Assist educators and leaders in securely collecting and analyzing student data.
In the November 2021 post Learning Analytics in Higher Education: What’s Working, I examine the role that student data can play in course design and instruction at the postsecondary level. Student data may include biographical and socio-economic information related to a student’s background (e.g. whether or not a student is first-generation, financial aid information, etc.), student behavior and participation in courses and campus life, and student performance in particular courses in the form of grades. Learning analytics typically use data collected through a learning management system (LMS), and they can prove helpful at the institutional level to improve student retention rates, at the instructor level as a way of efficiently identifying student needs in real time within a course, and at the student level when student feedback data, often via surveys, is used meaningfully in support of student success. Of course, educators are wise to bear in mind that any single data set is only part of a larger story. Learning analytics seem to be at their best to the extent that they are used in support of individual student growth and flourishing in all aspects of education. The more the use of learning analytics in higher education shifts focus away from a prediction emphasis and towards a dynamic understanding of students’ real-time learning experiences, the more we’re able to see authentic and substantive improvements in student outcomes.
As another example in support of this standard, the October, 2020 post Bias in Higher Ed Admissions: Is New Tech Helping or Hurting? explored ways in which technology tools–specifically AI and algorithms–are being used to interpret data and to assist with the college admissions process. Student data is used to influence decisions at all levels of education, not just pedagogical decisions in the classroom. In the 2019 “Varsity Blues” admissions scandal, 30 affluent parents, largely from the entertainment industry, were caught offering bribes to higher education personnel to influence admissions decisions for their children at elite California universities. A number of the parents were successful, and a number of higher education officials were complicit in the scandal. Thus, there is a question as to whether or not technology might be used in support of equitable admissions, ultimately helping to mitigate the wrongful influence of human bias in college admissions.
Finally, in a survey-based program evaluation completed in Winter of 2022, I evaluated the user comfort and training processes associated with the Slate Customer Relations Management (CRM) software at a higher education institution. The CRM is used for hosting, evaluating, and processing student applications, maintaining student records, sharing data with existing SPU systems (i.e. Banner), data management and reporting, event management, and organizing communications and outreach. The results of the survey and program evaluation suggested that staff and faculty users needed ongoing professional development activities with Slate use in mind, especially where student data collection, reporting, and analysis are concerned. With increased Slate fluency, higher education leaders and instructors will be better equipped to use data to inform big-picture decisions related to student admissions and retention.
