Computational Modeling of Phonological Learning

2019 ◽  
Vol 5 (1) ◽  
pp. 67-90 ◽  
Author(s):  
Gaja Jarosz
Keyword(s):  
Computational Modeling ◽  
Language Learning ◽  
Learning Task ◽  
Phonological Structure ◽  
Phonological Learning ◽  
Research Areas ◽  
Phonological Knowledge ◽  
Naturally Occurring ◽  
Language Data ◽  
Recent Developments

Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in computational modeling that have made connections between fully explicit theories of learning, naturally occurring corpus data, and the richness of psycholinguistic and typological data possible. These advances fall into two broad research areas: ( a) the development of models capable of learning the quantitative, noisy, and inconsistent patterns that are characteristic of naturalistic data and ( b) the development of models with the capacity to learn hidden phonological structure from unlabeled data. After reviewing these advances, the article summarizes some of the most significant consequent discoveries.

scholarly journals A Seamless Learning Design for Mobile Assisted Language Learning: An Iranian Context

2016 ◽  
Vol 9 (5) ◽  
pp. 206 ◽  
Author(s):  
Elham Mohammadi Foomani ◽  
Mohsen Hedayati
Keyword(s):  
Language Learning ◽  
Communication Technology ◽  
Information Communication ◽  
Design Based Research ◽  
Mobile Assisted Language Learning ◽  
Seamless Learning ◽  
Teachers And Students ◽  
Recent Developments ◽  
E Learning ◽  
Iranian Context

<p>Recent developments in information communication technology (ICT) have resulted in a paradigm shift in e-Learning and there is a growing interest in developing design-based research (DBR) focusing on learners and their involvement in knowledge sharing in a contextualized mode. The present study reports a mobile-assisted language learning (MALL) design with a focus on contextualized student-created content having a seamless learning approach. The students in this study (N= 24) used their mobile devices to take photos and create artifacts to represent English idioms and share them on Padlets with their peers for further discussion and feedback. In the first four weeks of the study, students were taught English idioms and in the following next two weeks they created and shared their own artifacts to represent the learnt idioms. The post-study reflections and results of the interviews and obtained from students and the teacher at the end of study revealed that they favor and support greater learner autonomy achieved by learner-generated context (LGC) which bridges the in-classroom and out-of-classroom learning. The article also highlights the necessity of reconceptualization of teachers and students’ perceptions of mobile use in language learning in Iran.</p>

scholarly journals Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2717
Author(s):  
Nusrat Rouf ◽  
Majid Bashir Malik ◽  
Tasleem Arif ◽  
Sparsh Sharma ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Digital Libraries ◽  
Stock Price ◽  
Ensemble Methods ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Stock Market Prediction ◽  
Research Areas ◽  
Recent Developments

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

scholarly journals Self-Regulation: Why is it Important for Promoting Learner Autonomy in the School Context?

2014 ◽  
pp. 342-356 ◽  
Author(s):  
Yoshiyuki Nakata
Keyword(s):  
Language Learning ◽  
Learning Strategies ◽  
Self Regulation ◽  
School Context ◽  
Language Education ◽  
Learner Autonomy ◽  
Foreign Language Education ◽  
Teacher Autonomy ◽  
Language Learning Strategies ◽  
Research Areas

Both researchers and practitioners in the field of foreign language education are increasingly interested in the notions of self-regulation and learner autonomy. Indeed, there is a growing body of evidence highlighting the importance of self-regulation in promoting learner autonomy. For many practitioners, an important question to be addressed is how to help learners become more self-regulated in order to promote their learner autonomy. As it stands, however, the majority of learner autonomy research following this line of inquiry has been conducted within the framework of language learning strategies. Although learner autonomy research conducted within the framework of language learning strategies has to some extent contributed to addressing the question above, it has not provided enough guidance to practitioners and practitioner trainers, especially those who are struggling to promote autonomy in their learners in the EFL school context, which is full of constraints and limitations and does not allow much freedom. The present paper attempts to fill this gap, first by comparing the roots and the avenues of development of these two (essentially related but) distinct research areas—self-regulation and learner autonomy—and then by integrating the notion of self-regulation within the theoretical framework of learner autonomy, together with other notions of agency, teacher autonomy and scaffolding.

