short-paper

Exploring college major choice and middle school student behavior, affect and learning: what happens to students who game the system?

Authors:

Maria O. Z. San Pedro,

Neil T. Heffernan,

Jaclyn L. OcumpaughAuthors Info & Claims

LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge

Pages 36 - 40

https://doi.org/10.1145/2723576.2723610

Published: 16 March 2015 Publication History

Abstract

Choosing a college major is a major life decision. Interests stemming from students' ability and self-efficacy contribute to eventual college major choice. In this paper, we consider the role played by student learning, affect and engagement during middle school, using data from an educational software system used as part of regular schooling. We use predictive analytics to leverage automated assessments of student learning and engagement, investigating which of these factors are related to a chosen college major. For example, we already know that students who game the system in middle school mathematics are less likely to major in science or technology, but what majors are they more likely to select? Using data from 356 college students who used the ASSISTments system during their middle school years, we find significant differences in student knowledge, performance, and off-task and gaming behaviors between students who eventually choose different college majors.

References

[1]

Baker, R., Corbett, A., and Aleven, V. 2008. More Accurate Student Modeling through Contextual Estimation of Slip & Guess Probabilities in Bayesian Knowledge Tracing. In Proc. ITS 2010, 406--415.

Digital Library

[2]

Baker, R. S. J.d., Corbett, A. T., Koedinger, K. R., Evenson, S. E., Roll, I., Wagner, A. Z., Naim, M., Raspat, J., Baker, D. J., and Beck, J. 2006. Adapting to When Students Game an Intelligent Tutoring System. In Proceedings ITS 2006, 392--401.

Digital Library

[3]

Benjamini, Y. and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 289--300.

[4]

Cocea, M., Hershkovitz, A., and Baker, R. S. 2009. The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate? In Proceedings of AIED 2009, 507--514.

Digital Library

[5]

Corbett, A. T. and Anderson, J. R. 1995. Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 4, 253--278.

[6]

Holland, J. L. 1997. Making vocational choices: A theory of vocational personalities and work environments. Psychological Assessment Resources.

[7]

Lent, R. W., Brown, S. D., and Hackett, G. 1994. Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79--122.

[8]

National Middle School Association research summary #12: Academic achievement. 2002. Retrieved January 14, 2003, from www.nmsa.org/research/ressum12.htm.

[9]

Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., and Heffernan, C. 2014. Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45, 3, 487--501.

[10]

Pardos, Z. A., Baker, R. S., San Pedro, M. O. C. Z., Gowda, S. M., and Gowda, S. M. 2013. Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. In Proceedings LAK 2013, 117--124.

Digital Library

[11]

Porter, S. R., and Umbach, P. D. 2006. College major choice: An analysis of person--environment fit. Research in Higher Education, 47, 4, 429--449.

[12]

Razzaq, L., Feng, M., Nuzzo-Jones, G., Heffernan, N. T., Koedinger, K. R., Junker, B., Ritter, S., Knight, A., Aniszczyk, C., Choksey, S., Livak, T., Mercado, E., Turner, T. E., Upalekar. R, Walonoski, J. A., Macasek. M. A. and Rasmussen, K. P. 2005. The Assistment project: Blending assessment and assisting. In Proc. AIED 2005, 555--562.

Digital Library

[13]

Régis, C. 2004. Physicians gaming the system: modern-day Robin Hood. Health Law Review, 13, 1, 19--24.

[14]

San Pedro, M. O. Z., Baker, R. S. J.d., Bowers, A. J., and Heffernan, N. T. 2013. Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School. In Proceedings of the 6th International Conference on Educational Data Mining, 177--184.

[15]

San Pedro, M. O. Z., Ocumpaugh, J. L., Baker, R. S., Heffernan, N. T. 2014. Predicting STEM and Non-STEM College Major Enrollment from Middle School Interaction with Mathematics Educational Software. In Proceedings EDM 2014, 276--279.

