A critical analysis of the use of formative assessment in schools

The literature is replete with exhortations about the need for teachers to use formative assessment. Clearly there are many advantages in doing so. Yet, empirical studies reveal that there is very little evidence that formative assessment is used frequently in classrooms. It is necessary to examine the reasons, why this is the case and to look for possible solutions to the problem.     

Contemporary Models of Communicative Practice: Shaky Foundations in the Foundation Stage?

This article outlines a four‐year‐old child's multi‐modal encounters with texts and analyses one of those encounters in the light of a framework for the analysis of such events. It is argued that this analysis leads to the conclusion that the experiences with texts that permeate young children's lives are complex in nature and that this complexity is not sufficiently addressed in relevant curriculum frameworks. Further, it is suggested that young children's encounters with televisual texts need to be identified as emergent techno‐literacy practices which should be supported and extended in ways which correlate to the strategies used with regard to print‐based texts.     

Purposes for literacy in children's use of the online virtual world Club Penguin

This paper reports on a study of the purposes for literacy discernible in young children's use of the virtual world, Club Penguin. Twenty‐six children aged between 5 and 11 took part in semi‐structured interviews in which their use of virtual worlds was explored. Further, three 11‐year‐old children were filmed using Club Penguin four times each over the period of 1 month in their own homes and they and their parents took part in interviews regarding their use of this virtual world. Findings indicate that the purposes for literacy in virtual worlds such as these are varied and have much in common with purposes for literacy in the off ine world. The virtual world Club Penguin provided a motivating and enjoyable context for reading and writing and enabled the construction and maintenance of online social networks. The implications for research, policy and practice are discussed.     

Building the mentoring relationship: humanism and the importance of storytelling between mentor and mentee

ABSTRACT

In this paper, we describe a mentoring program designed to help prepare high school seniors at Arizona State University Preparatory Academy for postsecondary education. Specifically, we address the usefulness of ‘humanistic mentoring,’ a form of mentoring that stresses the importance of reciprocity, mutuality, and empathy in the mentor/mentee relationship. We further describe how sharing stories was useful in developing such humanistic relationships. Based on etic-thematic coding of interviews from 13 of the mentees, a humanistic lens for mentoring and sharing personal stories is productive for both developing relationships and passing on knowledge.     

Structural Equation Models of Latent Interactions: Clarification of Orthogonalizing and Double-Mean-Centering Strategies

The purpose of this investigation is to compare a new (double-mean-centering) strategy to estimating latent interactions in structural equation models with the (single) mean-centering strategy (Marsh, Wen, & Hau, 2004 Marsh, H. W., Wen, Z. and Hau, K. T. 2004. Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction.. Psychological Methods, 9: 275–300. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar], 2006 Marsh, H. W., Wen, Z. and Hau, K. T. 2006. “Structural equation models of latent interaction and quadratic effects”. In A second course in structural equation modeling Edited by: Hancock, G. and Mueller, R. 225–265. Greenwich, CT: Information Age.  [Google Scholar]) and the orthogonalizing strategy (Little, Bovaird, & Widaman, 2006 Little, T. D., Bovaird, J. A. and Widaman, K. F. 2006. On the merits of orthogonalizing powered and product term: Implications for modeling interactions among latent variables.. Structural Equation Modeling, 13: 497–519. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]; Marsh et al., 2007 Marsh, H. W., Wen, Z., Hau, K. T., Little, T. D., Bovaird, J. A. and Widaman, K. F. 2007. Unconstrained structural equation models of latent interactions: Contrasting residual- and mean-centered approaches.. Structural Equation Modeling, 14: 570–580. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). A key benefit of the orthogonalizing strategy is that it eliminated the need to estimate a mean structure as required by the mean-centering strategy, but required a potentially cumbersome 2-step estimation procedure. In contrast, the double-mean-centering strategy eliminates both the need for the mean structure and the cumbersome 2-stage estimation procedure. Furthermore, although the orthogonalizing and double-mean-centering strategies are equivalent when all indicators are normally distributed, the double-mean-centering strategy is superior when this normality assumption is violated. In summary, we recommend that applied researchers wanting to estimate latent interaction effects use the double-mean-centering strategy instead of either the single-mean-centering or orthogonalizing strategies, thus allowing them to ignore the cumbersome mean structure.     

Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices,parameter estimates,goodness of fit,and adjusted sample sizes

The use of sample covariance matrices constructed with pairwise deletion for data missing completely at random (S_PW) is addressed in a simulation study based on 3 sample sizes (n = 200, 500, 1,000) and 5 levels of missing data (%miss = 0, 1, 10, 25, and 50). Parameter estimates were unbiased, parameter variability was largely explicable in terms of the number of nonmissing cases, and no sample covariance matrices were nonpositive definite except when %miss was 50 and the sample size was 200. However, nominal χ² test statistics (and, thus, fit indices based on χ²s) were substantially biased by %miss and its interaction with N. Corrected χ²s based on the minimum, mean, and maximum number of nonmissing cases per measured variables and cases per covariance term (NPC) reduced but did not eliminate the bias. Empirically derived power functions did substantially better but may not generalize to other situations. Whereas the minimum NPC (the default in the SPSS version of LISREL) is probably better than most simple alternatives in many applications, the problem of how to assess fit for models fit to S_PWS has no simple solution; caution is recommended, and there is need for further research with more suitable methods for this problem.     

Evaluating Model Fit With Ordered Categorical Data Within a Measurement Invariance Framework: A Comparison of Estimators

A paucity of research has compared estimation methods within a measurement invariance (MI) framework and determined if research conclusions using normal-theory maximum likelihood (ML) generalizes to the robust ML (MLR) and weighted least squares means and variance adjusted (WLSMV) estimators. Using ordered categorical data, this simulation study aimed to address these queries by investigating 342 conditions. When testing for metric and scalar invariance, Δχ² results revealed that Type I error rates varied across estimators (ML, MLR, and WLSMV) with symmetric and asymmetric data. The Δχ² power varied substantially based on the estimator selected, type of noninvariant indicator, number of noninvariant indicators, and sample size. Although some the changes in approximate fit indexes (ΔAFI) are relatively sample size independent, researchers who use the ΔAFI with WLSMV should use caution, as these statistics do not perform well with misspecified models. As a supplemental analysis, our results evaluate and suggest cutoff values based on previous research.