Autonomy Learning in The Praxis of Language Education: A Systematic Review

The rapid increase of technology has transformed the praxis of language education and linked to the construct of autonomy learning. It is believed that the great technology accommodated the language learners to achieve their goals and aims. However, this statement is still debatable in language instruction. Given the previous consideration, the purpose of this study was to explore by reviewing 23 articles focusing on the autonomy learning issues that scholars or researchers discussed in their papers. The used research method in this manuscript was a systematic review. This study investigated six prior subheadings: the change number varies by year, the foci and purposes, countries where the articles were published, the applied research methods and data collection tools, the occupied sample group, sample size, and the adopted method theoretical frameworks and dimensions. The data collection was taken by categorizing the six subheadings and analyzing them by presenting bar graphics, figure maps, and tables. The study revealed various insights related to autonomy learning, such as still in the recent trending issue, applicable in multiple approaches, potentially in the sundry of theoretical frameworks, elaborating in numerous dimensions, with ample applied research methods, and so forth. In addition, the implication of this study considerably can be useful for the freshmen or sophomore researchers to gain state of the art or novelty before conducting research or publishing papers specifically on the topic of autonomy learning in the praxis of language education.Keywords: Autonomy Learning; Language Education; Systematic Review

Through an English Teacher’s Eyes: Self-Directed Learning and Advising in Language Education E-Conference

In this review, I aim to convey my ideas on the Self-Directed and Advising in Language Education Conference from an English teacher’s point of view. I watched both plenary sessions and some of the concurrent sessions in which I learned a lot. I will briefly mention the topics in the plenary sessions and concurrent sessions, my related ideas, and the implications I drew. I attended the conference as an English teacher seeking solutions for learners’ language learning issues and the specific problems that I personally faced during distance education times. This event encouraged me to reflect more deeply about education during post-COVID times, which, I believe, underlines the importance of self-directed learning and advising in language learning.

A Research Agenda for Implementing Augmented Reality in Ab Initio Pilot Training

Augmented reality applications have the potential to change the learning experience and outcomes for the next generation of pilots. This study aimed at identifying learning issues related to ab initio flight training and exploring the potential of augmented reality in tackling them. A survey was conducted with 43 pilots (31 men, 12 women) and seven flight instructors (six men, one woman). The results show the contents of the ab initio flight course that were most difficult to learn according to the pilots’ and instructors’ experience. In addition, the participants assessed the training conditions they experienced. After familiarization with typical use cases of augmented reality, the pilots and flight instructors indicated flight training elements that could benefit from augmented reality. The results are used to specify a research agenda for developing augmented reality applications for future ab initio pilot training.

A Comparative Study of Parameters Measuring in Data Mining Function Using SVM

Due to the vast amount of data available on the internet nowadays, it is necessary to categorise the data, and fast, accurate, and resilient algorithms for data analysis are required. Support vector machines (SVMs) are a form of machine learning technique that is commonly used to solve a variety of statistical learning issues. It's been designed as a reliable categorization tool, and it's especially useful when there's a lot of data. Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, with the goal of steadily improving accuracy. Algorithms are trained to create classifications by using statistical approaches. These should ideally have an impact on important growth measures. In this study, we found that employing the Support Vector Machine technique provides the best accuracy and efficiency for our dataset. Our work is based on the evaluation of parameters like accuracy, recall and precision.

From the Normal to the New Normal: Reflecting Metamorphosis in Pedagogies

The study aims to shed some light on pedagogical and learning issues that have cropped up during the pandemic for transitioning from traditional classrooms to entirely virtual learning. The study reflects how the paradigm shift can directly or indirectly affect the soft skills of all parties involved in the success or the failure of an educational system. An in-depth focus group discussion methodology was used to collect information in this qualitative study. Thirty faculty who taught online courses at the tertiary level were selected for the discussions. They provide significant insights about how the fully online learnings continued longer than the anticipated timelines impact the pedagogical value and the futurity of an individual’s educational goals. The study shows that embracing the virtual approach in teaching can promote teachers’ innovativeness by using various new applications and digital tools but bring new challenges for dealing with diverse learning styles

