Quality of geometry instruction and its short-term impact on students' understanding of the Pythagorean Theorem

Lipowsky, Frank; Rakoczy, Katrin; Pauli, Christine; Drollinger-Vetter, Barbara; Klieme, Eckhard; Reusser, Kurt

doi:10.1016/j.learninstruc.2008.11.001

Cited by 348 publications

(288 citation statements)

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“…Seidel [49] reveals that the differences in students' motivational learning outcomes relate to perceived learning conditions in mathematics classrooms such as instructional clarity and interaction with the teacher. Based on a theoretical conceptualization of learning as a self-directed process, in which teachers provide learning opportunities that must be perceived and utilized by students [17,50], in the present study we expected to find distinct student subtypes with respect to perceived instructional quality corresponding to the subtypes of motivational learning outcomes as shown by Seidel [49]. Thus, we expected to find a subtype of learners with overall positive perceptions of the characteristics of instructional quality in math class.…”

Section: Characteristics Of Instructional Quality Mathematics Self-comentioning

confidence: 77%

“…The basic dimension of cognitive activation refers to characteristics of instruction, which promote students' conceptual understanding by including, for example, challenging tasks or enhancing different solution strategies and nonroutine problem solving [17]. Additionally, discursive effectiveness, which refers to students' opportunity to coconstruct knowledge by participating in mathematics classroom discourse, is viewed as a mechanism to activate cognitive processing [18,45].…”

Section: Characteristics Of Instructional Quality Mathematics Self-comentioning

confidence: 99%

“…Although a vast number of previous studies consider characteristics of instruction and their impact on learning [13][14][15][16], individual differences in students' perceptions of instructional quality in their mathematics classrooms are explored less frequently. However, it is important to address individual differences as students' learning success depends highly on the level of adaptability of learning environments to students' individual needs [17,18]. Subsequently, in the current study, we explored distinct student patterns of perceived instructional quality in mathematics classes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Instructional Quality and Attitudes toward Mathematics: Do Self-Concept and Interest Differ across Students' Patterns of Perceived Instructional Quality in Mathematics Classrooms?

Lazarides

Ittel

2012

Child Development Research

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Using a person-centered research approach, the present study explored individual differences in students' perceptions of instructional quality in secondary school mathematics classes and their relations to students' self-concept and interest in mathematics. Drawing on data collected from 425 high school students from ten schools in Berlin, Germany (male: 53.2%; female: 46.3%), latent class analyses (LCA) revealed four distinct patterns of perceived quality of instruction. Almost half of the sample (46%) had a high likelihood of perceiving an overall low quality in mathematics classes. Those students reported particular low self-concept and interest in mathematics. Compared to male students, female students were significantly more likely to belong to this "challenging pattern." Consequences for educational practice are discussed and suggest that instruction in mathematics should take into account learners' highly individual ways of perceiving and evaluating their learning environment.

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Section: Characteristics Of Instructional Quality Mathematics Self-comentioning

confidence: 77%

Section: Characteristics Of Instructional Quality Mathematics Self-comentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Instructional Quality and Attitudes toward Mathematics: Do Self-Concept and Interest Differ across Students' Patterns of Perceived Instructional Quality in Mathematics Classrooms?

Lazarides

Ittel

2012

Child Development Research

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“…Few studies have focused on domain-specific teaching practices and examined pre and post-tests to establish a causal direction to interpret the effects of teaching practices on students' outcomes (see also Kunter et al, 2007;Lipowsky, et al, 2009). None (to our knowledge) have looked specifically at representational competence although there is now widespread acceptance of its importance with science practice and education (Yore & Hand, 2010).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

“…However, no study we know of has empirically analysed the interactive relationship between RC and content knowledge (CK) and only a few studies have analysed how teaching practices in authentic classroom settings affect RC (e.g., Hubber, Tytler, & Hastam, 2010;Kohl & Finkelstein, 2006;Prain & Waldrip, 2006;Tytler, Prain, & Peterson, 2007). This is surprising as there is strong evidence that students' cognitive and affective outcomes are REPRESENTATIONAL COMPETENCE IN BIOLOGY CLASSES 3 generally influenced by teaching practices and instructional quality (e.g., Fraser, 2003;Kunter, Baumert, & Köller, 2007;Lipowsky et al, 2009;Schroeder et al, 2011). Therefore, we argue that it is crucial to incorporate an explicit representational focus in teaching and learning science as a part of domain-specific instructional quality in order to develop students' RC.…”

Section: Introductionmentioning

confidence: 99%

Do student perceptions of teaching predict the development of representational competence and biological knowledge?

Nitz

Ainsworth

Nerdel

et al. 2014

Learning and Instruction

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Dealing with representations is a crucial skill for students and such representational competence is essential for learning science. This study analysed the relationship between representational competence and content knowledge, student perceptions of teaching practices concerning the use of different representations, and their impact on students' outcome over a teaching unit.Participants were 931 students in 51 secondary school classes. Representational competence and content knowledge were interactively related. Representational aspects were only moderately included in teaching and students did not develop rich representational competence although content knowledge increased significantly. Multilevel regression showed that student perceptions of interpreting and constructing visual-graphical representations and active social construction of knowledge predicted students' outcome at class level, whereas the individually perceived amount of terms and use of symbolic representations influenced the students' achievement at individual level. Methodological and practical implications of these findings are discussed in relation to the development of representational competence in classrooms.

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Machine learning for the educational sciences

Hilbert

Coors

Kraus

et al. 2021

Review of Education

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Machine learning (ML) provides a powerful framework for the analysis of high‐dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non‐linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large‐scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML‐based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large‐scale assessment in education.

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Quality of geometry instruction and its short-term impact on students' understanding of the Pythagorean Theorem

Cited by 348 publications

References 38 publications

Instructional Quality and Attitudes toward Mathematics: Do Self-Concept and Interest Differ across Students' Patterns of Perceived Instructional Quality in Mathematics Classrooms?

Instructional Quality and Attitudes toward Mathematics: Do Self-Concept and Interest Differ across Students' Patterns of Perceived Instructional Quality in Mathematics Classrooms?

Do student perceptions of teaching predict the development of representational competence and biological knowledge?

Machine learning for the educational sciences

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