%0 Conference Paper %1 Pardos_2019 %A Pardos, Zachary A. %A Chau, Hung %A Zhao, Haocheng %B Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale %D 2019 %I ACM %K LAS2019 course-articulation course-recommender %R 10.1145/3330430.3333622 %T Data-Assistive Course-to-Course Articulation Using Machine Translation %U https://doi.org/10.1145%2F3330430.3333622 %X 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.

@inproceedings{Pardos_2019, abstract = {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.}, added-at = {2022-03-01T03:57:55.000+0100}, author = {Pardos, Zachary A. and Chau, Hung and Zhao, Haocheng}, biburl = {https://www.bibsonomy.org/bibtex/2a97d2c7b0e793482c911a59d3018ca15/brusilovsky}, booktitle = {Proceedings of the Sixth (2019) {ACM} Conference on Learning @ Scale}, description = {Data-Assistive Course-to-Course Articulation Using Machine Translation | Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale}, doi = {10.1145/3330430.3333622}, interhash = {13ca1bb71fd7e1c12f56f790ff1c82b1}, intrahash = {a97d2c7b0e793482c911a59d3018ca15}, keywords = {LAS2019 course-articulation course-recommender}, month = jun, publisher = {{ACM}}, timestamp = {2022-03-01T03:57:55.000+0100}, title = {Data-Assistive Course-to-Course Articulation Using Machine Translation}, url = {https://doi.org/10.1145%2F3330430.3333622}, year = 2019 }