Each year, the Financial Times ranks international MBA programs. This year the top 10 are Harvard, Stanford, INSEAD (France/Singapore), U Penn – Wharton, London Business School, CEIBS (China), U Chicago – Booth, MIT – Sloan, Columbia, IESE (Spain), Yale, and Northwestern – Kellogg.
School ranking methods are often controversial: schools, educational goals, and concepts of quality vary considerably and the data and inputs into the models are often subjective. (Here’s Malcolm Gladwell on an older version of the US News & World Report ranking methodology). The FT mitigates these problems by focusing on a set of more narrowly comparable institutions (top tier MBA programs), on relatively unambiguous inputs (it heavily weights student outcomes measured in salary and program value in cost), and by limiting scoring to a top 10 (instead of elevating the minor differences that separate schools in the long tail). FT includes a “research rank” derived from the number of articles that faculty published in top journals, but salary factors outweigh research factors by 4-1. And it lightly weights a host of other variables — gender ratios, international diversity, faculty PhDs, and so on — that contribute to the quality of a business education.
In collaboration with the FT, we’ve been exploring whether Open Syllabus data can capture a different dimension of school quality: the degree to which a business school’s faculty influence the teaching of business in general, throughout the field. Our method is simple: we took the 500 most frequently assigned texts from the
The Galaxy has received a massive upgrade in scale and functionality. The previous version mapped 164,000 titles and could display 30,000 at a time. The new version maps 1.1 million titles and can display 500,000 at a time. The resolution of fields and subfields is vastly improved as a result.
The Galaxy also implements a much-requested ‘search by topic’ function, which searches against the full text of syllabi rather than titles and authors–though you can still do that too. Results are now heat mapped to help users zoom in on areas of interest. David McClure has written up a detailed technical post on the new Galaxy for those who want a look under the hood.
OER Metrics is a new subsite for investigating trends and adoption patterns for openly-licensed books and textbooks (i.e., Open Educational Resources). It provides the first tools for mapping the demand side of the OER ecosystem and–we hope–can help inform adoption decisions by instructors and programs and investment decisions by authors, publishers, and funders.
Link Lab is an exploration of ‘non-traditional’ teaching materials in the collection identified by URLs in the syllabi. These links are then walked back to their source to collect titles, authors, and other metadata. Link Lab picks up newspaper and magazine stories, videos and documentaries, blog posts, and other materials that are frequently taught but rarely recognized or curated as
Open Syllabus has focused mostly on tracking books in the curriculum. But of course people teach with all sorts of materials–newspaper and magazine articles, YouTube videos and PBS documentaries, blogs and reference sites, software tools and resources. What do those choices look like?
We’ve begun to explore this question in the Link Lab. ‘Link’ refers to the underlying resource: we look for URLs in the syllabus collection and trace them back to their sources. ‘Lab’ refer to the fact that we’re still working out the process for collecting and organizing this data, which is different than our approach to books. Eventually this will be folded into the main OS dataset. In the meantime, it’s a fun way to explore the role of media outlets and journalists in the curriculum.
Today we’re excited to release a big update to the Galaxy visualization, an interactive UMAP plot of graph embeddings of books and articles assigned in the Open Syllabus corpus! (This is using the new v2.5 release of the underlying dataset, which also comes out today.) The Galaxy is an attempt to give a 10,000-meter view of the “co-assignment” patterns in the OS data – basically, which books and articles are assigned together in the same courses. By training node embeddings on the citation graph formed from (syllabus, book/article) edges, we can get really high-quality representations of books and articles that capture the ways in which professional instructors use them in the classroom – the types of courses they’re assigned in, the other books they’re paired with, etc.
The new version is a pretty big upgrade from before, both in terms of the size of the slice of the underlying citation graph that we’re operating on, and the capabilities of the front-end plot viewer. The plot now contains the 1,138,841 most frequently-assigned books and articles in the dataset (up from 160k before) and shows 500,000 points on the screen at once (up from 30k before).
Under the hood, this is a pretty straightforward transformation of the raw citation graph that comes out of the OS data pipeline. The citation extractor identifies references to books and articles in the syllabus, which can take a few different forms – lists of required books, week-by-week reading assignments, bibliographies, etc. Eg, from “Statistical
Olga Togarczuk won the Nobel Prize in Literature in 2018. She appears on 22 syllabi in the OS dataset. Peter Handke won in 2019 and appears on 221. Louise Glück, who won this past September, appears on 91. These are low numbers (even assuming, in Glück’s case, that we structurally undercount poetry, which we probably do). None of these authors are widely taught. Curious, I spent some time exploring the place of Nobel Prize-winners in the curriculum. The results are pretty striking. Here are the past forty Literature winners.
I’ve struggled somewhat to make generalizations here. There is clearly a lot variation in how often the prize winners are taught, ranging from the ubiquitous Toni Morrison to a whole raft of writers who are almost never assigned. The notions of literary reputation and value that animate the Nobel committee appear to have little connection to the judgements that faculty make in assigning texts. Nor–by all appearances–is winning a prize a guarantee of more teaching attention.
For Nobel watchers, this probably isn’t a surprise. Generations of commentators have written about the byzantine politics of literary reputation and influence that shape the prize, about its varieties of regional and gender bias and more recent politics of outreach, and about the diverse uses of the prize for social and political commentary. There are endless arguments about the uneven judgement of the committee, focused mostly on the literary giants left unrecognized and the winners who were (and remain) obscure. (For an entertaining, polemical