| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 226 | 81 | 99 | 24 |
| Social Sciences and Humanities | 306 | 12 | 28 | 13 |
The main research concerns discussed in the journal are Multimedia, Educational technology, Human–computer interaction, Artificial intelligence and Context (language use). Some problems in Multimedia that were presented in the journal overlapped with concepts under Engineering education, Distance education and The Internet, World Wide Web, Mobile device. The work on The Internet presented in IEEE Transactions on Learning Technologies focuses on Remote laboratory in particular.
The main emphasis of it is the research on World Wide Web, emphasizing the topic of Personalization. It explores topics in Educational technology which can be helpful for research in disciplines like Active learning, Experiential learning, Knowledge management and Cooperative learning, Teaching method. IEEE Transactions on Learning Technologies focuses on Human–computer interaction but the discussions also offer insight into other areas such as Collaborative learning, User interface, Set (psychology) and Visualization.
The work on Collaborative learning tackled in it brings together disciplines like Synchronous learning, Collaborative software and Team learning. The research on Artificial intelligence tackled can also make contributions to studies in the areas of Machine learning, Task (project management), Task analysis and Natural language processing. The work tackled in IEEE Transactions on Learning Technologies goes beyond the discipline of Context (language use) as it also encompasses Learning analytics.
The most cited publications primarily tackle Multimedia, Educational technology, Human–computer interaction, Knowledge management and Cooperative learning. While the journal publications focused on Educational technology, they were also able to explore topics like E-learning (theory), Context (language use), Teaching method and World Wide Web. Machine learning and Artificial intelligence are some topics wherein Context (language use) research discussed in the most cited articles has an impact.
The journal aims to foster the development of research in Task analysis, Human–computer interaction, Data science, Artificial intelligence and Context (language use). It focuses on Human–computer interaction but sometimes tackles the closely related topic of Collaborative learning which is concerned with Learning Management and Eye tracking. The research on Data science featured in it combines topics in other fields like Learning environment, Educational technology and Virtual reality.
Educational technology research featured in it incorporates concerns from various other topics such as Noise measurement, Noise level and Perceived learning. It explores issues in Artificial intelligence which can be linked to other research areas like Machine learning, Dropout (neural networks) and Natural language processing. Most of the works presented in it deals with The Internet but it intersects with the subject of Multimedia.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in IEEE Transactions on Learning Technologies (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in IEEE Transactions on Learning Technologies (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 20.83% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 10.53% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.89% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.79% of all publications and 65.79% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
The increasing influence of technology on education has opened up numerous career opportunities in the discipline of Learning Technologies. For instance, research positions are available in diverse areas such as Multimedia, Educational Technology, Human-Computer Interaction, Artificial Intelligence, and Context (language use).
Additionally, there are promising educational roles for those interested in Engineering Education, Distance Education, and Internet-based education platforms and applications. There are also distinct career pathways in Task Analysis, Data Science, Collaborative Software, and Synchronous Learning, among others.
Those considering a career in this dynamic field may wonder how long does it take to become a teacher in Texas or other areas of the United States. Depending on the chosen pathway, the required education level, and the licensing requirements, the duration can vary greatly. However, the commitment is often rewarded with the opportunity to contribute to significant advances in the world of education and technology.
Wannisa Matcha;Noraayu Ahmad Uzir;Dragan Gasevic;Abelardo Pardo
(2020)Susan Bull
(2020)John Saint;Alexander Whitelock-Wainwright;Dragan Gasevic;Abelardo Pardo
(2020)Wookhee Min;Megan H. Frankosky;Bradford W. Mott;Jonathan P. Rowe
(2020)Fabio Buttussi;Luca Chittaro
(2021)For students pursuing Computer Science in the USA, exploring flexible and accelerated degree options can be a game-changer. Many professionals are turning to online PhD programs to advance their careers without sacrificing work or personal commitments. These programs offer rigorous training but with the convenience of remote learning.
If you're looking for a quicker path to specialization, consider online one year masters programs. They provide an intense, focused curriculum that prepares students for leadership roles in tech industries within a short timeframe.
To maximize earning potential, understanding which degrees offer faster returns is essential. Exploring quick degrees that pay well can help you make informed decisions about your education and career path.
Lastly, knowing the college majors that align with your interests and market demands is crucial. Computer Science remains a top choice, but related fields like data science, cybersecurity, and software engineering also offer promising opportunities.
