In the PhD project, which is supported by the Federal Ministry of Education and Research, we develop and test subject specific supportive measures for students in the introductory phase of biology study programmes.

While there is a need for qualified graduates in the field of natural sciences (Vollmer, 2015), dropout rates at German universities are above average (Heublein & Schmelzer, 2018). Thus, supporting academic achievement in these fields of study is particularly important. In correspondence to theoretical frameworks of academic achievement (Blüthmann et al., 2008; Kuh et al., 2007), subject specific prior knowledge has been identified as a crucial predictor of student academic success (Binder et al., 2019; Binder et al., 2019a; Buschhüter et al., 2017; Hell et al., 2007; Sorge et al., 2016; Woitkowski, 2019). Different types of knowledge have been shown to have different impact depending on the subject (Binder et al., 2019; Hailikari et al., 2007; Hailikari et al., 2008; Hailikari & Nevgi, 2010).

In biology and physics there is a significant correlation between student academic achievement and Knowledge of Meaning (Binder et al., 2019). Moreover, in the field of physics there is, in addition, a significant correlation between academic achievement and Application of Knowledge (Binder et al., 2019). Based on these research findings so far, we develop and test subject specific measures for the relevant types of knowledge needed in order to support students of biology in the early introductory phase of biology study programmes. Concept Maps (e.g., Novak & Gowin, 1984; Haugwitz & Sandmann, 2009) will be used to enhance Knowledge of Meaning. Application of Knowledge will be fostered by Worked Examples (e.g., Sweller & Cooper, 1985; Renkl, 2001). Each type of knowledge is surveyed before and after the intervention. Academic achievement is operationalized by the weighted average of all examination results of the first semester. By implementing an experimental design, we will be able to evaluate previously found correlations regarding causality.

This PhD project is mainly focused on students in the field of biology. Close cooperation with the departments of Physics Education at the Universities of Duisburg-Essen and Potsdam will enable a comparison of students of biology to students in the field of physics. Based on previous correlative findings we expect increments in Knowledge of Meaning to lead towards higher academic achievement in Biology. By providing specific and effective measures to support student learning, this PhD project will contribute to supporting student academic achievement.

Binder, T., Sandmann, A., Sures, B., Friege, G., Theyssen, H. & Schmiemann, P. (2019). Assessing prior knowledge types as predictors of academic achievement in the introductory phase of biology and physics study programmes using logistic regression. International Journal of STEM Education, 6(1). https://doi.org/10.1186/s40594-019-0189-9

Binder, T., Schmiemann, P. & Theyssen, H. (2019a). Knowledge Acquisition of Biology and Physics University Students – the Role of Prior Knowledge. Education Sciences, 9(4), 281. https://doi.org/10.3390/educsci9040281

Blüthmann, I., Lepa, S. & Thiel, F. (2008). Studienabbruch und -wechsel in den neuen Bachelorstudiengängen. Zeitschrift für Erziehungswissenschaft, 11(3), 406-429. https://doi.org/10.1007/s11618-008-0038-y

Buschhüter, D., Spoden, C. & Borowski, A. (2017). Studienerfolg im Physikstudium: Inkrementelle Validität physikalischen Fachwissens und physikalischer Kompetenz. Zeitschrift für Didaktik der Naturwissenschaften, 23(1), 127-141.

Hailikari, T., Nevgi, A. & Lindblom-Ylänne, S. (2007). Exploring alternative ways of assessing prior knowledge, its components and their relation to student achievement: A mathematics based case study. Studies in Educational Evaluation, 33(3-4), 320-337.

Hailikari, T., Katajavuori, N. & Lindblom-Ylänne, S. (2008). The relevance of prior knowledge in learning and instructional design. American Journal of Pharmaceutical Education, 72(5), 1-9.

Hailikari, T. & Nevgi, A. (2010). How to diagnose at-risk students in chemistry: The case of prior knowledge assessment. International Journal of Science Education, 32(15), 2079-2095.

Hell, B., Trapmann, S. & Schuler, H. (2007). Eine Metaanalyse der Validität von fachspezifischen Studierfähigkeitstests im deutschsprachigen Raum. Empirische Pädagogik, 21(3), 251-270.

Heublein, U. & Schmelzer, R. (2018). Die Entwicklung der Studienabbruchquoten an den deutschen Hochschulen. Berechnungen auf Basis des Absolventenjahrgangs 2016. Hannover: Deutsches Zentrum für Hochschul- und Wissenschaftsforschung (DZHW)

Haugwitz, M. & Sandmann, A. (2009). Kooperatives Concept Mapping in Biologie: Effekte auf den Wissenserwerb und die Behaltensleistung. Zeitschrift für Didaktik der Naturwissenschaften, 15, 89-107.

Kuh, G. D., Kinzie, J., Buckley, J. A., Bridges, B. K. & Hayek, J. C. (2007). Piecing together the student success puzzle: Research, propositions, and recommendations. ASHE higher education report: v. 32, no. 5. Wiley Subscription Services at Jossey-Bass.

Novak, J. D. & Gowin, D. B. (1984). Learning how to learn. Cambridge University Press.

Renkl, A. (2001). Lernen aus Lösungsbeispielen: Einführung. Unterrichtswissenschaft. 29(1), 2-4.

Sorge, S., Petersen, S. & Neumann, K. (2016). Die Bedeutung der Studierfähigkeit für den Studienerfolg im 1. Semester in Physik. Zeitschrift für Didaktik der Naturwissenschaften, 22(1), 165-180.

Sweller, J. & Cooper, G. A. (1985). The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra. Cognition and Instruction, 2(1), 59-89.

Vollmer, M. (2015). Bestimmung von Fachkräfteengpässen und Fachkräftebedarfen in Deutschland: Fokus-Studie der deutschen nationalen Kontaktstelle für das Europäische Migrationsnetzwerk (EMN).

Woitkowski, D. (2019). Erfolgreicher Wissenserwerb im ersten Semester Physik: Analyse mithilfe eines Niveaumodells. Zeitschrift für Didaktik der Naturwissenschaften, 25(1), 97-114.