Use of Virtual Tools in Teaching-Learning Processes: Advancements and Future Direction

Botero-Gómez, Vanessa; Ruiz-Herrera, Luis Germán; Arias, Alejandro Valencia; Díaz, Alejandra Romero; Garnique, Juan Carlos Vives

doi:10.3390/socsci12020070

Cited by 7 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IoT stands poised to realize this vision and address pertinent challenges, creating a superior alternative to traditional elearning systems [26][27][28][29][30]. However, it is worth noting that developing nations such as Saudi Arabia have yet to fully embrace the IoT within their educational infrastructures [31][32][33][34], and technologies such as e-learning [35], m-learning [36], ubiquitous learning [37], and virtual teaching/learning methods [38] have not brought about the expected transformative impact in higher education. This study aims to establish a robust strategy to enhance the integration of the IoT within HEIs, particularly in developing countries.…”

Section: Introductionmentioning

confidence: 99%

IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia

Ali,

Madni,

Jahangeer

et al. 2023

Sustainability

View full text Add to dashboard Cite

The realm of the Internet of Things (IoT), while continually transforming as a novel paradigm in the nexus of technology and education, still contends with numerous obstacles that hinder its incorporation into higher education institutions’ (HEIs) e-learning platforms. Despite substantial strides in IoT utilization from industrialized nations—the United States, the United Kingdom, Japan, and China serving as prime exemplars—the scope of its implementation in developing countries, notably Saudi Arabia, Malaysia, Pakistan, and Bangladesh, lags behind. A significant gap exists in research centered on the trajectory of IoT integration within e-learning systems of economically disadvantaged nations. Specifically, this study centers on Saudi Arabia to illuminate the main factors catalyzing or encumbering IoT uptake within its HEIs’ e-learning sector. As a preliminary step, this research has embarked on an exhaustive dissection of prior studies to unearth critical variables implicated in the IoT adoption process. Subsequently, we employed an inferential methodology, amassing data from 384 respondents in Saudi Arabian HEIs. Our examination divulges that usability, accessibility, technical support, and individual proficiencies considerably contribute to the rate of IoT incorporation. Furthermore, our data infer that financial obstacles, self-efficacy, interactive capability, online surveillance, automated attendance tracking, training programs, network and data safeguarding measures, and relevant tools significantly influence IoT adoption. Contrarily, factors such as accessibility, internet quality, infrastructure preparedness, usability, privacy concerns, and faculty support appeared to have a negligible impact on the adoption rates within HEIs. This research culminates in offering concrete recommendations to bolster IoT integration within Saudi Arabian HEIs, presenting valuable insights for government entities, policy architects, and HEIs to address the hurdles associated with IoT implementation in the higher education sector.

show abstract

Section: Introductionmentioning

confidence: 99%

IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia

Ali,

Madni,

Jahangeer

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

Limits and Development of an Educational Platform for Automation and ENSAP Integrating Moodle Regarding Client Experience

Arias,

Laos,

Pacheco

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

A comprehensive bibliometric and content analysis of artificial intelligence in language learning: tracing between the years 2017 and 2023

Rahman,

Raj,

Tomy

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

The rising pervasiveness of Artificial Intelligence (AI) has led applied linguists to combine it with language teaching and learning processes. In many cases, such implementation has significantly contributed to the field. The retrospective amount of literature dedicated on the use of AI in language learning (LL) is overwhelming. Thus, the objective of this paper is to map the existing literature on Artificial Intelligence in language learning through bibliometric and content analysis. From the Scopus database, we systematically explored, after keyword refinement, the prevailing literature of AI in LL. After excluding irrelevant articles, we conducted our study with 606 documents published between 2017 and 2023 for further investigation. This review reinforces our understanding by identifying and distilling the relationships between the content, the contributions, and the contributors. The findings of the study show a rising pattern of AI in LL. Along with the metrics of performance analysis, through VOSviewer and R studio (Biblioshiny), our findings uncovered the influential authors, institutions, countries, and the most influential documents in the field. Moreover, we identified 7 clusters and potential areas of related research through keyword analysis. In addition to the bibliographic details, this review aims to elucidate the content of the field. NVivo 14 and Atlas AI were used to perform content analysis to categorize and present the type of AI used in language learning, Language learning factors, and its participants.

show abstract

Use of Virtual Tools in Teaching-Learning Processes: Advancements and Future Direction

Cited by 7 publications

References 47 publications

IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia

IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia

Limits and Development of an Educational Platform for Automation and ENSAP Integrating Moodle Regarding Client Experience

A comprehensive bibliometric and content analysis of artificial intelligence in language learning: tracing between the years 2017 and 2023

Contact Info

Product

Resources

About