16 SES 17 C JS, Digital Technology in School: Designing large scale interventions and corresponding research Part 2
Joint Symposium NW 16 and NW 27 continued from 16 SES 16 C JS
Background. In Switzerland, the Swiss Science Education (SWiSE, Koch, Stübi, Felchlin, & Labudde, 2015) project tried to develop constructivist and inquiry-based learning in science and technology in compulsory school (kindergarten, primary, and lower-secondary school) for three years. The idea was to support individual projects. How can one evaluate the efficacy of such a heterogenious program? Based on quasi-experimental theory, we developed a evaluation system that allows for both generalized statistical anayses and case studies. Theory. Quasi-experiments are considered an optimal methodology to establish causal relationships in non-randomized studies (Shadish, Cook, & Campbell, 2002). Albeit having a strong design, the problem of quasi-experiments can be seen in the assumption of standardized interventions. In order to avoid inert knowledge accumulation, Kirkpatrick & Kirkpatrick (2006) propose four conditions that programs should consider, a person's: desire to change, knowledge of what and how to change, transfer climate, and a reward. Huber (2011) adds to this, that such programs may have an impact on participants' competences, attitudes etc., performative practice, school development, cooperation and communication and teaching. Method. Two teachers per school chose their individual development focus. In sum, 120 teachers in 60 schools had 60 different projects. A standardized evaluation was implemented on school level, teacher level and student level. Variables were defined on a general level, i.e. for example measuring constructivist teaching in general, not focussing on a clearly defined aspect (because there were 60 different projects) or assessing motivation in science learning (because primary school does not have separate science subjects, whereas in lower-secondary school some students answered the questionaire with reference to their chemistry teacher others were referring to their physics class etc.). Two teacher control groups were sampled (one with project teachers' colleagues, one with independent teachers in other schools), including their classes. Results. Statistical analyses showed plausible relationshiph between all variables. Longitudinal developments on all levels were realistic. In comparison to the control group and over time the impact of the program was low. Discussion. The SWiSE evaluation can be described as a multi-level multi-group longitudinal black-box quasi-experiment. This approach seems rare in social sciences. The major problem is to evaluate the black-box. Quantitative results show plausibe variable relationships. Further analyses will merge open question formats with quantitative data in order to make individual developmental profiles visible. Also, the data can be disassembled in 60 quantitative case studies.
D-EDK. (2016). Lehrplan 21. In Deutschschweizer-Erziehungsdirektoren-Konferenz (Hrsg.), http://v-ef.lehrplan.ch/. Abgerufen von http://v-ef.lehrplan.ch Hutchison, A., & Colwell, J. (2016). Preservice Teachers’ Use of the Technology Integration Planning Cycle to Integrate iPads Into Literacy Instruction. Journal of Research on Technology in Education, 48(1), 1–15. Koch, A. F., Stübi, C., Felchlin, I., & Labudde, P. (2015). SWiSE - Research and Development in Practice. EAPRIL conference proceedings 2014., (1), 194–208. Peschel, M., & Koch, A. F. (2014). Lehrertypen - Typisch Lehrer? Huber, S. G. (2011). The impact of professional development: a theoretical model for empirical research, evaluation, planning and conducting training and development programmes. Professional Development in Education, 37(5), 837–853. Kirkpatrick, D. L., & Kirkpatrick, J. D. (2006). Evaluating training programs : the four levels (3rd Ed.). San Francisco, Calif.: Berrett-Koehler. Koch, A. F., Stübi, C., Felchlin, I., & Labudde, P. (2015). SWiSE - Research and Development in Practice. EAPRIL conference proceedings 2014., (1), 194–208. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, New York: Houghton Mifflin.
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