How much C is in TPACK? A systematic review on the assessment of TPACK in mathematics DOI Creative Commons
Alina Kadluba, Anselm Strohmaier, Christian Schons

et al.

Educational Studies in Mathematics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 7, 2024

Abstract Teachers need technological pedagogical content knowledge (TPACK) for teaching with technology, and its assessment is crucial research practice. Previous literature reviews on TPACK were not specific to a area (e.g., mathematics), although, by definition, the framework includes content-specific facets. Consequently, requirements could differ depending content. Further, reliable of mathematics-specific depends quality test instruments used, but there no consensus type used in past studies. This systematic review adds existing focusing mathematics, investigating study characteristics , instrument operationalizations TPACK. Regarding characteristics, findings reveal an increase number studies conducted across various countries worldwide. As researchers frequently self-developed assess TPACK, often without providing information reliability or validity measures. operationalizations, more than half self-report scales followed observations material analyses, while tests hardly used. Additionally, assessments typically referred domain mathematics as whole instead subdomains mathematics. The results raise questions regarding comparability

Language: Английский

Understanding pre-service mathematics teachers’ intentions to use GeoGebra: The role of technological pedagogical content knowledge DOI Creative Commons
Esra YILDIZ, İbrahim Arpacı

Education and Information Technologies, Journal Year: 2024, Volume and Issue: 29(14), P. 18817 - 18838

Published: March 14, 2024

Abstract The research investigated the “Technological Pedagogical Content Knowledge” (TPACK) of pre-service mathematics teachers and its impact on their sustained intention to utilize GeoGebra in teaching mathematics. This study introduced a novel model by extending “Unified Theory Acceptance Use Technology” (UTAUT) with TPACK. Through “Structural Equation Modeling” (SEM) applied data collected from 205 teachers, was evaluated. results revealed that factors like effort expectancy, social influence, performance TPACK significantly predicted continuous use GeoGebra. However, no substantial relationship found concerning facilitating conditions. findings have managerial practical implications for academics, principals, decision-makers promoting educational settings.

Language: Английский

Citations

7
Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model DOI Open Access
Xin Tang, Zhiqiang Yuan, Shaojun Qu

et al.

Journal of Computer Assisted Learning, Journal Year: 2024, Volume and Issue: 41(1)

Published: Dec. 19, 2024

ABSTRACT Background Generative artificial intelligence (AI) represents a significant technological leap, with platforms like OpenAI's ChatGPT and Baidu's Ernie Bot at the forefront of innovation. This technology has seen widespread adoption across various sectors society is anticipated to revolutionise educational landscape, especially in domain tertiary education. However, there gap understanding factors influencing university students' behavioural intention use generative AI, leading hesitation its adoption. Objectives The primary objective this study was investigate that influence engage utilise AI. sought delve into fundamental reasons obstacles students encounter when contemplating for their academic endeavours. Methods used quantitative research design, utilising revised version Unified Theory Acceptance Use Technology 2 (UTAUT2) model. Data were collected from sample 380 Changsha, capital city Hunan China. Partial least squares structural equation modelling (PLS‐SEM) analyse relationships between variables model, which included performance expectancy (PE), effort (EE), social (SI), facilitating conditions (FC), learning value, habit intention. Results analysis revealed PE EE have direct impact on value. Additionally, SI FC found directly affect formation habit. Among these factors, value emerged as most potent predictor Habit also demonstrated significant, albeit smaller, effect Conclusions study's findings underscore importance driving AI among students. Efforts enhance could significantly increase uptake higher Furthermore, role habit, while less pronounced, suggests consistent exposure can foster greater inclination towards These insights provide foundation targeted interventions aimed improving integration application within settings. Stakeholders, including educators, policymakers designers leverage create an environment conducive effective

Language: Английский

Citations

5
Factors influencing technology integration among mathematics educators in South Africa: A modified UTAUT2 perspective DOI
Antony Musasa, Jameson Goto, Geoffrey Lautenbach

et al.

Contemporary Educational Technology, Journal Year: 2025, Volume and Issue: 17(2), P. ep564 - ep564

Published: Jan. 28, 2025

Educators must effectively integrate technology into their teaching practices in today’s technology-driven world. This study investigated factors influencing integration among mathematics educators Gauteng secondary schools South Africa. The unified theory of acceptance and use technology, extended by adding the technological pedagogical content knowledge (TPACK) framed study. Data was collected using an online questionnaire from 309 educators. Exploratory confirmatory factor analyses were used to validate verify measurement model. structural equation modelling indicated that hedonic motivation (HM), performance expectancy (PE) TPACK influenced behavioral intention (BI) technology. TPACK, facilitating conditions (FC), effort (EE), social influence (SIN), descriptive norms (SID) habit (HT) (BU) integration. second-order all constructs contributed Still, most important, with highest explained variance 64.4%, followed EE, FC, HM HT, which had variances above 50%. BI BU, PE less than 50% variance. Our findings could provide insights future interventions for effective in-service educator training.

