short-paper

Real-time indicators and targeted supports: using online platform data to accelerate student learning

Authors:

Hiroyuki Yamada,

Christopher ThornAuthors Info & Claims

LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge

Pages 183 - 187

https://doi.org/10.1145/2883851.2883942

Published: 25 April 2016 Publication History

Abstract

Statway® is one of the Community College Pathways initiatives designed to promote students' success in their developmental math sequence and reduce the time required to earn college credit. A recent causal analysis confirmed that Statway dramatically increased students' success rates in half the time across two different cohorts. These impressive results were also obtained across gender and race/ethnicity groups. However, there is still room for improvement. Students who did not succeed in Statway often did not complete the first of the two-course sequence. Therefore, the objective of this study is to formulate a series of indicators from self-report and online learning system data, alerting instructors to students' progress during the first weeks of the first course in the Statway sequence.

References

[1]

Bailey, T., Jeong, D. W., & Cho, S.-W. (2010). Referral, enrollment, and completion in developmental education sequences in community colleges. Economics of Education Review, 29, 255--270.

[2]

Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010). Female teachers' math anxiety affects girls' math achievement. Proceedings of the National Academy of Sciences, USA, 107, 1060--1063.

[3]

Blackwell, L. S., Trzesniewski, K. H. & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246--263.

[4]

Boaler, J. (1998). Open and closed mathematics: Student experiences and understandings. Journal for Research in Mathematics Education, 29, 41--62.

[5]

Bryk, A. S., Gomez, L. M., & Grunow, A. (2011). Getting ideas into action: Building networked improvement communities in education. In M. T. Hallinan (Ed.), Frontiers in sociology of education (pp. 127--162). New York, NY: Springer.

[6]

Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How America's schools can get better at getting better. Cambridge, MA: Harvard Education Press.

[7]

Cohen, G. L., Garcia, J., Purdie-Vaughns, V., Apfel, N., & Brzustoski, P. (2009). Recursive processes in self-affirmation: Intervening to close the minority achievement gap. Science, 324, 400--403.

[8]

Cohen, G. L., Steele, C. M., & Ross, L. D. (1999). The mentor's dilemma: Providing critical feedback across the racial divide. Personality and Social Psychology Bulletin, 25, 1302--1318.

[9]

Dolle, J. R., Gomez, L. M., Russell, J. L., & Bryk, A. S. (2013). "More than a network: building communities for educational improvement." In B. J. Fishman, W. R. Penuel, A. R. Allen, & B. H. Cheng (Eds.), Design-based implementation research: Theories, methods, and exemplars. National Society for the Study of Education Yearbook. New York, NY: Teachers College Record.

[10]

Dweck, C. S. (2006). Mindset. New York, NY: Random House.

[11]

Haynes, T. L., Perry, R. P., Stupnisky, R. H., & Daniels, L. M. (2009). A review of attributional retraining treatments: Fostering engagement and persistence in vulnerable college students. In Smart, J. C. (Ed.), Higher education: Handbook of theory and research (pp. 227--272). New York, NY: Springe

[12]

Hong, G., & Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children's cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis, 27, 205--224.

[13]

Krumm, A. E., D'Angelo, C., Podkul, T. E., Feng, M., Yamada, H., Beattie, R., Hough, H., & Thorn, C. (2015, 14-15 March). Practical measures of learning behaviors. Paper presented at the ACM Conference on Learning @ Scale (L@S'15), Vancouver, BC, Canada.

Digital Library

[14]

Krumm, A. E., Waddington, R. J., Teasley, S. D., & Lonn, S. (2014). Using learning analytics to support academic advising in undergraduate engineering education. In J. A. Larusson & B. White (Eds.). Learning Analytics: From Research to Practice (pp. 103--119). New York: Springer.

[15]

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.

[16]

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41--55.

[17]

Silva, E., & White, T. (2013). Pathways to improvement: Using psychological strategies to help college students master developmental math. Carnegie Foundation for the Advancement of Teaching. Stanford, CA.

[18]

Sowers, N., & Yamada, H. (2015). Pathways Impact Report. Stanford, CA: Carnegie Foundation for the Advancement of Teaching.

[19]

Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African-Americans. Journal of Personality and Social Psychology, 69, 797--811.

