Assessing teacher pedagogical knowledge: the Video Assessment of Teacher Knowledge (VATK)

Wiens, Peter D.; Beck, Jori S.; Lunsmann, Christina

doi:10.1080/03055698.2020.1750350

Cited by 18 publications

(7 citation statements)

References 31 publications

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“…There are many different ways to assess student achievement [22][23][24]. Numerous studies assessed students' performance and assessment in general [25][26][27], whereas other research assessed students for a particular goal, including academic accomplishment [5]. For each of the activities listed above, some cutting-edge research studies that assessed student performance in various ways are discussed below.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating Student Knowledge Assessment Using Machine Learning Techniques

Alruwais

Zakariah

2023

Sustainability

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The process of learning about a student’s knowledge and comprehension of a particular subject is referred to as student knowledge assessment. It helps to identify areas where students need additional support or challenge and can be used to evaluate the effectiveness of instruction, make important decisions such as on student placement and curriculum development, and monitor the quality of education. Evaluating student knowledge assessment is essential to measuring student progress, informing instruction, and providing feedback to improve student performance and enhance the overall teaching and learning experience. This research paper is designed to create a machine learning (ML)-based system that assesses student performance and knowledge throughout the course of their studies and pinpoints the key variables that have the most significant effects on that performance and expertise. Additionally, it describes the impact of running models with data that only contains key features on their performance. To classify the students, the paper employs seven different classifiers, including support vector machines (SVM), logistic regression (LR), random forest (RF), decision tree (DT), gradient boosting machine (GBM), Gaussian Naive Bayes (GNB), and multi-layer perceptron (MLP). This paper carries out two experiments to see how best to replicate the automatic classification of student knowledge. In the first experiment, the dataset (Dataset 1) was used in its original state, including all five properties listed in the dataset, to evaluate the performance indicators. In the second experiment, the least correlated variable was removed from the dataset to create a smaller dataset (Dataset 2), and the same set of performance indicators was evaluated. Then, the performance indicators using Dataset 1 and Dataset 2 were compared. The GBM exhibited the highest prediction accuracy of 98%, according to Dataset 1. In terms of prediction error, the GBM also performed well. The accuracy of optimistic forecasts on student performance, denoted as the performance indicator ‘precision’, was highest in GBM at 99%, while DT, RF, and SVM were 98% accurate in their optimistic forecasts for Dataset 1. The second experiment’s findings demonstrated that practically no classifiers showed appreciable improvements in prediction accuracy with a reduced feature set in Dataset 2. It showed that the time required for related learning objects and the knowledge level corresponding to a goal learning object have less impact.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating Student Knowledge Assessment Using Machine Learning Techniques

Alruwais

Zakariah

2023

Sustainability

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“…In the context of teacher education, teacher knowledge has been an important research line since the early 1980s (e.g., Ben-Peretz, 2011;Black & Halliwell, 2000;Carter, 1990;Elbaz, 1981;Hung, 2020). During this time, the research focus has shifted from "knowledge for teachers" to "knowledge of teachers" (Meijer et al, 1999) and "teacher knowledge" (Wiens et al, 2022;Xu & Connelly, 2009). According to scholars, while in the knowledge for teachers perspective, knowledge is seen as a target that should be delivered to them, in the teacher knowledge view, knowledge is a narratively embodied and practically expressed construct (Schaefer & Clandinin, 2018) that is shaped by teachers' informal and formal educational spheres such as personal life experiences (Li, 2020), learning and teaching experiences (Wang et al, 2019), academic background (Schaik et al, 2018), and participation in professional development activities (Golombek, 2009;Xu & Connelly, 2009).…”

Section: Introductionmentioning

confidence: 99%

Implications of Teachers’ Personal Practical Knowledge for Teacher Training Programs: A Case Study of Primary Teachers

Talaee

Bozorg

Schrittesser

2023

IJEMST

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This study aims to understand teachers’ Personal Practical Knowledge to provide future teacher educators with essential points for improving teacher training programs. The study was conducted with six teachers in the primary education context, using narrative inquiry as a qualitative approach. The semi-structured interview with teachers indicated that they had a rich knowledge of factors affecting the success of the teaching profession. As such, data analysis of teachers’ transcripts using open, axial, and selective coding resulted in five main themes crucial for future teacher educators to consider for developing effective teacher training programs: (1) Entwined and complicated aspects of teaching, (2) Effective communication is key, (3) Ineffectiveness of theoretical versus practical courses, (4) Being ignored by society, and (5) Losing the dream of being perfect. The paper concludes with implications for the theory and practice in the teacher education field.

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“…Tais mudanças repercutem no âmbito educacional, em que, cada vez mais, se reconhece a importância do acesso à informação e à escolarização em todos os níveis. Nesse cenário, a universidade assume papel central na formação de um novo perfil de profissional do ensino, mais flexível, competente e polivalente, capaz de lidar com um conhecimento em construção e de se adaptar a um mundo em constante transformação (CUNHA, 2011;BORGES;HUNGER, 2012;SINCLAIR;WEBB, 2020;WIENS;BECK;LUNSMANN, 2020).…”

Section: Introductionunclassified

Reflexões Da Docência No Ensino Superior: A Base De Conhecimentos Para O Ensino

PEREIRA¹,

Duek²,

Folle³

et al. 2023

INCLU

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Este estudo de natureza teórica apresenta como objetivo refletir e sistematizar os conhecimentos que sustentam a prática pedagógica do professor que atua no ensino superior. Focaliza os processos de formação e construção de uma base de conhecimento do professor universitário que precisa atuar em um cenário de mudanças e incertezas e de profundas transformações sociais e educacionais, que traz novas atribuições à escola e, por conseguinte, à formação dos futuros professores. Os diferentes tipos de conhecimentos são adquiridos ao longo da vida, perpassando por ambientes como a educação básica e a universidade, sendo nestes contextos em que se dá a construção de conhecimentos específicos para o professor universitário e os futuros professores. A base teórica a partir do modelo de conhecimentos para o ensino apresenta um olhar para as diversas possibilidades de compreensão do processo de ensino pelos tipos de conhecimentos formais, os quais estão interligados e contribuem ao processo formativo.

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Assessing teacher pedagogical knowledge: the Video Assessment of Teacher Knowledge (VATK)

Cited by 18 publications

References 31 publications

Evaluating Student Knowledge Assessment Using Machine Learning Techniques

Evaluating Student Knowledge Assessment Using Machine Learning Techniques

Implications of Teachers’ Personal Practical Knowledge for Teacher Training Programs: A Case Study of Primary Teachers

Reflexões Da Docência No Ensino Superior: A Base De Conhecimentos Para O Ensino

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