Pre-Service Teachers’ Attitudes Toward Teaching Science and Their Science Learning at Indonesia Open University

Abstract

This study focuses on attitudes toward (teaching) science and the learning of science for primary school among pre-service teachers at the Open University of Indonesia. A threeyear longitudinal survey was conducted, involving 379 students as pre-service teachers (PSTs) from the Open University in Surabaya regional office. Attitudes toward (teaching) science' (ATS) instrument was used to portray PSTs' preparation for becoming primary school teachers. Data analyses were used, including descriptive analysis and confirmatory factor analysis. The model fit of the attitudes toward (teaching) science can be described from seven dimensions: self-efficacy for teaching science, the relevance of teaching science, gender-stereotypical beliefs, anxiety in teaching science, the difficulty of teaching science, perceived dependency on contextual factors, and enjoyment in teaching science. The results of the research also described science learning at the Open University of Indonesia looks like. Implications for primary teacher education are discussed.

Confirmatory Factor Analysis of Pupils’ Attitudes Towards Technology (PATT) Scales: A case of Pakistan

“…The aforementioned model presented a comprehensive framework for research. This has been empirically and theoretically verified through a variety of contexts and fields (Thibaut et al, 2018;Suprapto &Mursid, 2017;Asma et al, 2011). Additionally, a wide number of researchers concerned with technology education referred to the model under discussion to examine the professional attitudes of teachers toward technology (Asma et al 2011).…”

Development and Validation of Students’ Perception on Learning by Questioning Scale in Physics

“…By referring these steps, previously, the author determined whether the data were appropriate to perform an EFA through Kaiser-Meyer-Olkin (KMO) and Bartlett's test (Suprapto, 2016b;Suprapto, Chang, & Ku, 2017). "The KMO Index and Bartlett's Sphericity Test were used in the study to determine the adequacy of the sample and to indicate whether or not the data fitted the factor analysis" (Suprapto & Mursid, 2017). Data could be factorized if the KMO value is .50 or above (Field, 2009;Polat, 2017).…”

“…It was noted that the cut-off of the loading factor was .40 (Stevens, 2002). Additionally, the acceptable indexes for CFA were [X2/df<3, .05<RMSEA<.10, .90<GFI<.95, .85<AGFI<.90, and .90<CFI<.95], with referring to Joreskog & Sorbom (1993); Kline (2005); and Suprapto & Mursid (2017).…”

“…The ratio of Chi-square (χ 2 ) and degree of freedom (df) obtained from CFA were 2.399 (p<.001), it was less than 3.0 as an indicator for good fit index. In a CFA analysis, the value of RMSEA (.05<RMSEA<.10) is an indicator of acceptable indexes (Kline (2005); Schermelleh-Engel, Moosbrugger, & Müller (2003); and Suprapto & Mursid (2017).…”

“…Accordingly, it can be asserted that the model-data fit was allowable. Meanwhile, the criteria for CFI (Comparative Fit Index), NFI (Normed Fit Index) and IFI (Incremental Fit Index) was .90, an indicator of a perfect fit between the model and the data obtaining (Joreskog & Sorbom (1993); Kline (2005); and Suprapto & Mursid (2017). The results of the analysis also determined CFI, NFI, IFI as .901, .843 and .902, respectively, which may be considered an acceptable model.…”