Reviewerly/exHarmony huggingface dataset:
Exercise parameters influencing exercise difficulty

Macro-adaptive systems aim to assign practice exercises to language learners that match their proficiency levels. While learner-dependent parameters of exercise difficulty need to be considered online, learner-independent parameters can inform an exercise’s difficulty level in a resource-efficient offline procedure. We present an evaluation of learners’ responses to form-based grammar exercises that aims to identify learner-independent exercise parameters affecting exercise complexity. The results indicate that the exercise type can yield coarse-grained complexity estimates, whereas exercise type specific features can inform more fine-grained estimates. For fine-grained estimates, we show that syntactic variants significantly impact exercise difficulty. Since there is strong variation between learning targets and learners with respect to the impact of different exercise parameters on a learner’s performance, exercise difficulty can only be reliably determined if the exercises are created in a systematic way and by also considering characteristics of the learner.
Using Pimsleur for the self-regulated learning of spoken phrases in Brazilian Portuguese: a case study

This case study examines the self-regulated use of Pimsleur, a Language Learning Platform (LLP), as a tool to aid in the acquisition of spoken phrases in Brazilian Portuguese (BP) and their related pronunciation. Like many LLPs, research on Pimsleur is scant, as is the number of studies done on BP compared to other major languages. This study aims to address this gap in research. The participant-researcher completed the Pimsleur program through daily study over a 10-week period, after which quantitative data were collected through a post-test and delayed post-test. The results showed that Pimsleur contributed to the learning of the target phrases in the short term and that the participant produced speech that was highly intelligible, moderately comprehensible, but heavily accented. This shows that Pimsleur can be an effective tool for developing spoken BP and can offer a unique learning experience with its methodology and mobile capability that mitigates some of the issues around mobile-assisted language learning (e.g. app attrition).
Writing with automatic speech recognition: Examining user’s behaviours and text quality (lexical diversity)

This study explores the potential of Automatic Speech Recognition (ASR) as a writing tool by investigating user behaviours (strategies henceforth) and text quality (lexical diversity) when users engage with the technology. Thirty English second language writers dictated texts into an ASR system (Google Voice Typing) while also using optional additional input devices, such as keyboards and mice. Analysis of video recordings and field observations revealed four strategies employed by users to produce texts: use of ASR exclusively, ASR in tandem with keyboarding, ASR followed by keyboarding, and ASR followed by both keyboarding and ASR. These strategies reflected cognitive differences and text generation challenges. Text quality was operationalized through lexical diversity metrics. Results showed that ASR use in tandem with keyboarding and ASR followed by both keyboarding and ASR yielded greater lexical diversity, whereas the use of ASR exclusively or ASR followed by keyboarding had lower diversity. Findings suggest that the integrated use of ASR and keyboarding activates dual channels, thus dispersing cognitive load and possibly improving text quality (i.e. lexical diversity). This exploratory study demonstrates potential for ASR as a complementary writing tool and lays groundwork for further research on the strategic integration of ASR and keyboarding to improve the quality of written texts.
Evaluating the effectiveness of Microsoft Transcribe for automating the assessment of pronunciation in language proficiency tests

Improvements in Automatic Speech Recognition (ASR) have created opportunities for using it as a tool to facilitate second and foreign language (L2) assessment. These technical improvements have not only enabled automation of language proficiency test scoring but also reduced evaluator bias and errors, decreased processing time, and lowered costs for testing organizations. The purpose of this study was to evaluate English as a Second Language (ESL) pronunciation using the ASR feature in the Microsoft 365 product suite, Transcribe (MS-T). The study involved adult ESL learners at a Canadian university that partook in a language proficiency test. We examined the audio recordings of 56 candidates during the pronunciation portion of the test. Building on previous studies that found a strong correlation between scores from Google Voice Typing and human raters, the current study conducted a similar analysis comparing scores derived from MS-T to both human ratings and Google Voice Typing. Our findings indicate that the ASR capabilities of MS-T, similar to Google Voice Typing, can assume an important role in L2 speaking assessment by providing objectivity and reliability to the testing process, expediting scoring, and reducing costs.
Beyond the walls of classrooms: Exploring the pedagogical effectiveness of Text-To-Speech-based Shadowing (TTS-S) on the development of Mandarin tones