[16]

The College Board. 2014. College Majors and Career Search. Retrieved from https://bigfuture.collegeboard.org.

[17]

Wang, X. 2013. Why Students Choose STEM Majors Motivation, High School Learning, and Postsecondary Context of Support. American Educational Research Journal, 50, 5, 1081--1121.

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
Andres JHutt SOcumpaugh JBaker RNasiar NPorter C(2022)How Anxiety Affects Affect: A Quantitative Ethnographic Investigation Using Affect Detectors and Data-Targeted InterviewsAdvances in Quantitative Ethnography10.1007/978-3-030-93859-8_18(268-283)Online publication date: 11-Jan-2022
https://doi.org/10.1007/978-3-030-93859-8_18
Ocumpaugh JBaker RSan Pedro MHawn MHeffernan CHeffernan NSlater SWise AWinne PLynch GOchoa XMolenaar IDawson SHatala M(2017)Guidance counselor reports of the ASSISTments college prediction model (ACPM)Proceedings of the Seventh International Learning Analytics & Knowledge Conference10.1145/3027385.3027435(479-488)Online publication date: 13-Mar-2017
https://dl.acm.org/doi/10.1145/3027385.3027435
Show More Cited By

Recommendations

The Evaluation of Schools to College and Career (S2C) Readiness Program for Middle School Student
Middle school students using Alice: what can we learn from logging data?
SIGCSE '13: Proceeding of the 44th ACM technical symposium on Computer science education

There is growing interest in how we can use computer logging data to improve computational tools and pedagogies to engage children in complex thinking and self-expression, but our techniques lag far behind our theories. Only recently have learning ...
Constructing knowledge networks in middle school classrooms: an integrated approach
FIE '01: Proceedings of the Frontiers in Education Conference, 2001. 31st Annual - Volume 02

Providing opportunities for K-12 educators to further their use and understanding of current technologies in the classroom has never been more important than it is at present. This paper explores ways that university faculty members can work with K-12 ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge

March 2015

448 pages

ISBN:9781450334174

DOI:10.1145/2723576

Conference Chairs:
Josh Baron
Marist College
,
Grace Lynch
SoLAR
,
Nicole Maziarz
Marist College
,
Program Chairs:
Paulo Blikstein
Stanford University
,
Agathe Merceron
Beuth University of Applied Sciences Berlin
,
George Siemens
University of Texas at Arlington

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

Bill & Melinda Gates Foundation
NSF

Conference

LAK '15

LAK '15: the 5th International Learning Analytics and Knowledge Conference

March 16 - 20, 2015

New York, Poughkeepsie

Acceptance Rates

LAK '15 Paper Acceptance Rate 20 of 74 submissions, 27%;

Overall Acceptance Rate 236 of 782 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
393
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)4

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

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
Andres JHutt SOcumpaugh JBaker RNasiar NPorter C(2022)How Anxiety Affects Affect: A Quantitative Ethnographic Investigation Using Affect Detectors and Data-Targeted InterviewsAdvances in Quantitative Ethnography10.1007/978-3-030-93859-8_18(268-283)Online publication date: 11-Jan-2022
https://doi.org/10.1007/978-3-030-93859-8_18
Ocumpaugh JBaker RSan Pedro MHawn MHeffernan CHeffernan NSlater SWise AWinne PLynch GOchoa XMolenaar IDawson SHatala M(2017)Guidance counselor reports of the ASSISTments college prediction model (ACPM)Proceedings of the Seventh International Learning Analytics & Knowledge Conference10.1145/3027385.3027435(479-488)Online publication date: 13-Mar-2017
https://dl.acm.org/doi/10.1145/3027385.3027435
Hewner MMishra SSheard JTenenberg JChinn DDorn B(2016)When Everyone Knows CS is the Best MajorProceedings of the 2016 ACM Conference on International Computing Education Research10.1145/2960310.2960318(3-11)Online publication date: 25-Aug-2016
https://dl.acm.org/doi/10.1145/2960310.2960318

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten