| 1. |
- Kaufmann, Odd Tore, et al.
(författare)
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Teachers’ Error-handling Practices Within and Across Lesson Phases in the Mathematics Classroom
- 2022
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Ingår i: International Journal of Science and Mathematics Education. - : Springer. - 1571-0068 .- 1573-1774.
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Tidskriftsartikel (refereegranskat)abstract
- Multiple studies have been conducted regarding teachers’ error-handling practices, and how errors can be treated as opportunities for learning, albeit in the context of whole-class discussions. The aim of the present research is to continue to investigate teachers’ error-handling practices as they occur in different phases of maths lessons: introduction of the task, when students are working alone, and when students are working in pairs and finally, as part of the whole-class discussion. The study included 51 lessons from twelve teachers. A cross-case analysis was made across the individual teacher cases to look for similarities and differences between different teachers’ error-handling practices across the lesson phases in order to create teaching profiles with similar handling of student errors across the lesson phases. Five error-handling teaching profiles were identified; correcting errors throughout all phases, correcting errors during students’ work while few errors are brought up in whole class, correcting errors during students’ work while using a variety of practices in whole class, ignoring errors while using some of them in whole class, and discussing and explaining errors.
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| 2. |
- Kaufmann, Odd Tore, et al.
(författare)
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Teachers’ framing of students’ difficulties in mathematics learning in collegial discussions
- 2022
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Ingår i: Scandinavian Journal of Educational Research. - : Informa UK Limited. - 0031-3831 .- 1470-1170. ; 67:7, s. 1069-1085
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates the diagnostic and prognostic framings of Swedishmathematics teachers regarding the difficulties experienced by studentsin mathematics learning. Collegial discussions among 65 mathematicsteachers in nine collegial groups were videotaped during a professionaldevelopment (PD) program entitled Boost for Mathematics for analysis.The results show that the diagnostic framings of the teachers weremainly attributed to the cognitive abilities of students, whereas theprognostic framings were mainly related to lesson organization suchthat students should collaborate. While the teachers emphasizecollaborate group work, they put little emphasis on how they could actin these learning situations. These results contribute to theunderstanding of Swedish mathematics teachers’ framing of students’difficulties in mathematics learning and to the role of collegialdiscussions in PD initiatives.
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| 3. |
- Matta, Corrado, 1978-, et al.
(författare)
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Automatized visualization methods for the qualitative analysis of teachers' collegial discussions
- 2022
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Ingår i: New Trends in Qualitative Research (NTQR). - : Ludomedia. - 2184-7770. ; 11:-, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we discuss a set of procedures for the visualization of conversation data, based on machine learning and text mining algorithms. The methodological function of these visualization methods is to support (rather than substitute) humanistic and interpretive qualitative data analysis. The context of our methodological discussion is the study of teachers’ learning communities. Enabling collegial learning among teachers has been argued to play a pivotal role in both teachers’ professional development and students’ learning. The problem is that the interpretive study of collegial discussions among teachers, which are typically considered a fruitful context for the study of teachers' collegial learning, is both time-consuming and cognitively demanding. Several scholars have discussed the potential of including machine learning and text mining in the toolbox of qualitative data analysis as a way to analyze large qualitative data sets and solve the time demand problem. However, the literature discussing the use of machine learning and text mining for qualitative research typically conceives the algorithms as substitutes for the work of the researcher. These automatized approaches are introduced to categorize the data, substituting the researcher in the analysis process. In contrast, we discuss the potential rewards of using automatized methods to support the analysis of teachers’ collegial discussions. We introduce a set of visualization procedures based on semi-supervised topic modeling and supervised classification algorithms and discuss how these visualizations can provide additional elements useful for the analysis of collegial discussions. Whereas these visualizations do not substitute interpretive analysis, we argue that they can support researchers’ interpretations by reducing data, identifying patterns of interactions, and possibly, visualizing group-level learning.
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