3D bioprinting for meniscus tissue engineering: a review of key components, recent developments and future opportunities

2021 ◽  
Author(s):  
Xavier Barceló ◽  
Stefan Scheurer ◽  
Rajesh Lakshmanan ◽  
Cathal J Moran ◽  
Fiona Freeman ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Regenerative Medicine ◽  
Meniscal Repair ◽  
Spatial Patterning ◽  
3D Bioprinting ◽  
Research Areas ◽  
Engineered Tissues ◽  
Meniscal Tissue ◽  
Meniscus Tissue ◽  
Recent Developments

3D bioprinting has the potential to transform the field of regenerative medicine as it enables the precise spatial patterning of biomaterials, cells and biomolecules to produce engineered tissues. Although numerous tissue engineering strategies have been developed for meniscal repair, the field has yet to realize an implant capable of completely regenerating the tissue. This paper first summarized existing meniscal repair strategies, highlighting the importance of engineering biomimetic implants for successful meniscal regeneration. Next, we reviewed how developments in 3D (bio)printing are accelerating the engineering of functional meniscal tissues and the development of implants targeting damaged or diseased menisci. Some of the opportunities and challenges associated with use of 3D bioprinting for meniscal tissue engineering are identified. Finally, we discussed key emerging research areas with the capacity to enhance the bioprinting of meniscal grafts.

Overview of Machine Learning Approaches for Wireless Communication

2021 ◽  
pp. 1579-1597
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

scholarly journals The Role of a Domain-specific Language Mechanism in Learning Natural and Unnatural Stress

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Angela C. Carpenter
Keyword(s):  
Language Learning ◽  
Learning Task ◽  
Target Language ◽  
Domain Specific Language ◽  
Training Procedure ◽  
The Novel ◽  
Specific Language ◽  
Domain Specific ◽  
Artificial Language Learning ◽  
French And English

AbstractIn an artificial language-learning task, two groups of English and French participants learned one of two language rules: 1) stress the first heavy (CVC) syllable, else the first syllable, or, 2) stress the first light (CV) syllable, else the first syllable. French and English participants were chosen to compare learning outcomes by speakers of different native stress systems, fixed and variable. Participants were trained on the target language by listening to a set of nonsense familiarization words exemplifying the stress rule. This was followed by a forced-choice task to choose the correct version of the words they had just learned. Following the training procedure, participants were tested on novel words with the same stress pattern to which they were familiarized. The result of the novel word testing was that the natural rule with stress on heavy syllables was learned significantly better than the unnatural, stress light syllables, rule. To account for the learnability of both the natural and the unnatural rules, I argue for the interaction of a general cognitive mechanism that facilitates learning in general and a domain-specific language mechanism that can access universal phonological principles to aid in learning a natural language rule.

Technology-enhanced language learning: Recent developments and what to expect next

2018 ◽  
Vol 7 (2.28) ◽  
pp. 147
Author(s):  
Vilma Mikašytė
Keyword(s):  
Language Learning ◽  
Foreign Languages ◽  
Technology Enhanced Learning ◽  
Future Studies ◽  
Key Characteristics ◽  
Recent Developments ◽  
Educational Potential ◽  
Near Future ◽  
Enhanced Learning ◽  
Insight Into

One of the key characteristics that are expected from a contemporary instructor is being able to creatively implement innovation in his/her day-to-day teaching activities. An example of such innovative approaches to teaching is the integrated teaching of, for instance, STEM subjects with foreign languages (FL). The latter can be successfully achieved via technology-enhanced learning (TEL) approach. Currently, there are numerous apps and platforms available or still being developed for teaching STEM subjects, which could be combined with learning FLs in order to ensure successful learning outcomes even more. However, the full educational potential of TEL tools for teaching FLs should be investigated and disclosed beforehand. To this end, the present paper provides an overview of the most recent developments in technology-enhanced language learning (TELL). It firstly surveys state-of-the art and then gives an insight into what to expect from the near future studies on TELL. 

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