1 Tojalabar

86 Evaluation Essay

Textbooks provide novice teachers with guidance in course and activity design; it assures a measure of structure, consistency, and logical progression in a class. This paper reports on a study that investigated one of the textbooks (Top Notch) which is used in some of the Iranian English language institutions. The purpose of this research project was to determine the overall pedagogical value and suitability of the book towards students’ needs. For this purpose, 105 students and 32 teachers were selected and data were gathered by two questionnaires which were prepared by Litz (2001). The teacher questionnaire consisted of 40 items and the student version consisted of 25 items. An additional component of the study consisted of a student “needs analysis” that was conducted at the same time as the textbook evaluation survey. After analyzing data, it was shown that although the textbook had some shortcomings, it had met students’ needs and it could be a good book in the hand of a good teacher.


We examine how passive and active observations are useful to evaluate an air quality analysis. By leaving out observations from the analysis, we form passive observations, and the observations used in the analysis are called active observations. We evaluated the surface air quality analysis of O3 and PM2.5 against passive and active observations using standard model verification metrics such as bias, fractional bias, fraction of correct within a factor of 2, correlation and variance. The results show that verification of analyses against active observations always give an overestimation of the correlation and an underestimation of the variance. Evaluation against passive or any independent observations display a minimum of variance and maximum of correlation as we vary the observation weight, thus providing a mean to obtain the optimal observation weight. For the time and dates considered, the correlation between (independent) observations and the model is 0.55 for O3 and 0.3 for PM2.5 and for the analysis, with optimal observation weight, increases to 0.74 for O3 and 0.54 for PM2.5. We show that bias can be a misleading measure of evaluation and recommend the use of a fractional bias such as the modified normalized mean bias (MNMB). An evaluation of the model bias and variance as a function of model values also show a clear linear dependence with the model values for both O3 and PM2.5. View Full-Text

Keywords: chemical data assimilation; air quality model diagnostics; cross-validationchemical data assimilation; air quality model diagnostics; cross-validation

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