Applying a Responsible Innovation Framework in Developing an Equitable Early Alert System:

A Case Study

Authors

DOI:

https://doi.org/10.18608/jla.2023.7795

Keywords:

responsible innovation, retention, early alert systems, equity, students as partners, research paper

Abstract

The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and implementation of data analytics in higher education. In a case study of creating an early-alert retention system at James Madison University, a working group of ~20 faculty, staff, and students creatively utilized the AIRR framework. The present study discusses how the AIRR framework was utilized to observe and enhance group processes, and the outcomes of those enhanced processes.

References

Beck, U. (2000). Risk society revisited: Theory, politics, and research programmes. In B. Adam., U. Beck, & J. Van Loon (Eds.), The risk society and beyond: Critical issues for social theory (pp. 211–229). Sage Publications. https://doi.org/10.4135/9781446219539.n12

Bensimon, E. M. (2005). Closing the achievement gap in higher education: An organizational learning perspective. New Directions for Higher Education, 131, 99–111. https://doi.org/10.1002/he.190

Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: Learner choice and fundamental rights. Learning, Media and Technology, 45(3), 312–324. https://doi.org/10.1080/17439884.2020.1786399

Bird, K. A., Castleman, B. L., Mabel, Z., & Song, Y. (2021). Bringing transparency to predictive analytics: A systematic comparison of predictive modeling methods in higher education. AERA Open, 7. https://doi.org/10.1177/23328584211037630

Broughan, C., & Prinsloo, P. (2020). (Re)centring students in learning analytics: In conversation with Paulo Freire. Assessment & Evaluation in Higher Education, 45(4), 617–628. https://doi.org/10.1080/02602938.2019.1679716

Brown McNair, T., Bensimon, E. M., & Malcom-Piqueux, L. (2020). From equity talk to equity walk: Expanding practitioner knowledge for racial justice in higher education. John Wiley & Sons. https://doi.org/10.1002/9781119428725.fmatter

Brown Wright, G. (2011). Student-centered learning in higher education. International Journal of Teaching and Learning in Higher Education, 23(3), 92–97. https://files.eric.ed.gov/fulltext/EJ938583.pdf

Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1

Carnevale, A. P., Rose, S. J., & Cheah, B. (2013). The college payoff: Education, occupations, lifetime earnings. Georgetown University Center on Education and the Workforce. https://repository.library.georgetown.edu/bitstream/handle/10822/559300/collegepayoff-complete.pdf

Casadevall, S. R. (2016). Improving the management of water multi-functionality through stakeholder involvement in decision-making processes. Utilities Policy, 43A, 71–81. https://doi.org/10.1016/j.jup.2016.04.015

Creswell, J. W. (2014). A concise introduction to mixed methods research. SAGE.

Crosling, G., Heagney, M., & Thomas, L. (2009). Improving student retention in higher education. Australian Universities’ Review, 51(2), 9–18. https://files.eric.ed.gov/fulltext/EJ864028.pdf

Culver, K. C., Harper, J., & Kezar, A. (2021). Design for equity in higher education. University of Southern California, Pullias Center for Higher Education. https://files.eric.ed.gov/fulltext/ED615816.pdf

Foster, E., & Siddle, R. (2020). The effectiveness of learning analytics for identifying at-risk students in higher education. Assessment & Evaluation in Higher Education, 45(6), 842–854. https://doi.org/10.1080/02602938.2019.1682118

Francis, P., Broughan, C., Foster, C., & Wilson, C. (2020). Thinking critically about learning analytics, student outcomes, and equity of attainment. Assessment & Evaluation in Higher Education, 45(6), 811–821. https://doi.org/10.1080/02602938.2019.1691975

Fulcher, K. H., & Prendergast, C. O. (2021). Improving student learning at scale: A how-to guide for higher education. Stylus Publishing.

Fulcher, K. H., Good., M. R., Coleman, C. M., & Smith, K. L. (2014, December). A simple model for learning improvement: Weigh pig, feed pig, weigh pig. (Occasional Paper No. 23). University of Illinois and Indiana University, National Institute for Learning Outcomes Assessment (NILOA). https://www.learningoutcomesassessment.org/wp-content/uploads/2019/02/OccasionalPaper23.pdf

Garcia, N. M., López, N., & Vélez, V. N. (2018). QuantCrit: Rectifying quantitative methods through critical race theory. Race Ethnicity and Education, 21(2), 149–157. https://doi.org/10.1080/13613324.2017.1377675

Garcia, P., Sutherland, T., Cifor, M., Chan, A. S., Klein, L., D’Ignazio, C., & Salehi, N. (2020). No: Critical refusal as feminist data practice. Companion of the 2020 ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW ’20) 17–21 October 2020, Virtual (pp. 199–202). ACM Press. https://doi.org/10.1145/3406865.3419014

Gregory, A. (2007). Involving stakeholders in developing corporate brands: The communication dimension. Journal of Marketing Management, 23(1–2), 59–73. https://doi.org/10.1362/026725707X178558

Jayaprakash, S. M., Moody, E. W., Lauría, E. J., Regan, J. R., & Baron, J. D. (2014). Early alert of academically at-risk students: An open source analytics initiative. Journal of Learning Analytics, 1(1), 6–47. https://doi.org/10.18608/jla.2014.11.3

Jiménez, D., & Glater, J. D. (2020). Student debt is a civil rights issue: The case for debt relief and higher education reform. Harvard Civil Rights–Civil Liberties Law Review, 55(1), 131–198. https://harvardcrcl.org/wp-content/uploads/sites/10/2020/09/Jimenez-Glater.pdf

Jones, K. M. L. (2019). Learning analytics and higher education: A proposed model for establishing informed consent mechanisms to promote student privacy and autonomy. International Journal of Educational Technology in Higher Education, 16, 24. https://doi.org/10.1186/s41239-019-0155-0

Kalsbeek, D. H. (2013). Framing retention for institutional improvement: A 4 Ps framework. New Directions for Higher Education, 2013(161), 5–14. https://doi.org/10.1002/he.20041

Kezar, A. (2019). Creating a diverse student success infrastructure: The key to catalyzing cultural change for today’s student. University of Southern California, Pullias Center for Higher Education. https://pullias.usc.edu/download/creating-a-diverse-student-success-infrastructure-the-key-to-catalyzing-cultural-change-for-todays-student/

Lang, C., Teasley, S., & Stamper, J. (2017). Building the learning analytics curriculum: Workshop. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 520–521). ACM Press. https://doi.org/10.1145/3027385.3029439

Montenegro, E., & Jankowski, N. A. (2020, January). A new decade for assessment: Embedding equity into assessment praxis (Occasional Paper No. 42). University of Illinois and Indiana University, National Institute for Learning Outcomes Assessment (NILOA). https://www.learningoutcomesassessment.org/wp-content/uploads/2020/01/A-New-Decade-for-Assessment.pdf

National Center for Education Statistics. (2022, May). Undergraduate retention and graduation rates. https://nces.ed.gov/programs/coe/indicator/ctr#:~:text=Fall%20Enrollment%20component.-,See%20Digest%20of%20Education%20Statistics%202021%2C%20table%20326.30.,fall%202019%20was%2061%20percent

Prinsloo, P., & Slade, S. (2014). Student data privacy and institutional accountability in an age of surveillance. In M. R. Menon, D. G. Terkla, & P. Gibbs (Eds.), Using data to improve higher education: Research, policy, and practice (pp. 197–214). SensePublishers Rotterdam. https://doi.org/10.1007/978-94-6209-794-0_12

Prinsloo, P., & Slade, S. (2018). Mapping responsible learning analytics: A critical proposal. In B. H. Kahn, J. R. Corbeil, & M. E. Corbeil (Eds.), Responsible analytics and data mining in education: Global perspectives on quality, support, and decision making (pp. 63–80). Routledge. https://doi.org/10.4324/9780203728703-5

Reidenberg, J. R., & Schaub, F. (2018). Achieving big data privacy in education. Theory and Research in Education, 16(3), 263–279. https://doi.org/10.1177%2F1477878518805308

Sclater, N., Peasgood, A., & Mullan, J. (2016, April). Learning analytics in higher education: A review of UK and international practice. Jisc. https://www.jisc.ac.uk/sites/default/files/learning-analytics-in-he-v3.pdf

Seidman, A. (2012). Taking action: A retention formula and model for student success. In A. Seidman (Ed.), College student retention: Formula for student success, 2nd ed. (pp. 267–284). Rowman & Littlefield Publishers.

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366

Spady, W. G. (1971). Dropouts from higher education: Toward an empirical model. Interchange, 2, 38–62. https://doi.org/10.1007/BF02282469

Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008

Tamborini, C. R., Kim, C., & Sakamoto, A. (2015). Education and lifetime earnings in the United States. Demography, 52(4), 1383–1407. https://doi.org/10.1007/s13524-015-0407-0

Tampke, D. R. (2013). Developing, implementing, and assessing an early alert system. Journal of College Student Retention: Research, Theory & Practice, 14(4), 523–532. https://doi.org/10.2190%2FCS.14.4.e

Tinto, V. (1987). Leaving college: Rethinking the causes and cures of student attrition. University of Chicago Press.

Tinto, V. (2006). Research and practice of student retention: What next? Journal of College Student Retention: Research, Theory & Practice, 8(1), 1–19. https://doi.org/10.2190%2F4YNU-4TMB-22DJ-AN4W

Watt, S. K., Mahatmya, D., Mohelabi, M, & Martin-Stanley II, C. R. (Eds.). (2022). The theory of being: Practices for transforming self and communities across difference. Stylus Publishing.

Westin, A. F. (1967). Legal safeguards to insure privacy in a computer society. Communications of the ACM, 10(9), 533–537. https://dl.acm.org/doi/pdf/10.1145/363566.363579

Yin, R. K. (2014). Case study research: Design and methods (5th ed.). SAGE.

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Published

2023-03-12

How to Cite

Patterson, C., York, E., Maxham, D., Molina, R., & Mabrey, P. (2023). Applying a Responsible Innovation Framework in Developing an Equitable Early Alert System:: A Case Study. Journal of Learning Analytics, 10(1), 24-36. https://doi.org/10.18608/jla.2023.7795

Issue

Section

Special Section on Fairness, Equity, and Responsibility in Learning Analytics