Teaching Practices and Pedagogical Innovations

Evidence from TALIS

image of Teaching Practices and Pedagogical Innovations

Fortunately, teaching practices help shape the learning experiences and increase motivation and achievement for students. In addition, it has been revealed that when teachers collaborate well together they also tend to work better with students. This new informative publication clearly identifies and arranges profiles in relation to two connected areas of professional teacher practices: classroom teaching practices and participation in professional learning communities.

This practical book enables a more comprehensive understanding of teaching practice and participation in professional learning communities nationally and internationally. It provides policy makers and other key stakeholders with the relevant information they need for educational system monitoring.


Annex A Multilevel Latent Profile Analysis

Latent profile analysis (LPA) is derived from conventional latent class analysis, originally introduced by Lazarsfeld and Henry (1968) for the purposes of deriving latent attitude variables from responses to dichotomous survey items. Important contributions to latent class analysis have been made by Clogg (1995). For a review, see Magidson and Vermunt (2004) and Kaplan, Kim, and Kim (2009). In a traditional latent class analysis, it is assumed that an individual belongs to one and only one latent class, and that – given an individual’s latent class membership – the observed responses are independent of one another (referred to as the assumption of local independence). The latent classes arise from the patterns of response frequencies to categorical items, where the response frequencies play a role similar to that of the correlation matrix in factor analysis (Lanza, Collins, Lemmon and Schafer, 2007). The analogue of factor loadings are parameters that estimate the probability of a particular response on the manifest indicators given membership in the latent class. Unlike continuous latent variables (i.e. factors), categorical latent variables (latent classes) divide individuals into mutually exclusive and exhaustive groups.


This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error