This study examines the pedagogical effectiveness of using Text-To-Speech synthesis (TTS) combined with Shadowing (TTS-S) for self-regulated learning of Mandarin tones 1 and 4. The aim is to determine the probability of success of this innovative approach that uses TTS to generate audio for shadowing practice. The research was guided by the following research question: can TTS-S help L2 learners raise their awareness and improve their perception and production of the target Mandarin tones over a period of six weeks? Over six week, ten participants engaged in self-regulated activities using TTS-S to learn the two pronunciation targets. By means of pre-/post-tests (to assess effectiveness in pronunciation) participants were asked to complete: (1) an awareness task in which they verbalized their metacognitive knowledge of Mandarin tones; (2) ABX tasks to assess their perception of Mandarin tones; and (3) a production task to evaluate their production of the target tones. Our findings are inconclusive regarding the effectiveness of TTS-S for improving awareness, perception, and production of Mandarin tones 1 and 4 among L2 learners. They also indicate that while certain aspects of phonological development, specifically production, showed some improvements, the overall impact was not statistically significant.
Machine translation use in the English as a Foreign Language (EFL) classroom

Machine Translation (MT) increasingly has the potential to facilitate or impede second language learning in the English as a Foreign Language (EFL) classroom. To understand teacher and EFL learner perspectives about its use, two surveys of 20 university professors (10 non-native English teachers and 10 native English teachers) and 139 EFL students were carried out. To measure MT’s capacity to promote language awareness, a comparative study with an online dictionary was done, using two translation tasks, a posttest, and a post-questionnaire. The preliminary survey results revealed variations in how learners and teachers perceive text-based machine translation as a valuable tool for language learning and how much they felt its use by students in educational settings should be accepted. Analysis of the posttest also found significant differences in the capacity of DeepL (MT network) to promote language awareness compared to the online dictionary, which aligned with the students’ post-questionnaire feedback. These results emphasise the need for additional research and workplace dialogues going forward, regarding the incorporation and supervision of MT in L2 instruction.
Using generative AI tools and LARA to create multimodal language learning resources for L2 Icelandic

We utilize ChatGPT and other generative AI tools in developing illustrated multimodal resources for learning Icelandic as a second language (L2) via reading in the online platform LARA. These are illustrated short stories in Icelandic created using specific prompts including information about learner levels and age groups. We present a methodology for creating engaging stories and a way to evaluate the quality and suitability of text and images generated by AI. Additionally, we assess how 46 adult learners perceived reading a story at A1 and A2 level in LARA, with a positive vocabulary learning effect. We conclude that generating suitable prompts greatly assists with getting the desired output; however, this is restricted to the language one works with. Much human post-editing is still necessary for Icelandic texts to improve their quality in grammar, vocabulary, and cultural aspects, and their suitability for language teaching.
Harnessing the power of images in CALL: AI image generation for context specific visual aids in less commonly taught languages

This paper explores the application of AI image generation in Computer-Assisted Language Learning (CALL) for Less Commonly Taught Languages (LCTLs). It delves into the potential of text to image generation models in creating context specific visual aids to enhance comprehension and engagement among learners. The integration of AI generated images in a language learning game, Cipher, is discussed, showcasing the benefits and challenges encountered. Learner feedback indicates positive inclinations towards the AI generated images, but also highlights the need for meticulous selection to address biases and stereotypes. Overall, this approach shows promise in creating culturally relevant CALL resources and improving language learning experiences for learners of LCTLs.
Assessing 21st century digital literacies in Japanese higher education

This study examines how the seven digital learner profiles included in the International Society for Technology in Education (ISTE) standards for students apply to 193 participants in Japan, utilizing a questionnaire developed by Mills and White (2023). These profiles explore a broad range of 21st century digital skills, competencies, and literacies essential for learners to be successful in this digital age, and include both creativity and communication. The results revealed positive identification with the learner profiles (M=3.874). While Japanese learners are often found to lack digital skills, the highest mean values were associated with the profiles of Digital Citizen, Knowledge Constructor, and Computational Thinker. In contrast, the profiles of Empowered Learner, Innovative Designer, Creative Communicator, and Global Collaborator demonstrated lower mean values, particularly in more creative and communicative aspects. To ensure students are well-prepared for the digital era, therefore, fostering creativity and communication skills is crucial. There have been a lack of studies that measure the various aspects of 21st century digital skills (van Laar et al., 2020), and this study aims to add to the literature.
Journal of Research, Policy &amp; Practice of Teachers &amp; Teacher Education

