University student retention: Best time and data to identify undergraduate students at risk of dropout

References

• Adam, A. J., & Gaither, G. H. (2005). Retention in higher education: A selective resource guide. New Directions for Institutional Research, 125, 107–122.
   Google Scholar
• Aina, C. (2013). Parental background and university dropout in Italy. Higher Education, 65, 437–456.
   Web of Science ®Google Scholar
• Arce, M. E., Crespo, B., & Míguez-Álvarez, C. (2015). Higher Education drop-out in Spain–Particular case of Universities in Galicia. International Education Studies, 8, 247–264.
   Google Scholar
• Bacallao, J., & Bacallao, J. (2010). Imputación múltiple en variables categóricas usando data augmentation y Árboles de decisión. Investigación Operacional, 31, 133–139.
   Google Scholar
• Baradwaj, B. K., & Pal, S. (2012). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2, 63–69.
   Google Scholar
• Berge, Z. L., & Huang, Y.-P. (2004). A model for sustainable student retention: Holistic perspective on the student dropout problem with special attention to e-learning. Deosnews, 13. Retrieved from https://www.researchgate.net/profile/Zane_Berge/publication/237429805_A_Model_for_Sustainable_Student_Retention_A_Holistic_Perspective_on_the_Student_Dropout_Problem_with_Special_Attention_to_e-Learning/links/0deec52f30b93d72a1000000.pdf
   Google Scholar
• Bernold, L. E., Spurlin, J. E., & Anson, C. M. (2007). Understanding our students: A longitudinal-study of success and failure in engineering with implications for increased retention. Journal of Engineering Education, 96(3), 263–274.
   Web of Science ®Google Scholar
• Bland, C. J., Taylor, A. L., Shollen, S. L., Weber-Main, A. M., & Mulcahy, P. A. (2009). Faculty success through mentoring: A guide for mentors, mentees, and leaders. Plymouth, England: R&L Education.
   Google Scholar
• Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. London, UK: CRC press.
   Google Scholar
• Brooman, S., & Darwent, S. (2014). Measuring the beginning: A quantitative study of the transition to higher education. Studies in Higher Education, 39, 1523–1541.
   Web of Science ®Google Scholar
• Cabrera, L., Bethencourt, J. T., Pérez, P. Á., & Alfonso, M. G. (2006). El problema del abandono de los estudios universitarios. Relieve, 12, 171–203.
   Google Scholar
• Cole, B., High, K., & Weinland, K. (2013). High school pre-engineering programs: Do they contribute to college retention? American Journal of Engineering Education (AJEE), 4, 85–98.
   Google Scholar
• Dekker, G. W., Pechenizkiy, M., & Vleeshouwers, J. M. (2009, July 1–3). Predicting students drop out: A case study. Paper presented at the International Conference on Educational Data Mining (EDM), 2nd, Cordoba, Spain, 41–50. Retrieved from https://eric.ed.gov/?id=ED539082
   Google Scholar
• Engelbrecht, J., Harding, A., & Du Preez, J. (2007). Long-term retention of basic mathematical knowledge and skills with engineering students. European Journal of Engineering Education, 32, 735–744.
   Google Scholar
• Esteban-García, M., Bernardo-Gutiérrez, A. B., Tuero-Herrero, E., Cerezo-Menéndez, R., & Núñez-Pérez, J. C. (2016). El contexto sí importa: identificación de relaciones entre el abandono de titulación y variables contextuales. European Journal of Education and Psychology, 9, 79–88.
   Web of Science ®Google Scholar
• Fantz, T. D., Siller, T. J., & DeMiranda, M. A. (2011). Pre-collegiate factors influencing the self-efficacy of engineering students. Journal of Engineering Education, 100, 604.
   Web of Science ®Google Scholar
• Forsman, J., Van den Bogaard, M., Linder, C., & Fraser, D. (2015). Considering student retention as a complex system: A possible way forward for enhancing student retention. European Journal of Engineering Education, 40, 235–255.
   Web of Science ®Google Scholar
• Herzog, S. (2006). Estimating student retention and degree-completion time: Decision trees and neural networks vis-à-vis regression. New Directions for Institutional Research, 131, 17–33.
   Google Scholar
• Larose, S., Cyrenne, D., Garceau, O., Harvey, M., Guay, F., Godin, F., & Deschenes, C. (2011). Academic mentoring and dropout prevention for students in maths, science and technology. Mentoring & Tutoring: Partnership in Learning, 19, 419–439.
   Google Scholar
• Lin, S.-P. (2015). Using EDM for developing EWS to predict university students drop out. International Journal of Intelligent Technologies and Applied Statistics, 8, 365–388.
   Google Scholar
• Lowis, M., & Castley, A. (2008). Factors affecting student progression and achievement: Prediction and intervention. A two‐year study. Innovations in Education and Teaching International, 45, 333–343.
   Web of Science ®Google Scholar
• McFarlane, K. J. (2016). Tutoring the tutors: Supporting effective personal tutoring. Active Learning in Higher Education, 17, 77–88.
   Web of Science ®Google Scholar
• Mendez, G., Buskirk, T. D., Lohr, S., & Haag, S. (2008). Factors associated with persistence in science and engineering majors: An exploratory study using classification trees and random forests. Journal of Engineering Education, 97, 57–70.