Language: Английский

Citations

0
The effect of innovation performance on the adoption of human resources analytics in business organizations DOI Creative Commons
Eithel F. Bonilla-Chaves, Pedro R. Palos‐Sánchez, José Antonio Folgado-Fernández

et al.

Electronic Research Archive, Journal Year: 2024, Volume and Issue: 32(2), P. 1126 - 1144

Published: Jan. 1, 2024

<abstract> <p>Our study objective is to examine the determinants that influence adoption of human resource (HR) analytics, along with external variable called Innovation Performance. The research model was developed by adapting theoretical unified theory acceptance and use technology (UTAUT) adding variable, data collected using a survey at Amazon Mechanical Turk (MTurk) in USA. Initially, total 602 responses were obtained. Finally, 554 questionnaires obtained after information quality filters for debugging. This reveals main on HR analytics exerted performance expectancy, social influence, facilitating conditions, innovation behavioral intention. Likewise, innovative performance, behavior intention are major influences Use Behavior. found from an empirical analysis generalized structured component (GSCA) software package shows, tabled data, relationships model. into Analytics investigated standard UTAUT Performance business organization.</p> </abstract>

Language: Английский

Citations

3
Using the UTAUT-TPACK model to explain digital teaching behaviour of elementary school mathematics teacher DOI
Xin Tang, Zhiqiang Yuan,

Haibin Kuang

et al.

Asia Pacific Journal of Education, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: Aug. 6, 2024

The appropriate use of digital technology in the classroom is very helpful to improve mathematics teaching elementary school. However, current situation school teachers' behaviour not optimistic. This study analysed factors influencing behaviour. A revised unified theory acceptance and (UTAUT) model with technological pedagogical content knowledge (TPACK) was used understand questionnaire survey conducted Hunan province China. Three hundred teachers provided valid data. partial least squares structural equation modelling (PLS-SEM) approach analyse It found that TPACK facilitating conditions positively significantly affected teachers, biggest influential factor. research results have important implications for improving promoting transformation education.

Language: Английский

Citations

3
What influences vocational college students’ actual use of mobile learning? An empirical study using the PLS-SEM method DOI Creative Commons
Qihua Tan,

Xuefei Lin,

Li Wei

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Abstract The vulnerable group of students in vocational institutions seems to have received insufficient attention today's world digital change. Thus, this study aimed explore the factors influencing college students’ actual use mobile learning, and examine moderating effect gender. For purpose, technology acceptance model was adopted. There were 319 from Liaoning (China), these data analyzed using a partial least squares structural equation modeling (PLS-SEM) method. results showed that behavioral intention positively significantly affected their learning. Based on multi-group analysis, it indicated gender had significant relationship between perceived ease usefulness attitude intention. This provides empirical evidence revisiting rationality classic model. Additionally, findings are also beneficial for leaders teachers promote learning an era global technological

Language: Английский

Citations

0
Factors affecting teachers’ use of digital resources for teaching mathematical cultures: An extended UTAUT-2 model DOI
Jinhai Liu, Qin Dai, Jihe Chen

et al.

Education and Information Technologies, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 28, 2024

Language: Английский

Citations

3
How much C is in TPACK? A systematic review on the assessment of TPACK in mathematics DOI Creative Commons
Alina Kadluba, Anselm Strohmaier, Christian Schons

et al.

Educational Studies in Mathematics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 7, 2024

Abstract Teachers need technological pedagogical content knowledge (TPACK) for teaching with technology, and its assessment is crucial research practice. Previous literature reviews on TPACK were not specific to a area (e.g., mathematics), although, by definition, the framework includes content-specific facets. Consequently, requirements could differ depending content. Further, reliable of mathematics-specific depends quality test instruments used, but there no consensus type used in past studies. This systematic review adds existing focusing mathematics, investigating study characteristics , instrument operationalizations TPACK. Regarding characteristics, findings reveal an increase number studies conducted across various countries worldwide. As researchers frequently self-developed assess TPACK, often without providing information reliability or validity measures. operationalizations, more than half self-report scales followed observations material analyses, while tests hardly used. Additionally, assessments typically referred domain mathematics as whole instead subdomains mathematics. The results raise questions regarding comparability

Language: Английский

Citations

0