Relationships between Domain-Specific Knowledge, Generic Attributes, and Instructional Skills

Kuhn, Christiane; Zlatkin‐Troitschanskaia, Olga; Lindmeier, Anke; Jeschke, Colin; Saas, Hannes; Heinze, Aiso

doi:10.1007/978-3-658-27886-1_5

Cited by 6 publications

(7 citation statements)

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“…This article deals with the question of the suitability of automated scoring for the rating of in-service teachers' PCK in the domain of economics. For this purpose, open-ended PCK tasks (Kuhn et al, 2016(Kuhn et al, , 2020 were scored by human raters and the scoring engine ESCRITO (Zesch and Horbach, 2018). On average, there is a significant agreement between automated and human scoring.…”

Section: Discussionmentioning

confidence: 99%

“…The data is based on the answers to a standardized and validated paper-pencil test by Kuhn (2014) for assessing teachers' PCK in economics. The long version of the test consists of nine open-ended and eight closed situation-based tasks (Kuhn et al, 2016); the short version of six open-ended and five closed tasks (Kuhn et al, 2020). The closed tasks in multiple-choice (MC) format require the cognitive processes of application and analysis, the open-ended tasks additionally require the process of creation (of new ideas, products) (Kuhn et al, 2016).…”

Section: Methods Pedagogical Content Knowledge Instrumentmentioning

confidence: 99%

“…A further assessment of teachers' PCK using the short version of the test (11 items) was carried out in 2018 as part of the ELMaWi 3 project. The total sample (N = 372) comprised students of business and economics education, trainees and in-service teachers of economics (Kuhn et al, 2020, Table 2).…”

Section: Samplementioning

confidence: 99%

“…In addition, other personal factors such as general cognitive abilities, selfefficacy and ambiguity tolerance were assessed. The analyses showed that there were either no or only weak correlations between these measured factors and the participants' PCK (general cognitive abilities: 0.014; self-efficacy: −0.008; ambiguity tolerance: 0.213 * * ; * * p < 0.01) (Kuhn et al, 2020). Therefore, it is questionable whether these factors may also affect the automated scoring of PCK, and they are consequently not included in the following analyses.…”

Section: Samplementioning

confidence: 99%

“…The open-ended responses were transcribed and coded by two human raters (Kuhn, 2014;Kuhn et al, 2020). In 2011, 62% of the open-ended responses (randomly selected) were double coded, whereas in 2018, all of the open-ended responses were double coded.…”

Section: Scoring and Analysis Proceduresmentioning

confidence: 99%

See 4 more Smart Citations

Automated Scoring of Teachers’ Pedagogical Content Knowledge – A Comparison Between Human and Machine Scoring

Wahlen

Kuhn

Zlatkin‐Troitschanskaia

et al. 2020

Front. Educ.

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To validly assess teachers' pedagogical content knowledge (PCK), performance-based tasks with open-response formats are required. Automated scoring is considered an appropriate approach to reduce the resource-intensity of human scoring and to achieve more consistent scoring results than human raters. The focus is on the comparability of human and automated scoring of PCK for economics teachers. The answers of (prospective) teachers (N = 852) to six open-response tasks from a standardized and validated test were scored by two trained human raters and the engine "Educational SCoRIng Toolkit" (ESCRITO). The average agreement between human and computer ratings, κ w = 0.66, suggests a convergent validity of the scoring results. The results of the single-sector variance analysis show a significant influence of the answers for each homogeneous subgroup (students = 460, trainees = 230, in-service teachers = 162) on the automated scoring. Findings are discussed in terms of implications for the use of automated scoring in educational assessment and its potentials and limitations.

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Section: Discussionmentioning

confidence: 99%

Section: Methods Pedagogical Content Knowledge Instrumentmentioning

confidence: 99%

Section: Samplementioning

confidence: 99%

Section: Samplementioning

confidence: 99%

Section: Scoring and Analysis Proceduresmentioning

confidence: 99%

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Automated Scoring of Teachers’ Pedagogical Content Knowledge – A Comparison Between Human and Machine Scoring

Wahlen

Kuhn

Zlatkin‐Troitschanskaia

et al. 2020

Front. Educ.

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Evaluating and developing teacher instructional practices in economics using a new video-based test

Day

Kuhn

Saas

2022

The Journal of Social Studies Research

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Teachers’ instructional practices are important for student learning. However, there are few tools for evaluating instructional practices in social studies. To this end, we present a video-based instrument with teacher-student interactions for assessing teaching practices in a high school economics course. We investigate, using an iterative design-based methodology, the adaptation and validation of a test originally developed in Germany for use in the United States. We found that experienced economics teachers and those with more content knowledge demonstrated higher-level instructional skills, and were more flexibile with their thinking. Inexperienced economics teachers and those with low content knowledge not only struggled with the test items, but also with the test format, the content, inter-cultural understanding, and with feeling overwhelmed. The test provides an opportunity for economics teachers to obtain feedback, practice, reflection, and improvement of their instruction in a way that has not previously been available. It also illustrates the possibilities and drawbacks of video-based testing in teacher training.

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Assessment of Student Learning Progress in Higher Education – Methodological Approaches and Results

Zlatkin‐Troitschanskaia¹,

Toepper²

2021

International Perspectives on Higher Education Research

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Relationships between Domain-Specific Knowledge, Generic Attributes, and Instructional Skills

Cited by 6 publications

References 65 publications

Automated Scoring of Teachers’ Pedagogical Content Knowledge – A Comparison Between Human and Machine Scoring

Automated Scoring of Teachers’ Pedagogical Content Knowledge – A Comparison Between Human and Machine Scoring

Evaluating and developing teacher instructional practices in economics using a new video-based test

Assessment of Student Learning Progress in Higher Education – Methodological Approaches and Results

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