   Web of Science ®Google Scholar
• Mercado, M. E. (2012). Las aplicaciones del análisis de segmentación. Empiria. Revista De Metodología De Ciencias Sociales, 1, 13–49.
   Google Scholar
• Pal, S. (2012). Mining educational data to reduce dropout rates of engineering students. International Journal of Information Engineering and Electronic Business, 4, 1.
   Google Scholar
• Quadri, M. M. N., & Kalyankar, D. N. V. (2010). Drop out feature of student data for academic performance using Decision Tree techniques. Global Journal of Computer Science and Technology, 10, 2–5.
   Google Scholar
• Raelin, J. A., Bailey, M. B., Hamann, J., Pendleton, L. K., Reisberg, R., & Whitman, D. L. (2014). The gendered effect of cooperative education, contextual support, and self-efficacy on undergraduate retention. Journal of Engineering Education, 103, 599–624.
   Web of Science ®Google Scholar
• Ramaswami, M., & Bhaskaran, R. (2010). A CHAID based performance prediction model in educational data mining. International Journal of Computer Sciences Issues, 7, 10–18.
   Google Scholar
• Sancho-Vinuesa, T., Escudero-Viladoms, N., & Masià, R. (2013). Continuous activity with immediate feedback: A good strategy to guarantee student engagement with the course. Open Learning, 28, 51–66.
   Google Scholar
• Stoessel, K., Ihme, T. A., Barbarino, M.-L., Fisseler, B., & Stürmer, S. (2015). Sociodemographic diversity and distance education: Who drops out from academic programs and why? Research in Higher Education, 56, 228–246.
   Web of Science ®Google Scholar
• Swail, W. S. (2004). The art of student retention: A handbook for practitioners and administrators. Educational Policy Institute. Texas Higher Education Coordinating Board 20th Annual Recruitment and Retention Conference Austin, TX June, 21, 2004.
   Google Scholar
• Thomas, L. (2011). Do Pre-entry Interventions such as ‘Aimhigher’ impact on student retention and success? A review of the literature. Higher Education Quarterly, 65, 230–250.
   Google Scholar
• Thomas, L. (2012). Building student engagement and belonging in Higher Education at a time of change. Paul Hamlyn Foundation, 100. Retrieved from https://www.phf.org.uk/wp-content/uploads/2014/10/What-Works-report-final.pdf
   Google Scholar
• Thomas, L., & Hovdhaugen, E. (2014). Complexities and challenges of researching student completion and non-completion of HE programmes in Europe: |A comparative analysis between England and Norway. European Journal of Education, 49, 457–470.
   Web of Science ®Google Scholar
• Tinto, V. (2006). Research and practice of student retention: What next? Journal of College Student Retention: Research, Theory and Practice, 8, 1–19.
   Google Scholar
• Van den Bogaard, M. (2012). Explaining student success in engineering education at Delft University of Technology: A literature synthesis. European Journal of Engineering Education, 37, 59–82.
   Google Scholar
• Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting academic performance by Data Mining methods. Education Economics, 15, 405–419.
   Google Scholar
• Vinson, D., Nixon, S., Walsh, B., Walker, C., Mitchell, E., & Zaitseva, E. (2010). Investigating the relationship between student engagement and transition. Active Learning in Higher Education, 11, 131–143.
   Google Scholar
• Vivian, C. (2005). Advising the at-risk college student. The Educational Forum, 69, 336–351.
   Google Scholar
• Vossensteyn, H., Kottmann, A., Jongbloed, B., Kaiser, F., Cremonini, L., Stensaker, B., & Wollscheid, S. (2015). Dropout and completion in higher education in Europe: Main report. Retrieved from https://publications.europa.eu/en/publication-detail/-/publication/4deeefb5-0dcd-11e6-ba9a-01aa75ed71a1/language-en
   Google Scholar
• Wilson, K. L., Murphy, K. A., Pearson, A. G., Wallace, B. M., Reher, V. G. S., & Buys, N. (2016). Understanding the early transition needs of diverse commencing university students in a health faculty: Informing effective intervention practices. Studies in Higher Education, 41, 1023–1040.
   Web of Science ®Google Scholar
• Wimshurst, K., Wortley, R., Bates, M., & Allard, T. (2006). The impact of institutional factors on student academic results: Implications for ‘quality’ in universities. Higher Education Research & Development, 25, 131–145.
   Google Scholar
• Yasmin, D. (2013). Application of the classification tree model in predicting learner dropout behaviour in open and distance learning. Distance Education, 34, 218–231.
   Web of Science ®Google Scholar
• Yorke, M. (2016). The development and initial use of a survey of student ‘belongingness’, engagement and self-confidence in UK higher education. Assessment & Evaluation in Higher Education, 41, 154–166.
   Web of Science ®Google Scholar
• Yorke, M., & Thomas, L. (2003). Improving the retention of students from lower socio-economic groups. Journal of Higher Education Policy and Management, 25, 63–74.
   Google Scholar
• Zepke, N., & Leach, L. (2010). Improving student engagement: Ten proposals for action. Active Learning in Higher Education, 11, 167–177.
   Google Scholar