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The College Completion Puzzle: A Hidden Markov Model Approach

Author

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  • Dirk Witteveen

    (The City University of New York)

  • Paul Attewell

    (The City University of New York)

Abstract

Higher education in America is characterized by widespread access to college but low rates of completion, especially among undergraduates at less selective institutions. We analyze longitudinal transcript data to examine processes leading to graduation, using Hidden Markov modeling. We identify several latent states that are associated with patterns of course taking, and show that a trained Hidden Markov model can predict graduation or nongraduation based on only a few semesters of transcript data. We compare this approach to more conventional methods and conclude that certain college-specific processes, associated with graduation, should be analyzed in addition to socio-economic factors. The results from the Hidden Markov trajectories indicate that both graduating and nongraduating students take the more difficult mathematical and technical courses at an equal rate. However, undergraduates who complete their bachelor’s degree within 6 years are more likely to alternate between these semesters with a heavy course load and the less course-intense semesters. The course-taking patterns found among college students also indicate that nongraduates withdraw more often from coursework than average, yet when graduates withdraw, they tend do so in exactly those semesters of the college career in which more difficult courses are taken. These findings, as well as the sequence methodology itself, emphasize the importance of careful course selection and counseling early on in student’s college career.

Suggested Citation

  • Dirk Witteveen & Paul Attewell, 2017. "The College Completion Puzzle: A Hidden Markov Model Approach," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(4), pages 449-467, June.
  • Handle: RePEc:spr:reihed:v:58:y:2017:i:4:d:10.1007_s11162-016-9430-2
    DOI: 10.1007/s11162-016-9430-2
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    References listed on IDEAS

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    1. Edward H. Ip & Alison Snow Jones & D. Alex Heckert & Qiang Zhang & Edward D. Gondolf, 2010. "Latent Markov Model for Analyzing Temporal Configuration for Violence Profiles and Trajectories in a Sample of Batterers," Sociological Methods & Research, , vol. 39(2), pages 222-255, November.
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    Cited by:

    1. Augustin Kelava & Pascal Kilian & Judith Glaesser & Samuel Merk & Holger Brandt, 2022. "Forecasting Intra-individual Changes of Affective States Taking into Account Inter-individual Differences Using Intensive Longitudinal Data from a University Student Dropout Study in Math," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 533-558, June.
    2. Hsun-Yu Chan & Xueli Wang, 2018. "Momentum Through Course-Completion Patterns Among 2-Year College Students Beginning in STEM: Variations and Contributing Factors," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(6), pages 704-743, September.
    3. Benoit, Dries F. & Tsang, Wai Kit & Coussement, Kristof & Raes, Annelies, 2024. "High-stake student drop-out prediction using hidden Markov models in fully asynchronous subscription-based MOOCs," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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