Using generic and content-specific teaching practices in teacher evaluation: An exploratory study of teachers' perceptions

Charalambous, Charalambos Y.; Komitis, Andreas; Papacharalambous, Maria; Stefanou, A

doi:10.1016/j.tate.2014.03.001

Cited by 13 publications

(10 citation statements)

References 58 publications

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“…This implies that, for instance, the distinction between the three factors of instructional quality, teacher support, cognitive activation, and classroom management should be reflected in students' perceptions. But current research suggests that this distinction is not perfect and may impose the coexistence between general and specific factors (e.g., Charalambous et al, 2014 ). As a consequence, traditional measurement models may not fully represent the conceptualization of perceived instructional quality, pointing to the need for flexible modeling approaches.…”

Section: Introductionmentioning

confidence: 98%

“…Besides addressing the conceptual overlap between the factors of instructional quality, researchers also pose the question of the extent to which ratings of the classroom environment represent general or specific perceptions of instruction—that is, is there a “halo effect” on students' ratings of instruction or can they well differentiate between different aspects such as the three factors of instructional quality (e.g., Harlen, 2006 ; Charalambous et al, 2014 )? Hence, the question arises whether students are generally able to distinguish between the different factors of instructional quality in their ratings (specific factors) or they only provide overall ratings of instruction irrespective of these factors (general factor).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Individual Students' Perceptions of Instructional Quality: An Investigation of their Factor Structure, Measurement Invariance, and Relations to Educational Outcomes

2016

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Students' perceptions of instructional quality are among the most important criteria for evaluating teaching effectiveness. The present study evaluates different latent variable modeling approaches (confirmatory factor analysis, exploratory structural equation modeling, and bifactor modeling), which are used to describe these individual perceptions with respect to their factor structure, measurement invariance, and the relations to selected educational outcomes (achievement, self-concept, and motivation in mathematics). On the basis of the Programme for International Student Assessment (PISA) 2012 large-scale data sets of Australia, Canada, and the USA (N = 26,746 students), we find support for the distinction between three factors of individual students' perceptions and full measurement invariance across countries for all modeling approaches. In this regard, bifactor exploratory structural equation modeling outperformed alternative approaches with respect to model fit. Our findings reveal significant relations to the educational outcomes. This study synthesizes different modeling approaches of individual students' perceptions of instructional quality and provides insights into the nature of these perceptions from an individual differences perspective. Implications for the measurement and modeling of individually perceived instructional quality are discussed.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Evaluating Individual Students' Perceptions of Instructional Quality: An Investigation of their Factor Structure, Measurement Invariance, and Relations to Educational Outcomes

2016

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“…In contrast with standardized tests, which often measure low-level skills (Resnick et al 2004), classroom observation instruments are often grounded in a theory of instruction aligned with conceptually oriented instruction, emphasizing the quality of teacher-student interactions and the development of content understanding via engagement in cognitively demanding activities (Grossman et al 2014;Seidel and Shavelson 2007). Content-generic instruments (e.g., Framework for Teaching [Danielson 2011]; CLASS [Pianta et al 2010]) are designed to assess instructional interactions across subject areas, whereas contentspecific instruments assess subject-specific instructional quality (Charalambous et al 2014). Examples of mathematics-content-specific instruments include the Instructional Quality Assessment (IQA; Boston 2012), which is rooted in research on challenging mathematical tasks and maintaining cognitive demand (e.g., Stein and Lane 1996).…”

Section: Theory Of Instruction and Student Learning Underpinning The Mqimentioning

confidence: 99%

Relationships between Observations of Elementary Mathematics Instruction and Student Achievement: Exploring Variability across Districts

Lynch¹,

Chin²,

Blazar

2017

American Journal of Education

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Much debate surrounding teacher quality has focused on students' standardized test scores, but recent federal and state initiatives have emphasized the use of multiple measures to evaluate teacher quality, including classroom observations. In this study, we explore differences across school districts in the relationship between student achievement outcomes and the observed quality of teachers' instruction. Using data from 298 elementary mathematics teachers in five urban US districts, we examine relationships between teachers' performance on the Mathematical Quality of Instruction observation instrument and their students' scores on both state standardized and researcher-developed tests. We find that these relationships differ across school districts. We explore the extent to which differences in skills and expectations for students across tests may explain this variability. An improved understanding of the relationship between classroom observations and student tests may help districts to better support teachers in developing their instructional effectiveness.

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“…It should be recognized that for some chemical engineering students such a course is quite challenging and perhaps a longer, more leisurely course may be better for learning [16]. Some topics introduced are by necessity advanced, for example "accounting for the pressure term".…”

Section: Instructor Viewsmentioning

confidence: 99%

Practical Teaching and Learning Evaluation of Computational Fluid Dynamics Based on Traditional Chemical Engineering Curriculum

Chen

2015

ILSHS

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Practical teaching and learning evaluation of computational fluid dynamics (CFD) have been incorporated into a chemical engineering curriculum at the intermediate undergraduate level. CFD has now become a component of professional life in engineering practice and to prepare students properly, they must get exposure to all aspects of their chosen profession. Issues of concern arise when mathematical modelling is being introduced into a curriculum. For example, at the practical level, it must be considered whether or not an appropriate platform has been developed to allow the students to use the software efficiently and importantly without frustration. Also it is important that students have been taught sufficient skills for the student to continue with simulations in a systematic and methodical manner. The incorporation of the CFD package into a traditional chemical engineering curriculum is described here, and evaluation results based on pre-post knowledge and skill experiments, and student survey results document successful learning outcomes and effectiveness of the approach.

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Using generic and content-specific teaching practices in teacher evaluation: An exploratory study of teachers' perceptions

Cited by 13 publications

References 58 publications

Evaluating Individual Students' Perceptions of Instructional Quality: An Investigation of their Factor Structure, Measurement Invariance, and Relations to Educational Outcomes

Evaluating Individual Students' Perceptions of Instructional Quality: An Investigation of their Factor Structure, Measurement Invariance, and Relations to Educational Outcomes

Relationships between Observations of Elementary Mathematics Instruction and Student Achievement: Exploring Variability across Districts

Practical Teaching and Learning Evaluation of Computational Fluid Dynamics Based on Traditional Chemical Engineering Curriculum

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