Computers & Education has been leading the field of computers in education for over 40 years, during which time it has developed into a well-known journal with significant influences on the ...educational technology research community. Questions such as “in what research topics were the academic community of Computers & Education interested?” “how did such research topics evolve over time?” and “what were the main research concerns of its major contributors?” are important to both the editorial board and readership of Computers & Education. To address these issues, this paper conducted a structural topic modeling analysis of 3963 articles published in Computers & Education between 1976 and 2018 bibliometrically. A structural topic model was used to profile the research hotspots. By further exploring annual topic proportion trends and topic correlations, potential future research directions and inter-topic research areas were identified. The major research concerns of the publications in Computers & Education by prolific countries/regions were shown and compared. Thus, this work provided useful insights and implications, and it could be used as a guide for contributors to Computers & Education.
•Conduct a structural topic modeling based analysis of Computers & Education.•Identify major contributors and visualize the scientific collaborations.•Identify predominant research topics and potential future directions.•Visualize topical distributions of major contributors.
Given the importance of word knowledge for second language acquisition, there is always a need for effective word-learning approaches from language learners. With the rapid development of educational ...technologies, game-based learning is emerging into a field with considerable potential, within which, digital game-based vocabulary learning has accrued increasing attention from language learners, educators and researchers. The present research reviews studies on digital game-based vocabulary learning from five perspectives: a general overview of published studies, digital games for vocabulary learning, theoretical frameworks, research issues and findings, and implications. Using specific criteria for article selection, 21 publications in SSCI journals were finalized for the systematic review. Findings revealed 10 types of digital games dominate the field, and these generally demonstrate positive effects in promoting short-term and long-term vocabulary learning, facilitating reading and listening comprehension, increasing motivation and engagement, decreasing anxiety and fostering interactions among learners. These findings further render implications that are meaningful for vocabulary learning and game design.
The British Journal of Educational Technology (BJET) has been active in the field of educational technology since 1970. To celebrate its 50th anniversary and to demonstrate a comprehensive overview ...of the field, we conducted a bibliometric analysis of the 3710 publications in this journal from 1971 to 2018 as indexed in the Web of Science with full bibliographic information. This study aimed to (1) identify the publication and citation trends, (2) explore the distribution of paper types, (3) recognize the most relevant countries/regions, affiliations and authors, and (4) reveal relevant thematic features by analyzing publication s and titles with the use of word cloud analysis and topic modeling analysis. The results highlighted several research hotspots and emerging topics such as Technology‐enhanced classroom pedagogy, Blended learning, Online social communities, Mobile assisted language learning, Game‐based learning and Socialized e‐learning.
With the increasing use of Artificial Intelligence (AI) technologies in education, the number of published studies in the field has increased. However, no large-scale reviews have been conducted to ...comprehensively investigate the various aspects of this field. Based on 4,519 publications from 2000 to 2019, we attempt to fill this gap and identify trends and topics related to AI applications in education (AIEd) using topic-based bibliometrics. Results of the review reveal an increasing interest in using AI for educational purposes from the academic community. The main research topics include intelligent tutoring systems for special education; natural language processing for language education; educational robots for AI education; educational data mining for performance prediction; discourse analysis in computer-supported collaborative learning; neural networks for teaching evaluation; affective computing for learner emotion detection; and recommender systems for personalized learning. We also discuss the challenges and future directions of AIEd.
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss ...function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs.
Innovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and ...facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example,
Interactive and multimedia learning
,
STEM (science, technology, engineering, and mathematics) education
,
Attendance and attention recognition
,
Blended learning for smart learning
, and
Affective and biometric computing
. Furthermore, several emerging topics were identified, for example,
Smart learning analytics
,
Software engineering for e-learning systems
,
IoT (Internet of things) and cloud computing
, and
STEM education
. Additionally, potential inter-topic directions were highlighted, for instance,
Attendance and attention recognition
and
IoT and cloud computing
,
Semantics and ontology
and
Mobile learning
,
Feedback and assessment
and
MOOCs (massive open online courses) and course content management
, as well as
Blended learning for smart learning
and
Ecosystem and ambient intelligence
.
Financial news articles are believed to have impacts on stock price return. Previous works model news pieces in bag-of-words space, which analyzes the latent relationship between word statistical ...patterns and stock price movements. However, news sentiment, which is an important ring on the chain of mapping from the word patterns to the price movements, is rarely touched. In this paper, we first implement a generic stock price prediction framework, and plug in six different models with different analyzing approaches. To take one step further, we use Harvard psychological dictionary and Loughran–McDonald financial sentiment dictionary to construct a sentiment space. Textual news articles are then quantitatively measured and projected onto the sentiment space. Instance labeling method is rigorously discussed and tested. We evaluate the models’ prediction accuracy and empirically compare their performance at different market classification levels. Experiments are conducted on five years historical Hong Kong Stock Exchange prices and news articles. Results show that (1) at individual stock, sector and index levels, the models with sentiment analysis outperform the bag-of-words model in both validation set and independent testing set; (2) the models which use sentiment polarity cannot provide useful predictions; (3) there is a minor difference between the models using two different sentiment dictionaries.
The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intelligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) ...technologies or application programs in educational settings to facilitate teaching, learning, or decision making. With the help of AI technologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions. Although AIED has been identified as the primary research focus in the field of computers and education, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disciplinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective of educational needs. We propose a framework to show the considerations of implementing AIED in different learning and teaching settings. The structure can help guide researchers with both computers and education backgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particular interest to this journal. Finally, we describe the type of articles we like to solicit and the management of the submissions.
Artificial Intelligence (AI) plays an increasingly important role in language education; however, the trends, research issues, and applications of AI in language learning remain largely ...under-investigated. Accordingly, the present paper, using bibliometric analysis, investigates these issues via a review of 516 papers published between 2000 and 2019, focusing on how AI was integrated into language education. Findings revealed that the frequency of studies on AI-enhanced language education increased over the period. The USA and Arizona State University were the most active country and institution, respectively. The 10 most popular topics were: (1) automated writing evaluation; (2) intelligent tutoring systems (ITS) for reading and writing; (3) automated error detection; (4) computer-mediated communication; (5) personalized systems for language learning; (6) natural language and vocabulary learning; (7) web resources and web-based systems for language learning; (8) ITS for writing in English for specific purposes; (9) intelligent tutoring and assessment systems for pronunciation and speech training; and (10) affective states and emotions. The results also indicated that AI was frequently used to assist students in learning writing, reading, vocabulary, grammar, speaking, and listening. Natural language processing, automated speech recognition, and learner profiling were commonly applied to develop automated writing evaluation, personalized learning, and intelligent tutoring systems.
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Many studies have highlighted the importance of personalized learning, and most current e-learning systems are able to personalize materials, activities, etc., based on individualized ...learner-factors. However, none of the extant word-learning systems provides a personalized learning experience that is guided by a comprehensive word learning theory. In this study, we develop such a system based on Nation and Webb's checklist for technique feature analysis - a thorough set of factors that promote effective word learning. This system recommends personalized word learning tasks based on the technique feature analysis scores of different tasks and user models. To examine the effectiveness of the proposed system, we conducted an experiment among 105 English learners, grouped them into three teams randomly, and asked them to learn forty target words through three approaches: a non-personalized approach, a personalized approach guided by a partial version of the technique feature analysis, and a personalized approach guided by the full list of the technique feature analysis. Significant differences were observed among the effectiveness of the three approaches in promoting word learning, with the personalized approach guided by the complete checklist leading to the best learning performance. It is therefore suggested that e-learning systems should be designed based on comprehensive learning theories.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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