[20]

Strother, S., & Sowers, N. (2014). Community College Pathways: Summative Assessments and Student Learning. Stanford, CA: Carnegie Foundation for the Advancement of Teaching.

[21]

U.S. Department of Education. (2013). Expanding evidence approaches for learning in a digital world. Washington, DC: Author. www.ed.gov/edblogs/technology/files/2013/02/Expanding-Evidence-Approaches. pdf

[22]

Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92, 82--96.

[23]

Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes among minority students. Science, 331, 1447--1451.

[24]

Walton, G. M., & Spencer, S. J. (2009). Latent ability: Grades and test scores systematically underestimate the intellectual ability of negatively stereotyped students. Psychological Science, 20, 1132--1139.

[25]

Wright, B. D., & Masters, G. N. (1982). Rating scale analysis: Rasch measurement. Chicago: MESA Press.

[26]

Yamada, H. (2014). Community College Pathways' Program Success: Assessing the First Two Years' Effectiveness of Statway®. Stanford, CA: Carnegie Foundation for the Advancement of Teaching.

[27]

Yeager, D., Bryk, A., Muhich, J., Hausman H., & Morales, L. (2013) Practical measurement. Carnegie Foundation for the Advancement of Teaching. Stanford, CA.

Cited By

Ahmad ASchneider JGriffiths DBiedermann DSchiffner DGreller WDrachsler H(2022)Connecting the dots – A literature review on learning analytics indicators from a learning design perspectiveJournal of Computer Assisted Learning10.1111/jcal.1271640:6(2432-2470)Online publication date: 26-Jul-2022
https://doi.org/10.1111/jcal.12716
Francis MWormington SHulleman C(2019)The Costs of Online Learning: Examining Differences in Motivation and Academic Outcomes in Online and Face-to-Face Community College Developmental Mathematics CoursesFrontiers in Psychology10.3389/fpsyg.2019.0205410Online publication date: 10-Sep-2019
https://doi.org/10.3389/fpsyg.2019.02054
Ifenthaler DMah DYau J(2019)Utilising Learning Analytics for Study Success: Reflections on Current Empirical FindingsUtilizing Learning Analytics to Support Study Success10.1007/978-3-319-64792-0_2(27-36)Online publication date: 18-Jan-2019
https://doi.org/10.1007/978-3-319-64792-0_2

Index Terms

Real-time indicators and targeted supports: using online platform data to accelerate student learning
1. Applied computing
  1. Education
    1. Computer-assisted instruction
    2. Learning management systems

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cover image ACM Other conferences

LAK '16: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge

April 2016

567 pages

ISBN:9781450341905

DOI:10.1145/2883851

General Chairs:
Dragan Gašević
University of Edinburgh, United Kingdom
,
Grace Lynch
Society for Learning Analytics Research
,
Program Chairs:
Shane Dawson
University of South Australia
,
Hendrik Drachsler
University of the Netherlands
,
Carolyn Penstein Rosé
Carnegie Mellon University

Copyright © 2016 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2016

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LAK '16

LAK '16: 6th International Conference on Learning Analytics and Knowledge

April 25 - 29, 2016

Edinburgh, United Kingdom

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LAK '16 Paper Acceptance Rate 36 of 116 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Ahmad ASchneider JGriffiths DBiedermann DSchiffner DGreller WDrachsler H(2022)Connecting the dots – A literature review on learning analytics indicators from a learning design perspectiveJournal of Computer Assisted Learning10.1111/jcal.1271640:6(2432-2470)Online publication date: 26-Jul-2022
https://doi.org/10.1111/jcal.12716
Francis MWormington SHulleman C(2019)The Costs of Online Learning: Examining Differences in Motivation and Academic Outcomes in Online and Face-to-Face Community College Developmental Mathematics CoursesFrontiers in Psychology10.3389/fpsyg.2019.0205410Online publication date: 10-Sep-2019
https://doi.org/10.3389/fpsyg.2019.02054
Ifenthaler DMah DYau J(2019)Utilising Learning Analytics for Study Success: Reflections on Current Empirical FindingsUtilizing Learning Analytics to Support Study Success10.1007/978-3-319-64792-0_2(27-36)Online publication date: 18-Jan-2019
https://doi.org/10.1007/978-3-319-64792-0_2

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