Discovering Trends of Mobile Learning Research Using Topic Modelling Approach

Hamzah, Almed; Hidayatullah, Ahmad Fathan; Persada, Andhika Giri

doi:10.3991/ijim.v14i09.11069

Cited by 8 publications

(7 citation statements)

References 27 publications

(30 reference statements)

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“…Technology mobility involves the use and adjustment of all mobile features to the learning design. In general, the goal is to make the learning paradigm maximally integrated to the digital learning environment [4].…”

Section: Resultsmentioning

confidence: 99%

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Educational Experience in the Mobile Learning Environment: Consumer Behavior Perspective

Volkovitckaia

Tikhonova

Kolosova

2020

Int. J. Interact. Mob. Technol.

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<p class="0abstract">The development of digital technologies necessitates the formation of digital literacy and entrepreneurship skills in the modern "digital generation". The study has as the objective to identify the aspects of consumer demand for educational services in the process of testing the conceptual model of mobile learning adapted to the modern business environment in the context of digital transformations of the socio-economic life. The concept of an educational course based on the Sakai mobile learning platform with a focus on the formation of digital literacy in the business segment has been proposed in the study. The educational and applied course "Entrepreneurship in the Digital Mode" was tested within the educational programs of the State University of Management (Lyubertsy, Russia) and Herzen State Pedagogical University of Russia (St. Petersburg, Russia). The experiment made it possible to identify key aspects of consumer demand for distance education based on the characteristics of the participants' perception of the advantages and effectiveness of mobile learning. Most participants confirmed their professional achievements and the fact of the implementation of personal educational goals. Mobile learning was described as an easy, comfortable, interesting, innovative and modern method of learning compared to traditional classroom lessons. In the process of mobile learning, students demonstrated an increase in personal responsibility and self-organization while practical classes and the synergy of the educational content with real business cases greatly contributed to their involvement and productivity. Until now, research has not dealt with real measurable in terms of real income from specific activities, the results of the application of mobile learning. The contribution of this study to the area under study is to establish pedagogical demands that can be satisfied with mobile learning.</p>

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Section: Resultsmentioning

confidence: 99%

“…Mobile learning is a new phenomenon in the educational context which is being rapidly spread [4]. Mobile applications for educational purposes have provided users with the opportunity to easily interact with information anywhere and anytime.…”

Section: Introductionmentioning

confidence: 99%

Educational Experience in the Mobile Learning Environment: Consumer Behavior Perspective

Volkovitckaia

Tikhonova

Kolosova

2020

Int. J. Interact. Mob. Technol.

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“…An LDA technique based on a topic modelling methodology was selected in mobile learning research to find the topic trends [64]. Out of 50 topics extracted from the LDA, 25 topics were selected and grouped into three dimensions of technology, learning and learners in that mobile learning.…”

Section: ) Non-probabilistic Modelsmentioning

confidence: 99%

“…Overall, LDA techniques [54], [56], [64]- [68], [70], [72], [75], [76], [83], [84] were the most used topic modelling methodology due to their generative process with the disambiguation of words in each topic and precise alignment of keywords to topics that may closely reflect the original collection.…”

Section: ) Non-probabilistic Modelsmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik

Tao

et al. 2022

IEEE Access

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Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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“…Lamba and Madhusudhan [3] presented a topic modeling using LDA to help librarians identify the trending themes in the library and information science field area. Hamzah et al [4]…”

Section: Introductionmentioning

confidence: 99%

Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

Nastiti

Hidayatullah

Pratama

2021

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Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.

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Discovering Trends of Mobile Learning Research Using Topic Modelling Approach

Cited by 8 publications

References 27 publications

Educational Experience in the Mobile Learning Environment: Consumer Behavior Perspective

Educational Experience in the Mobile Learning Environment: Consumer Behavior Perspective

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

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