Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities

  • Bianca Clavio Christensen
  • Brian Bemman
  • Hendrik Knoche
  • Rikke Gade
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 95)


Technical educations often experience poor student performance and consequently high rates of attrition. Providing students with early feedback on their learning progress can assist students in self-study activities or in their decision-making process regarding a change in educational direction. In this paper, we present a set of instruments designed to identify at-risk undergraduate students in a Problem-based Learning (PBL) university, using an introductory programming course between two campus locations as a case study. Collectively, these instruments form the basis of a proposed learning ecosystem designed to identify struggling students by predicting their final exam grades in this course. We implemented this ecosystem at one of the two campus locations and analyzed how well the obtained data predicted the final exam grades compared to the other campus, where midterm exam grades alone were used in the prediction model. Results of a multiple linear regression model found several significant assessment predictors related to how often students attempted self-guided course assignments and their self-reported programming experience, among others.


Academic performance Student retention Learning Management System Learning Tools Interoperability Problem-based Learning Flipped learning 


  1. 1.
    Banas, J. R., Velez-Solic, A.: Designing effective online instructor training and professional development. In: Virtual Mentoring for Teachers: Online Professional Development Practices: Online Professional Development Practices, p. 1 (2012)Google Scholar
  2. 2.
    Bergmann, J., Sams, A.: Flipping for mastery. Educ. Leadersh. 71, 24–29 (2014)Google Scholar
  3. 3.
    Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R.: Taxonomy of educational objectives: the classification of educational goals. In: Handbook I: Cognitive Domain. David McKay Company, New York (1956)Google Scholar
  4. 4.
    Bøgelund, P., Justesen, K., Kolmos, A., Bylov, S.M.: Undersøgelse af frafald og fastholdelse ved medialogi og andre uddannelser ved det Teknisk-Naturvidenskabelige Fakultet 2015–2016: Arbejdsrapport Nr. 1. Aalborg Universitet (2016)Google Scholar
  5. 5.
    Cabrera, A.F., Nora, A., Castaneda, M.B.: College persistence: structural equations modeling test of an integrated model of student retention. J. High. Educ. 64(2), 123–139 (1993)Google Scholar
  6. 6.
    Casey, K., Gibson, P., Paris, I.: Mining moodle to understand student behaviour. In: International Conference on Engaging Pedagogy (2010)Google Scholar
  7. 7.
    Chang, M., Li, Y.: Smart Learning Environments. Lecture Notes in Educational Technology. Springer, Heidelberg (2015)Google Scholar
  8. 8.
    Cohen, G.L., Garcia, J., Purdie-Vaughns, V., Apfel, N., Brzustoski, P.: Recursive processes in self-affirmation: intervening to close the minority achievement gap. Science 324(5925), 400–403 (2009)CrossRefGoogle Scholar
  9. 9.
    Dekker, G., Pechenizkiy, M., Vleeshouwers, J.: Predicting students drop out: a case study. In: Proceedings of Educational Data Mining (2009)Google Scholar
  10. 10.
    Duckworth, A.L., Quinn, P.D.: Development and validation of the short grit scale (Grit-S). J. Pers. Assess. 91(2), 166–174 (2009)CrossRefGoogle Scholar
  11. 11.
    Dweck, C.S.: Self-theories: Their Role in Motivation, Personality, and Development. Psychology Press, Philadelphia (2000)Google Scholar
  12. 12.
    Evans, C., Palacios, L.: Interactive self assessment questions within a virtual environment. Int. J. e-Adoption (IJEA) 3, 1–10 (2011)CrossRefGoogle Scholar
  13. 13.
    Giovannella, C.: Smart learning eco-systems: “fashion” or “beef”? J. e-Learn. Knowl. Soc. 10(3), 15 (2014)Google Scholar
  14. 14.
    Giovannella, C., Rehm, M.: A critical approach to ICT to support participatory development of people centered smart learning ecosystems and territories. Aarhus Ser. Hum. Centered Comput. 1(1), 2 (2015)CrossRefGoogle Scholar
  15. 15.
    Glynn, J.G., Sauer, P.L., Miller, T.E.: A logistic regression model for the enhancement of student retention: the identification of at-risk freshmen. Int. Bus. Econ. Res. J. 1(8), 79–86 (2011)Google Scholar
  16. 16.
    Green, L.S., Banas, J., Perkins, R.: The Flipped College Classroom: Conceptualized and Re-Conceptualized. Springer, Heidelberg (2016)Google Scholar
  17. 17.
    Guerrero, W.: Flipped classroom and problem-based learning in higher education. In: Latin-American Context, Conference Proceedings. The Future of Education, p. 118 (2017)Google Scholar
  18. 18.
    Herzog, S.: Measuring determinants of student return vs. dropout/stopout vs. transfer: a first-to-second year analysis of new freshmen. In: Proceedings of 44th Annual Forum of the Association for Institutional Research (AIR) (2004)Google Scholar
  19. 19.
    Higher Education Research Institut: CIRP Freshman Survey – HERI (2017).
  20. 20.
    Lassibille, G., Gomez, L.N.: Why do higher education students drop out? Evidence from Spain. Educ. Econ. 16(1), 89–105 (2007)CrossRefGoogle Scholar
  21. 21.
    Leal, J.P., Queirós, R.: Using the learning tools interoperability framework for LMS integration in service oriented architectures. In: Technology Enhanced Learning, TECH-EDUCATION 2011 (2011)Google Scholar
  22. 22.
    Levitz, R.N.: Retention management system plus samples survey and report samples (2012).
  23. 23.
    Mayer, R.E.: Principles for managing essential processing in multimedia learning: segmenting, pretraining, and modality principles. In: The Cambridge Handbook of Multimedia Learning, pp. 169–182 (2005)Google Scholar
  24. 24.
    Severance, C., Hardin, J., Whyte, A.: The coming functionality mash-up in personal learning environments. Interact. Learn. Environ. 16(1), 47–62 (2008)CrossRefGoogle Scholar
  25. 25.
    Seymour, E., Hewitt, N.: Talking About Leaving. Westview Press, Boulder (1997)Google Scholar
  26. 26.
    Touron, J.: The determination of factors related to academic achievement in the university: implications for the selection and counseling of students. High. Educ. 12, 399–410 (1983)CrossRefGoogle Scholar
  27. 27.
    Tsukayama, E., Duckworth, A.L., Kim, B.: Domain-specific impulsivity in school-age children. Dev. Sci. 16(6), 879–893 (2013)Google Scholar
  28. 28.
    Vygotsky, L.: Zone of proximal development. In: Mind in Society: The Development of Higher Psychological Processes, p. 157 (1987)Google Scholar
  29. 29.
    Yeager, D.S., Purdie-Vaughns, V., Garcia, J., Apfel, N., Brzustoski, P., Master, A., Hessert, W.T., Williams, M.E., Cohen, G.L.: Breaking the cycle of mistrust: wise interventions to provide critical feedback across the racial divide. J. Exp. Psychol. Gen. 143(2), 804–824 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Bianca Clavio Christensen
    • 1
  • Brian Bemman
    • 1
  • Hendrik Knoche
    • 1
  • Rikke Gade
    • 1
  1. 1.Aalborg UniversityAalborgDenmark

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