Higher education at scale, such as in the California public post-secondary system, has promoted upward socioeconomic mobility by supporting student transfer from 2-year community colleges to 4-year degree granting universities. Among the barriers to transfer is earning enough credit at 2-year institutions that qualify for the transfer credit required by 4-year degree programs. Defining which course at one institution will count as credit for an equivalent course at another institution is called course articulation, and it is an intractable task when attempting to manually articulate every set of courses at every institution with one another. In this paper, we present a methodology towards making tractable this process of defining and maintaining articulations by leveraging the information contained within historic enrollment patterns and course catalog descriptions. We provide a proof-of-concept analysis using data from a 4-year and 2-year institution to predict articulation pairs between them, produced from machine translation models and validated by a set of 65 institutionally pre-established course-to-course articulations. Finally, we create a report of proposed articulations for consumption by the institutions and close with a discussion of limitations and the challenges to adoption.
Higher education is a source of skill acquisition for many middle- and high-skilled jobs. But what specific skills do universities impart on students to prepare them for desirable careers? In this study, we analyze a large novel corpora of over one million syllabi from over eight hundred bachelors’ granting US educational institutions to connect material taught in higher education to the detailed work activities in the US economy as reported by the US Department of Labor. First, we show how differences in taught skills both within and between college majors correspond to earnings differences of recent graduates. Further, we use the co-occurrence of taught skills across all of academia to predict the skills that will be taught in a major moving forward. Our unified information system connecting workplace skills to the skills taught during higher education can improve the workforce development of high-skilled workers, inform educational programs of future trends, and enable employers to quantify the skills of potential workers.
With the increased popularity of electronic textbooks, there is a growing interests in developing a new generation of "intelligent textbooks", which have the ability to guide the readers according to their learning goals and current knowledge. The intelligent textbooks extend regular textbooks by integrating machine-manipulatable knowledge such as a knowledge map or a prerequisite-outcome relationship between sections, among which, the most popular integrated knowledge is a list of unique knowledge concepts associated with each section. With the help of these concept, multiple intelligent operations, such as content linking, content recommendation or student modeling, can be performed. However, annotating a reliable set of concepts to a textbook section is a challenge. Automatic unsupervised methods for extracting key-phrases as the concepts are known to have insufficient accuracy. Manual annotation by experts is considered as a preferred approach and can be used to produce both the target outcome and the labeled data for training supervised models. However, most researchers in education domain still consider the concept annotation process as an ad-hoc activity rather than an engineering task, resulting in low-quality annotated data. In this paper, we present a textbook knowledge engineering method to obtain reliable concept annotations. The approach has been applied to produce annotated concepts for Introduction to Information Retrieval textbook. As shown by the data we collected, the inter-annotator agreement gradually increased along with our procedure, and the concept annotations we produced led to better results in document linking and student modeling tasks. The outcomes of our work include a validated knowledge engineering procedure, a code-book for technical concept annotation, and a set of concept annotations for the target textbook, which could be used as gold standard in further research.
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