…The predictive tools are informed by students' informational profiles and professional interests, which are then compared with similar peers to determine students' compatibility with courses and programs (Kraft‐Terry & Kau, ). Other affordances of advising analytics take into account markers that determine whether or not a student is ready to pursue a university education and likely to be retained each semester (Phillips, ). The use of advising analytics has resulted in notable findings for two institutions in particular.…”

Section: Educational Data Mining and Learning Analyticsmentioning

confidence: 99%

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Jones

Rubel

LeClere

2019

Asso for Info Science & Tech

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Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.

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Section: Educational Data Mining and Learning Analyticsmentioning

confidence: 99%

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Jones

Rubel

LeClere

2019

Asso for Info Science & Tech

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“…Phillips (2013) provided an overview of the online advising system utilized by Arizona State University (ASU), explaining that advising was conducted more effectively and efficiently through this system. Specifically, relatively simple advising tasks (such as learning about course prerequisites) are now completed online by ASU students, allowing advisors to focus on advanced areas of advising, such as interpreting requirement information and facilitating decision making (Phillips, 2013). The ASU program effectively flips the advising environment in the same way that some instructors have flipped classroom education: Content is delivered online and processed in offline meetings (EDUCAUSE, 2012).…”

Section: Advising Communicationmentioning

confidence: 99%

Impact of Technology-Mediated Communication on Student Evaluations of Advising

et al. 2016

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The field of academic advising lags behind other similar fields in the use of technologically mediated communication modalities shown to improve academic outcomes. We investigated student satisfaction with undergraduate advising by examining the ways communication methods, such as social media, between student and advisor relate to student satisfaction. Results showed that although advisors rarely communicated with students via social media, text messaging, or instant messaging, the number of face-to-face advising meetings was positively predicted by advisor use of instant messaging and text messaging and negatively predicted by their use of Facebook. Furthermore, e-mail communication positively predicted a student's positive view of the advising experience, but communicating through Twitter predicted negative views of advising.

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“…For this reason, governments across the world have launched plans and roadmaps to support the development of big data in public and private entities aligned with the delivery of educational practices. According to Phillips (2013), the improvement of the technological infrastructures in education provides a framework for institutions in the sector to leverage on the concept of big data. Phillips goes on and argues that the primary determinant of the success of big data is the infrastructure laid out by learning and teaching institutions.…”

Section: Predictive Analytics In Educationmentioning

confidence: 99%

The Use of Predictive Analytics to Shift the Culture of Academic Advising Toward a Focus on Student Success

Bowdre¹

2020

JESP

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This paper explores the use of data through a predictive analytical approach to shift the current culture of academic advising from one of compliance to one that focuses on student learning and success in all areas of an educational experience.As educational institutions demonstrate an ever-increasing reliance on the use of data to make evidencebased decisions, the use of much of the same data can be used to develop an evidence-based profile of student performance. Advisors using analytics can predict future outcomes and proactively guide students to make betterdecisions regarding their selection of an academic major or coursework pathways, improving a student's likelihood of attaining a degree and consequent success in a given field. Due to the technological advances in the areas of educational data mining and the use of analytics, the capacity to guide students using rational, evidence-based information is now a possibility. This paper sets the stage for the use of predictive analytics that can influence a cultural shift in academic advising while offering caution that educational institutions must assess the quality of the data decisions that are being made.

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Improving Advising Using Technology and Data Analytics

Cited by 26 publications

References 0 publications

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Impact of Technology-Mediated Communication on Student Evaluations of Advising

The Use of Predictive Analytics to Shift the Culture of Academic Advising Toward a Focus on Student Success

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