From the Normal to the New Normal: Reflecting Metamorphosis in Pedagogies

The study aims to shed some light on pedagogical and learning issues that have cropped up during the pandemic for transitioning from traditional classrooms to entirely virtual learning. The study reflects how the paradigm shift can directly or indirectly affect the soft skills of all parties involved in the success or the failure of an educational system. An in-depth focus group discussion methodology was used to collect information in this qualitative study. Thirty faculty who taught online courses at the tertiary level were selected for the discussions. They provide significant insights about how the fully online learnings continued longer than the anticipated timelines impact the pedagogical value and the futurity of an individual’s educational goals. The study shows that embracing the virtual approach in teaching can promote teachers’ innovativeness by using various new applications and digital tools but bring new challenges for dealing with diverse learning styles.

Zenius Stimulation during a Pandemic: Framing of Republika.co.id and Liputan6.com on Distance Learning Issues in the Pandemic Era

Indonesia’s education sector has also been affected by the Covid-19 pandemic. The learning process has changed from conventional to online learning. Many students find it challenging to capture and understand their knowledge because they are accustomed to the learning process through face-to-face interactions with teachers. Teachers need to create a teaching system that is more efficient than just completing the curriculum. Regarding this problem, Zenius, as an educational application from Indonesia, issued the “Zenius Untuk Guru” platform, which helps teachers create learning systems. The product launch attracted influential online media in Indonesia, Liputan6.com and Republika.co.id. The interesting point about the launch platform is that these two online media display the related news with different framing. This research using Entman’s framing theory to describe how the two media perceive the platform. The results show that Liputan6. com frames Zenius commercially, while Republika.co.id communicates Zenius as a problem solver to improve teacher literacy in less technologically literate areas. Zenius also helps Indonesia in equalizing education to remote areas of Indonesia.

Certification Systems for Machine Learning: Lessons from Sustainability

Forthcoming (open access) in Regulation and GovernanceAbstract—The increasing deployment of machine learning systems has raised many concerns about its varied negative societal impacts. Notable among policy proposals to mitigate these issues is the notion that (some) machine learning systems should be certified. In this paper, we illustrate how recent approaches to certifying machine learning may be building upon the wrong foundations and examine what better foundations may look like. While prominent approaches to date have centered on networking standards initiatives led by organizations including the IEEE or ISO, we argue that machine learning certification may be better grounded in the very different institutional structures found in the sustainability domain. We first illustrate how policy challenges of machine learning and sustainability have significant structural similarities. Like many commodities, machine learning is characterized by difficult or impossible to observe credence properties, such as the characteristics of data collection, or carbon emissions from model training, as well as value chain issues, such as emerging core-periphery inequalities, networks of labor, and fragmented and modular value creation. We examine how focusing on networking standards, as is currently done, is likely to fail as a method to govern the credence properties of machine learning. While networking standards typically draw their adoption and enforcement from a functional need to conform in order to participate in a network, salient policy issues in machine learning issues benefit from no such dynamic. Finally, we apply existing research on certification systems for sustainability to the qualities and challenges of machine learning to generate lessons across the two, aiming to inform design considerations for emerging regimes.

Learning Challenges During Pandemic Situation: Lithuanian University Case

Learning issues in the context of the pandemic situation will be discussed in the paper. Research problem – learners lack relevant information, knowledge and skills about creativity for adequate problem solving. Distant learning requires to create environment suitable for students' needs in the case of a critical situation (for example, Covid 19 pandemic). It motivates students to change their attitudes and behaviours while combining different actions based on their self-awareness and creativity. Therefore, students need to develop their creativity skills to facilitating problem solving. Moreover, it is significant to raise students understanding about themselves to make adequate decisions for efficient learning during the lockdown because of Covid-19 pandemic. The aim of the research is to find out and discuss students' learning difficulties and experiences during a pandemic, and to find out how creativity can contribute to problem-solving. For this reason, it is essential that learners concentrate on how to develop their creativity for better learning in uncertainty conditions. Used research methods were scientific literature analysis, quantitative (questionnaire) study and descriptive analysis. The findings of the study demonstrate that learners have major distant learning difficulties and various social psychological problems during the pandemic situation in Lithuania. Moreover, the results show that learners lack understanding about the importance of knowledge and opportunities for creative problem solving during distant learning processes in the pandemic situation.