On meeting students where they are
Teacher judgment and the use of data in higher education
Bibliographic Data
| ID | 23213604 |
|---|---|
| Authors | Gina Schouten (0000-0002-7615-9320, Harvard University, corresponding author) |
| Year | 2017 |
| Volume | 15 |
| Issue | 3 |
| Pages | 321-338 |
| Publication date | 2017-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Theory and Research in Education (JOURNAL) |
| Journal identifiers | ISSN: 1477-8785 • E-ISSN: 1741-3192 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/1477878517734452 |
| OpenAlex | W2762823145 |
| Language | EN |
| Citations received | 4 |
| References cited | 10 |
It is treated as a truism that teaching well requires ‘meeting students where they are’. Data enable us to know better where that is. Data can improve instructional practice by informing predictions about which pedagogies will be most successful for which students, and it can improve advising practice by informing predictions about which students are likely to thrive on which pathways moving forward. But moral hazards lurk, and these have been highlighted especially in response to the burgeoning use of new data mining technologies to produce ‘big data’. This article explores the ethics of data use in higher education. I consider the ethics of aggregate data as a tool for meeting students where they are, comparing it to an ongoing debate about the use of statistics in the legal context. The comparison generates two important insights: First, even the most viable moral concerns about using statistical information in the educational context are not deal-breakers: Those concerns should lead us to exercise careful judgment in the use of statistical information but do not justify eschewing that information altogether. Second, surprisingly, those viable moral concerns show big data to have a moral advantage over traditional little data, suggesting that some of the resistance to the use of big data in education is either unfounded or at least needs to be balanced against the moral advantages big data offer.
Aggregate data · Big data · Context (archaeology) · Data mining · Data science · Defining Issues Test · Engineering ethics · Moral reasoning · Political science · Public relations · Sociology · Computer Science · Criminal Justice and Corrections Analysis · Engineering · Feminist Epistemology and Gender Studies · Political Philosophy and Ethics · Psychology · Social Psychology
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,8 |
| Citation span | 2021 - 2023 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |