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Developmental Education at SVCC


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A look at where Sauk is and where it could go with developmental education.

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Developmental Education at SVCC

  1. 1. Developmental Education at SVCC
  2. 2. The Developmental Education Landscape at SVCC • About 60% need remediation based on placement test results • Less than 50% complete developmental course on first try • About 50% of developmental students will drop out during their first year (Nunez, 2015)
  3. 3. New Research-Based Approaches • Multiple measures for placement • Pathways for gateway courses • Noncognitive assessments • Predictive modeling and machine learning • Intrusive, human advising
  4. 4. Multiple Measures College readiness determined by more than just placement test score • Applied many different ways, e.g. – “Conjunctive” – pass all measures – “Compensatory” – one can make up for another – “Complementary” – best one counts (Brockhart, 2009) • Students selected by MM as successful as those placed higher by placement tests (Ngo & Kwan, 2015)
  5. 5. Pathways Match gateway course with course of study, e.g. STEM with algebra, others with stats • Carnegie math pathways have been shown to be significantly more effective – 6% traditional vs. 54% Statway – 21% traditional vs. 63% Quantway (Huang, 2018) – Seek to also develop noncognitive skills such as tenacity and persistence (Silvia & White, 2013)
  6. 6. Noncognitive Factors All those “other” factors that impact student success • For example: – Mindset and learning strategies (Farrington et al., 2012) – Grit, tenacity, and perseverance (U.S. Department of Education, 2013) • Not only highly correlated, but practically beneficial for helping students succeed (Allen, Robbins, & Sawyer, 2010)
  7. 7. Predictive Modeling/ Machine Learning Machine learning can look for patterns we can’t see yet and project success • For example: – Analyze student’s past interactions to predict their future ones (Al-Sarem, 2015) – Analyze learning management system log files to determine study habits, develop virtual tutors (Arroyo & Woolf, 2005)
  8. 8. Human, Intrusive Advising Advisors reach out to students to keep them on track rather than waiting for them to seek help • AI is better than humans at quickly evaluating data, but can’t replace human factors (Braga & Logan, 2017) • Even if it will someday be possible, computers cannot demonstrate creativity (Boden, 2003) • Intrusive advising helps students be successful and more confident (Donaldson, McKinney, Lee, & Pino, 2016)
  9. 9. Where is Sauk Now? • State of IL pushing toward corequisite developmental education classes • Using multiple measures (SAT, placement), looking to expand • Developing intrusive advising model with College Student Inventory for noncognitive factors • Working with high schools and PASS to help students be better prepared
  10. 10. Where should Sauk Go? • Expand multiple measures to include more factors like high school GPA and noncognitive factors • Evaluate options for predictive data modeling • Remember the human element – Advisors use modeled data to assist the decisions, not automate them
  11. 11. References Adriana Braga, & Robert K. Logan. (2017). The emperor of strong AI has no clothes: Limits to artificial intelligence. Information, Vol 8, Iss 4, p 156 (2017), (4), 156. Allen, J., Robbins, S. B., & Sawyer, R. (2010). Can measuring psychosocial factors promote college success? Applied Measurement in Education, 23(1), 1–22. Al-Sarem, M. (2015). Predictive and statistical analyses for academic advisory support. ArXiv:1601.04244 [Cs]. Retrieved from Arroyo, I., & Woolf, B. P. (2005). Inferring learning and attitudes from a Bayesian Network of log file data. Frontiers in Artificial Intelligence and Applications, 125(Artificial intelligence in education), 33–40. Boden, M. A. (2003). The creative mind: Myths and mechanisms (2nd edition). London ; New York: Routledge. Brookhart, S. M. (2009). The many meanings of “multiple measures.” Educational Leadership, 67(3), 6–12. Donaldson, P., McKinney, L., Lee, M., & Pino, D. (2016). First-year community college students’ perceptions of and attitudes toward intrusive academic advising. NACADA Journal, 36(1), 30–42. Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., & Beechum, N. O. (2012). Teaching adolescents to become learners. The role of noncognitive factors in shaping school performance: A critical literature review. Chicago: University of Chicago Consortium on Chicago School Research. Retrieved from learners-role-noncognitive-factors-shaping-school Huang, M. (2018). 2016-2017 Impact report: Six years of results from the Carnegie math pathways. Retrieved from Ngo, F., & Kwon, W. W. (2015). Using multiple measures to make math placement decisions: Implications for access and success in community colleges. Research in Higher Education, 56(5), 442–470. Nunez, S. (2015, May 1). The use of academic data and demographic data from recently graduated high school students to predict acdemic success at Sauk Valley Community College (Thesis). Ferris State University. Retrieved from Silva, E., & White, T. (2013). Pathways to improvement: Using psychological strategies to help college students master developmental math. Retrieved from students-master-developmental-math/ U.S. Department of Education, Office of Educational Technology. (2013). Promoting grit, tenacity, and perseverance: Critical factors for success in the 21st century. Retrieved from for-success-in-the-21st-century/