Integrating syllabus data into student success models. Gardner, J., Onuoha, O., & Brooks, C. In Proceedings of the Seventh International Conference on Learning Analytics and Knowledge, of LAK '17, pages 586–587, Vancouver, BC, March, 2017. Association for Computing Machinery. ![Integrating syllabus data into student success models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this work, we present (1) a methodology for collecting, evaluating, and utilizing human-annotated data about course syllabi in predictive models of student success, and (2) an empirical analysis of the predictiveness of such features as they relate to others in modeling end-of-course grades in traditional higher education courses. We present a two-stage approach to (1) that addresses several challenges unique to the annotation task, and address (2) using variable importance metrics from a series of exploratory models. We demonstrate that the process of supplementing traditional course data with human-annotated data can potentially improve predictive models with information not contained in university records, and highlight specific features that demonstrate these potential information gains.
@inproceedings{gardner_integrating_2017,
address = {Vancouver, BC},
series = {{LAK} '17},
title = {Integrating syllabus data into student success models},
isbn = {978-1-4503-4870-6},
url = {http://doi.org/10.1145/3027385.3029473},
doi = {10.1145/3027385.3029473},
abstract = {In this work, we present (1) a methodology for collecting, evaluating, and utilizing human-annotated data about course syllabi in predictive models of student success, and (2) an empirical analysis of the predictiveness of such features as they relate to others in modeling end-of-course grades in traditional higher education courses. We present a two-stage approach to (1) that addresses several challenges unique to the annotation task, and address (2) using variable importance metrics from a series of exploratory models. We demonstrate that the process of supplementing traditional course data with human-annotated data can potentially improve predictive models with information not contained in university records, and highlight specific features that demonstrate these potential information gains.},
urldate = {2020-09-23},
booktitle = {Proceedings of the {Seventh} {International} {Conference} on {Learning} {Analytics} and {Knowledge}},
publisher = {Association for Computing Machinery},
author = {Gardner, Josh and Onuoha, Ogechi and Brooks, Christopher},
month = mar,
year = {2017},
pages = {586--587}
}
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