A personalised mobile learning system for promoting STEM discipline teachers' TPACK development

Kajonmanee, Tanachai; Chaipidech, Pawat; Srisawasdi, Niwat; Chaipah, Kornchawal

doi:10.1504/ijmlo.2020.10026336

Cited by 2 publications

(2 citation statements)

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“…Researchers denote that the technological pedagogical content recognition system (TPACK) can be applied to increase the competence of STEM teachers. To improve TPACK for educators, scholars propose to use an m-learning system that personalizes digital learning content by analyzing learning styles, content attributes, as well as capabilities of a mobile device and network [34]. Several researchers have made an attempt to integrate STEM and TPACK framework as a means to advance education.…”

Section: Discussionmentioning

confidence: 99%

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

Shchedrina

Galkina

Petunina

et al. 2020

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Over the past few years, the teaching process has transformed radically under significant investments in information and communication technologies. In this context, mobile technologies emerge as an innovative educational tool. Mobile devices are being used by a vast number of so-called "digital generation" representatives in daily life and educational activities. It has been proven that the implementation of mobile technologies in education contributes to the increase of students' motivation, participation in the learning process, and faster acquisition of professional competencies. These technologies take the role of the "driving force" in training. However, their comprehensive understanding is essential to use them effectively. The objective of this study was to analyze the intensity of mobile technologies' use and investigate their evolution in higher education using the example of the I.M. Sechenov First Moscow State Medical University. The research sample was represented by 151 students (data collected for 2015/2016) and 274 students (data for 2019/2020). The average participants' age corresponded to 19.8. The study also involved three experts who were required to evaluate questionnaires completed in Google Forms. The scientific method of the study was based on the organized examination, strict control over the involved respondents, and quantitative research. The research outcomes were analyzed through Chi-Square goodness-of-fit test. The target questions of the survey were rated on a 5-point Likert scale. According to the study results, 95% of the respondents used mobile devices for educational purposes, of which 65% agreed with the convenience of having course materials on a mobile device. In the 2015/2016 academic year, the share of students using smartphones for learning comprised 10.4%, while in 2019/2020, their percentage increased to 61.5%. The study findings will be useful for university teachers and representatives of educational institutions' administration.</p>

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Section: Discussionmentioning

confidence: 99%

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

Shchedrina

Galkina

Petunina

et al. 2020

Int. J. Interact. Mob. Technol.

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“…TAM is majorly being implemented online or instead e-learning in learning institutions, as it has been proven to be the most effective way of student engagement (Castañeda & Selwyn, 2018). E-learning, at times, referred to as online learning, has been applied in practically all higher learning institutions all over the world during the past 15 to 20 years (Chaipidech & Srisawasdi, 2016). The ability for students to use an online system that manages their coursework, materials, chats, and tests online has allowed it to surpass conventional teaching and learning techniques in the meantime.…”

Section: Students' Engagement Using Technologymentioning

confidence: 99%

AI ChatGPT and student engagement: Unraveling dimensions through PRISMA analysis for enhanced learning experiences

Abu Khurma,

Albahti,

Ali

et al. 2024

CONT ED TECHNOLOGY

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This PRISMA-based literature review aims to assess and analyze the measurement of student engagement dimensions within AI ChatGPT interactions. The central question is how to effectively evaluate these dimensions using established methods and leverage insights to enhance AI ChatGPT’s capacity to foster student engagement. The systematic review of PRISMA methodology identifies 16 relevant peer-reviewed research. All relevant and eligible research according to PRISMA methodology are analyzed to comprehend the intricacies of student engagement in AI ChatGPT interactions. The synthesis of these findings unveils the current state of knowledge on AI ChatGPT’s influence on student engagement and uncovers opportunities for future research. This review underscores AI ChatGPT’s potential as an educational tool, offering personalized experiences that bolster student engagement and learning outcomes. The systematic review established that, even though using ChatGPT has many advantages such as enhancing student engagement and academic involvement and supporting inquiry-based learning. However, there are some negative aspects such as lacking empathy and human emotions, limited contextual understanding, increased technology dependence and possibility of Inaccurate or Biased Information. In summary, this PRISMA-based review contributes to understanding the measurement of student engagement within AI ChatGPT. It identifies best practices, laying the foundation for further research and development. By optimizing AI ChatGPT’s effectiveness, educators and developers can craft more engaging and tailored learning experiences, ultimately enhancing educational outcomes.

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A personalised mobile learning system for promoting STEM discipline teachers' TPACK development

Cited by 2 publications

References 0 publications

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

AI ChatGPT and student engagement: Unraveling dimensions through PRISMA analysis for enhanced learning experiences

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