Testing the Validity of a Meta-reading Model
Authors: Gross Ariel 
Published: July 2011

Based on an integration of Brown and Flavell’s theories on meta-cognition, a meta-reading model was tested on its validity components. The model is organized with three factors (meta-knowledge, planning, and monitoring) and four sub factors (the text, the goal for reading, the strategy, and the characteristics of the reader). 354 10th grade students (15.6 yr. old) from ten religious high schools performed unseen tasks on three subjects: Talmud, History, and Math. After each task they were given the following: 1) A meta-reading questionnaire (based on the model) on the corresponding unseen subject. 2) An academic achievement test on the corresponding subject, 3) Raven’s Advanced Progressive Matrices, 4) Verbal thinking test (Glantz’s “Mem”).Factor analysis showed that meta-reading components were organized according to the school subject and not according to the components of the model. Further analysis showed that this result fit Talmud and History but in Math the factors were organized according to the components of the meta-reading model. Results of canonical analysis confirmed the similarity between Talmud and History and the differentiation between them and Math. The general correlation between Talmud and History was due to a high contribution of the monitoring factor in both subjects. On the other hand, the general correlation found between Talmud and Math was created by the dominant monitoring factor in Math and the dominant planning factor in Talmud. The general correlation which was created between History and Math was created by the meta-knowledge factor in History while in Math the planning factor was found to be the highest contributor to the correlation with History. Regression analysis also indicated a great similarity between Talmud and History as opposed to Math. In Talmud and History the planning factor is the best factor to predict the grades in meta-reading; however, the monitoring factor was found to be the best predictor of the meta-reading grades in Math. Both in Talmud and History on the sub-factor level, the planning factor was found to be the best predictor and the text factor the second best. In Math the planning factor was found to be the best predictor and after that the learning factor. The text factor was not found to be predicting. The results of the research show that construct validity (general-domain or specific domain) is not an all or none phenomenon but a continuum. Therefore, rather than pose only two options, it should be asked: to what extent is there construct validity in meta-reading? In History the dispersion of information, new concepts, and the need to remember large amounts of information cause learners to devote their energy to the content itself, thus making the metacognitive factors secondary. As opposed to this, in Math, where the textual factors are less important, the information is more concentrated, and it seems that the distractions are fewer. There are more opportunities for learners to organize their learning on the basis of meta-cognitive factors, to use meta-knowledge, and to plan and monitor. Further studies in meta-reading that will choose other subjects to test, will be able to add information on the question: “To what extent do the structure of disciplinary knowledge and the presentation of the subjects influence the meta-cognitive activity?”The low and non-significant correlation between meta-reading and intelligence tests indicates that there is discriminant validity; namely, meta-reading and intellectual abilities are separate variables measuring two different things. Achievement and meta-reading showed high significant correlations. Planning was the best predictor of achievement in Talmud and History and meta-knowledge was found to be the best predictor for Math. From the sub factors the planning factor was found to be the best predictor of achievements in all subjects. Meta-reading was found to be a better predicting factor of academic achievement than the Raven and Mem intelligence tests. The fact that the scores on the meta-reading tests were quite low (30%- 50%) decreases the efficiency of the tests and damages its sensitivity and its distribution. Therefore there is a need to increase the level of meta-reading by efficient teaching and then to replicate this research. In any case learners should study meta-reading (as well as meta-writing) within a meta-cognitive model and not wait for researchers to validate models.