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The competences and knowledge of data modeling extensively guide contemporary humans in exploring the natural world and life. We live in a data-driven society, and people who read the newspaper carefully can see a variety of charts, tables, graphs, and other data representations (Doerr et al., 2017; English, 2012). With the availability of huge amounts of data, people can increasingly engage in democratic dialogue and public decision making, as well as participate in the discussion of scienceand technology-related issues (Doerr et al., 2017; English, 2012; Provost & Fawcett, 2013). Data refers to things that can be recorded, analyzed, and reorganized, and presented in a quantitative format to organize the analysis so that people can use them to characterize the real world (Mayer-Schönberger & Cukier, 2014). Data modeling is a developmental process, starting with students’ exploration and investigation of meaningful phenomena, identifying which attributes are worthy of attention in the phenomenon, and organizing, structuring, visualizing, and representing data for these attributes (English, 2012; Lehrer & Lesh, 2003; Lehrer & Schauble, 2000). When students engage in scientific inquiry and communication in the science classroom, examining issues such as what data can be used to answer this question, how to organize the data to explain the question clearly, or how to organize evidence to make the most convincing explanation may provide opportunities for students to realize the process of data modeling (Namdar & Shen, 2015; NGSS Lead States, 2013). However, previous research (e.g., Srisawasdi & Panjaburee, 2019; Tsai, 2018) attached importance to inquiry teaching but failed to highlight the process and characteristics of data modeling. This may cause students to fail to develop data modeling competences (Jong et al., 2015; Nicolaou & Constantinou, 2014). For example, data collection usually requires the use of tools, but students may not be able to use such tools to measure data on things of interest (Michaels et al., 2007). In addition, when students interpret data, they may fail to grasp the meaning of the data as evidence of the hypothesis and may fail to interpret the meaning of the chart. Furthermore, students may impose rules on the data in a subjective way (Gott & Duggan, 2003). Data are not inherently structured. Scientists describe and organize data by selecting categories to impose structure on them. However, Jeng-Fung Hung National Kaohsiung Normal University, Taiwan Chun-Yen Tsai National Sun Yat-sen University, Taiwan Abstract. Previous studies on the effectiveness of virtual laboratories for learning have shown inconsistent results over the past decade. The purpose of this research was to explore the effects of a virtual laboratory and meta-cognitive scaffolding on students’ data modeling competences. A quasi-experimental design was used. Three classes of eighth graders from southern Taiwan participated in this research and were assigned to the Experimental Group I (EG I), the Experimental Group II (EG II), and the Control Group (CG). EG I (n=25) received the virtual laboratory and meta-cognitive scaffolding in the teaching and learning. EG II (n=28) received the virtual laboratory only in the teaching and learning. The CG (n=27) received the lecture with the cookbook laboratory. The teaching unit was Heat and Specific Heat, and the teaching time for the three groups was six lessons (of 45 minutes each). The Data Modeling Competences Test (DMCT) designed by the research team was used as the data collection instrument. The results showed that the virtual laboratory and meta-cognitive scaffolding had effects on students’ data modeling competences. This research shows the importance of the meta-cognitive scaffolding strategy for virtual laboratories when conducting data modeling teaching.

THE EFFECTS OF A VIRTUAL LABORATORY AND META-COGNITIVE SCAFFOLDING ON STUDENTS' DATA MODELING COMPETENCES