Measuring the individual's conception of knowledge and knowing in action
To overcome the challenges involved in exploring the conceptions of knowledge, researchers have also pointed out a need for authentic measures (Hofer, 2004; Sandoval, 2009). Here, “authenticity” means that individuals’ conceptions of knowledge and knowing are explored in real-world situations, for example in classrooms, and in different problem-solving or information-searching situations. The major advantage of authentic assessment is that it enables capturing such aspects, like students’ justification of knowledge, that are too complex and multifaceted to lend themselves to mere self-report methods. Authentic measurement may improve the reliability of the study because the findings do not depend on the respondents’ abilities to describe their conceptions. In addition, authentic measurement makes it possible to consider the characteristics of the context in which epistemic thinking is activated (Mason, Ariasi, & Boldrin, 2011; Sandoval, 2009). It provides an opportunity to deepen an understanding of the situated nature of thinking (Barzilai & Zohar, 2012; Hofer, 2004; Kuhn, lordanou, Pease, & Wirkala, 2008).
Recent studies have found that authentic measurements, such as searching information about a controversial topic and problem-solving tasks with open-ended questions, provide an opportunity for students to reflect spontaneously on their beliefs about knowledge and knowing (Barzilai & Zohar, 2012; Ferguson, Braten, & Stromso, 2012; Hyytinen et al., 2014; Mason et al., 2011). These kinds of measurements are found to be rich sources concerning how students analyse, evaluate, integrate, and justify the claims and sources of information and knowledge. They also show the criteria by which students evaluate not only knowledge but also the evidence that supports the knowledge (Hofer, 2004), as well as the way in which students respond to conflicting sources of knowledge.
Literature on these issues displays several facets as to how to explore individuals’ conceptions of knowledge and knowing in action. One way is to connect an authentic approach to the concurrent data collection strategy. For example, some researchers have combined online search and knowledge integration with a think-aloud method and video observation (e.g., Barzilai & Zohar, 2012; Hofer, 2004; Stromso & Braten, 2014), while others have focused on how students acquire, justify, process, and utilise knowledge from various sources in an open-ended problem-solving situation (e.g., Hyytinen et al., 2014). Furthermore, concept maps together with storylines or detailed written explanations have proved to be valuable tools for exploring students’ reflection, justification, and use of knowledge (Koponen & Nousiainen, 2013; Nousiainen, 2013). Such maps have also been found to provide various insights into analysing the development of students’ understanding (Schwendimann, 2014).
Although authentic methods bring clear advantages to the exploration of students’ conceptions of knowledge and knowing, these methods are not perfect, either. It is very common that data collection situations are video recorded (e.g., Barzilai & Zohar, 2012; Hyytinen et al., 2014; Stromso &
BrSten, 2014). Analysing video-observation data is time consuming: it takes a lot of time and effort to analyse properly this kind of data. Another challenge is how to combine different kinds of datasets produced in the data collection situation, such as video data on students’ think-aloud or written explanations (see Hyytinen et al., 2014; Stromso & Briten, 2014). Furthermore, authentic methods can be more demanding for participants than selfreports, because they really need to use higher-order thinking skills in the data collection situation when analysing, justifying, and utilising the various sources of knowledge.
One potential cause of inconsistency and ambiguity in results can be theoretical, that is to say, how researchers have specified and conceptualised the models of epistemic conceptions. In the literature, there is no consensus on what categories and dimensions of multidimensional phenomena are included in the measures (DeBacker et al., 2008; Muis et al., 2014; Schraw, 2013). Schraw (2013, p. 1) sums up the prevailing situation by pointing out that it is unclear whether the measurements used in contemporary research on individuals’ conceptions of knowledge really measure the same constructs and phenomena. There is also evidence that researchers define the dimensions of individuals’ conceptions of knowledge in conceptually and theoretically different ways (Chinn, Buckland, & Samarapungavan, 2011; Hofer & Pintrich, 2002; Holma & Hyytinen, 2015; Kallio, 2011). For example, the term “relativism” Is used to refer to at least three different epistemological positions (Holma & Hyytinen, 2015; for another philosophical critique, see Chapter 13). The theoretical problems with the concept of relativism have also been highlighted elsewhere. Kallio (2011) and Leadbeater (1986), for example, have demonstrated that the definition of relativism is ambiguous.
It is important to understand that theoretical frameworks play a significant role in how the data is analysed and interpreted. A theoretical framework is the researcher’s tool for analysing and interpreting data. If the tool Is not adequate, then there is a real risk that the analyses will be distorted (Holma & Hyytinen, 2015; Hyytinen, 2015). Therefore, we suggest that theoretical analysis of the current theoretical frameworks of epistemic conceptions would provide a bridge between theory and practice. By applying theoretical analyses it is possible to elucidate theoretical background assumptions as well as contradictory statements and inconsistencies in the theoretical framework by analysing the interconnections between the concepts. In summary, theoretical analyses have great relevance in developing research methods in future research (e.g., Chinn et al., 2011; Holma & Hyytinen, 2015; Kallio, 2011; Leadbeater, 1986).