Time investment, learning strategies and learning outcomes: An empirical study of the approaches taken by German Initial Teacher Education students

Author(s):

Dina Kuhlee(presenting / submitting)

Conference:

ECER 2012

Network:

10. Teacher Education Research

Format:

Paper

Session Information

10 SES 12 C, Parallel Paper Session

Parallel Paper Session

Time:

2012-09-21

09:00-10:30

Room:

FCEE - Aula 2.4

Chair:

Peter Gray

Contribution

Formal aspects of study programmes, for instance the formal definition of students’ time investment into their studies, known as workload, are currently being debated in Germany. This is related to the discourse on the outcomes of the Bologna Process and the results of more than 10 years of restructuring higher education. With the introduction of Bachelor and Masters degrees based on the European Credit Transfer System (ECTS), the requirements of the study programmes are now to be defined by the approximate time that is needed by an average student to fulfill those requirements. Generally it is assumed that students should invest 40 hours work per week in their studies; roughly 30 hours of study time are rewarded with 1 ECTS point for the student (see, for example, Wex 2005, HRK 2004). However, the discussion on the formal and actual average workloads of students does not seem to offer much insight into the realities of studying.

Empirical data show that the actual workload of students lies between 23 and 25 hours per week and, hence, well below those formally defined workloads (see Kuhlee 2011, also Schulmeister & Metzger 2011). Thereby, the empirical data indicate a highly individualized approach to studying with regard to the amount of time invested and the distribution of those investments over the semester, as well as to the different parts of the study programme. At the same time, significant correlations between this actual workload of students and (a) their perceived stress, (b) their average marks and (c) the actual period of study students need to eventually finish their study programme cannot be identified (see Kuhlee 2012). Aiming to understand students’ approaches to studying in more detail the question arises: Why, on the one hand, do students invest so differently into their studies? On the other hand, how do students use their invested time with regard to their studies? Are there differences in learning approaches between different groups of students? If so, what are the reasons for those differences? This paper discusses these questions.

Using empirical data from the Master of Education, a university-based Initial Teacher Education program in the Federal State of Berlin, the paper looks at possible relationships between time investment, learning approaches and learning outcomes of students. Therefore, the paper analyses individual qualitative and quantitative data on students’ time investment, learning approaches and learning outcomes. The paper therefore takes the individual characteristics of students into account, such as socio-demographic background, self-efficacy (see Jerusalem & Schwarzer 1999) and individual tolerance of uncertainty (see Dalbert 1999). It also includes institutional contexts, such as structural aspects of the study programme, in order to get a deeper insight into the realities of studying in Initial Teacher Education programmes.

Method

The data introduced in this paper are based on a detailed online journal study that ran for six months. 40 students who were matriculated in the third semester of the Master of Education program at the Humboldt University in Berlin took part. Using the individual time-table of the students, which they had to record in great detail, an individual daily journal was constructed for each student where students had to fill in their daily time investment in their studies. Alongside this, students had to document aspects of their learning behavior, learning experiences and learning progress on a regular basis. Amongst other things, the study includes questions on self-efficacy, self regulation and individual tolerance of uncertainty on psychological scales as well as on learning strategies (see Wild & Schiefele 1994). In addition, the study integrates questionnaires on, for example, the socio-economic backgrounds of the students as well as their perspectives on the study program and their actual learning outcomes. Overall the study follows a triangulation method, combining both quantitative and qualitative data.

Expected Outcomes

With regard to the time investment of students into their studies different groups of students can be identified. The empirical data seem to indicate that those differences in time investments do not function as predictors for the formal learning outcomes of students, on the one hand, such as marks gained and the period required for successful completion of the study program, and students’ perceived stress, on the other. Personal characteristics of the students, such as the ability to cope with stress and uncertainty, self-regulation, self-efficacy and time management might influence the differences in time investment. However, the perspective of the students on the study program and their motivation to study ITE also seems to be relevant in this context. More analysis is required in order to establish whether those student groups developed specific approaches of studying. There is likely to be a distinct difference between those students regarding their learning approaches, such as surface and deep-learning approaches (see, for example, Entwistle & Ramsden 1983), as well as regarding their perceived learning progress. The paper intends to describe the various student groups empirically in relation to these dimensions.

References

Dalbert, C. (1999). Die Ungewissheitstolerenzskala: Skaleneigenschaften und Valisierungsbefunde. In Hallesche Berichte zur Pädagogischen Psychologie Nr. 1. Halle: Martin-Luther-Universität. Entwistle, N. & Ramsden, P. (1983). Understanding student learning. London: Croom Helm. HRK (Hochschulrektorenkonferenz) (2004). ECTS als System zur Anrechnung, Übertragung und Akkumulierung von Studienleistungen. Entschließung des 98. Senats vom 10. Februar 2004. In http://www.hrk.de/de/beschluesse/109_276.php, 01.05.2009. Jerusalem, M. & Schwarzer, R. (1999). Allgemeine Selbstwirksamkeitserwartung (SWE). In Schwarzer, R. & Jerusalem, M. (eds.). Skalen zur Erfassung von Lehrer- und Schülermerkmalen. Dokumentation der psychometrischen Verfahren im Rahmen der wissenschaftlichen Begleitung des Modellversuchs Selbstwirksame Schulen. Berlin: Freie Universität. Kuhlee, D. (2011). Students‘ Workload and Perceived Stress: An empirical study. European Conference on Educational Research (ECER), Berlin, 13.-16. September 2011 Kuhlee, D. (2012). Brauchen wir eine Workload-Diskussion? Zur Rolle formaler Workloads für das Studierhandeln. Eine empirische Studie bei Lehramtsstudierenden des Master of Education. In: Das Hochschulwesen (in review). Schulmeister, R. & Metzger, C. (2011). Die Workload im Bachelor: Ein empirisches Forschungsprojekt. In Schulmeister, R. & Metzger, C. (eds.). Die Workload im Bachelor: Zeitbudget und Studierverhalten. Eine empirische Studie. Münster u.a.: Waxmann. Wex, P. (2005). Bachelor und Master. Die Grundlagen des neuen Studiensystems in Deutschland. Ein Handbuch. Berlin: Duncker & Humblot. Wild, K.-P. & Schiefele, U. (1994). Lernstrategien im Studium. Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens. In Zeitschrift für Differenzielle und Diagnostische Psychologie, 15, 185-200.

Author Information

Dina Kuhlee (presenting / submitting)

Humboldt University Berlin

Berlin

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