The impact of cloud technology and the MatLab app on the academic performance and cognitive load of further mathematics students

Feng, Tingting

doi:10.1007/s10639-023-12386-0

Educ Inf Technol

2023

DOI: 10.1007/s10639-023-12386-0

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The impact of cloud technology and the MatLab app on the academic performance and cognitive load of further mathematics students

Tingting Feng

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2024

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Cited by 2 publications

References 37 publications

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Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Zhu,

Chau,

Mokmin

2024

Sci Rep

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Adaptive microlearning has emerged as a crucial approach for enhancing the working skills of in-service personnel. This study introduces the design and development of an innovative adaptive microlearning (AML) system and investigates its effectiveness compared to a conventional microlearning (CML) system. The main distinguishing feature of an AML system from a CML system is its adaptive features that tailor the learning experience to individual needs, including personalized content delivery, real-time feedback, and adaptive learning paths. A quasi-experimental study involving 111 in-service personnel (N AML = 56, N CML = 55) was conducted. ANCOVA results confirmed that the AML system significantly reduced unnecessary cognitive load due to inappropriate instructional design (mean difference of -20.02, p < 0.05) and significantly improved learning adaptability (mean difference of 40.72, p < 0.05). These findings highlight the potential of adaptive microlearning systems to overcome barriers to effective learning, thereby supporting lifelong learning and professional development in various working contexts.

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Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Zhu,

Chau,

Mokmin

2024

Sci Rep

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Digital technologies in linguistic education: Experience of development and implementation

Riezina,

Yarova

2024

Turkish Journal of Education

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The aim of this study was to share our experience of developing a digital Natural Language Processing Tool and its implementation in the process of training future linguists. In this article, we demonstrate the process of creating the web application SENTIALIZER, which is a multilingual Sentiment Analysis Tool developed with the help of the Python programming language and its libraries NLTK, BS4, TextBlob, Googletrans. The integration of Sentiment Analysis Tools into the educational framework is relied on the Unified Theory of Acceptance and Use of Technology (UTAUT) as its foundation. The results show that students see the prospects of using Sentiment Analysis Tools in their educational and professional activities, are ready to use them in the future, but are not ready to participate personally in projects to develop and improve such technologies. The reasons for this attitude are discussed. The presented study has a clear focus on student learning outcomes, which is an important criterion for the successful integration of technology into the educational process.

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The impact of cloud technology and the MatLab app on the academic performance and cognitive load of further mathematics students

Cited by 2 publications

References 37 publications

Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Digital technologies in linguistic education: Experience of development and implementation

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