The shift from face-to-face to distance learning has challenged English Language teaching and learning. The present study aims to determine the ESL students’ affective filter level and the varied language learning strategies they employed to aid their learning in English online classes. The descriptive-quantitative research design was employed in this study since it used the mean percentage and the mode of the data gathered. The researchers employed a total remuneration sampling technique to select 48 respondents of the study and utilized two research instruments: Modified Attitude Motivation Test Battery (AMTB) and Oxford’s Strategy Inventory for Language Learning (SILL) version 7.0. As a result, it was found that ESL students have a low affective filter in English online classes. However, students were able to come up with their language learning strategies as ways of enhancing their language competence. Therefore, teachers may provide suitable learning activities in online classes, and students need to enhance their usage of language learning strategies to facilitate their learning. Parents may also support and encourage their children's goals to learn the English language.
ASEAN Students' Interest in Learning the Indonesian Language: A Descriptive Study from the Perspective of SEAMEO ASEAN

Language plays a crucial role in the educational environment, whether in schools or universities. Besides the national language, students are also exposed to regional and foreign languages to enhance their language proficiency. Teaching foreign languages encompasses a wide range of options, such as English, Korean, Japanese, and Chinese. Moreover, languages affiliated with ASEAN countries, like Malay, Thai, and Indonesian, have gained popularity. This study employs a descriptive approach to elucidate the findings regarding the interest in learning Indonesian among students in ASEAN nations. Conducted by SEAMEO ASEAN, the research draws its primary data from questionnaires and presentations submitted in September 2022. Participating ASEAN countries in this SEAMEO initiative encompass Brunei Darussalam, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, Timor Leste, and Vietnam and analyzed with descriptive statistical analysis. The study reveals a notable demand for Indonesian language learning among students from ASEAN countries. Indonesian is pursued for various purposes, including professional, socio-cultural, and personal development. Recognizing the interest in Indonesian among ASEAN students provides a foundation for the implementation of Indonesian as a foreign language through the BIPA program. There has not been much research on the interest of Indonesian language among students at the ASEAN level, therefore this research is included in the initial research on Indonesian language internationalization policies starting from the ASEAN level
A Bibliometric Analysis of Instructional Technological Leadership Research Using VOSviewer

This study aims to analyze the development of research on technology-related instructional leadership. The research method used is bibliometric analysis. The articles used in this study were sourced from the Google Schooler database with the help of the Publication of Perish from 2015-2022; 498 papers were found. Data analysis using the VOSviewer application. The results showed that the number of publications decreased yearly, where the most occurred in 2015, namely 102 publications. The results of the density visualization analysis shown in yellow circles indicate the themes most frequently used in recent research, namely instructional technology, technology integration, instructional leadership practice, school leader, transformational leadership, covid, time and student. Meanwhile, based on network visualization, the topic of instructional leadership has a relationship with the term development, effect, effectiveness, instructional leader, instructional leadership, instructional leadership practice, leader, leadership, model, principal, research, school, school leader, school leadership, school principal and transformational leadership. These results illustrate that technology-based instructional leadership can be developed by referring to instructional technology, technology integration, as a term rarely used based on visualization overlay.
Assamese Dialect Identification System using Convolution Neural Networks

Labeling speech in an audio file with appropriate dialect labels is the aim of a dialect identification system. This paper presents a method of using convolution neural networks (CNN) to identify four Assamese dialects: Goalporia dialect, Kamrupi dialect, Eastern Assamese dialect, and Central Assamese dialect. This study employed the speech patterns of four major Assamese regional dialects: the Central Dialects spoken in and around the district of Nagaon; the Eastern Assamese dialect spoken in the districts of Sibsagar and its neighboring areas; the Kamrupi dialect spoken in the districts of Kamrup, Nalbari, Barpeta, Kokarajhar, and some areas of Bongaigaon; and the Goaplari dialect spoken in the Goaplara, Dhuburi, and a portion of Bongaigaon district. Over the course of two hours, audio samples from each of the four dialects were used to train the classifier. Mel spectrogram pictures, which are produced from two to four second divisions of raw audio input with varying audio quality, are used by the CNN. The system's performance is also analyzed in relation to the lengths of the train and test audio samples. The proposed CNN model achieves an accuracy of 90.82 percent, which may be the best when compared to machine learning models.
Clickbait Detection in Indonesia Headline News Using Indobert and Roberta

This paper explores clickbait detection using Transformer models, specifically IndoBERT and RoBERTa. The objective is to leverage the models specifically for clickbait detection accuracy by employing balancing and augmentation techniques on the dataset. The research demonstrates the benefit of balancing techniques in improving model performance. Additionally, data augmentation techniques also improved the performance of RoBERTa. However, it resulted differently for IndoBERT with slightly decreased performance. These findings underline the importance of considering model selection and dataset characteristics when applying augmentation. Based on the result, IndoBERT, with a balanced distribution, outperformed the previous study and the other models used in this research. This study used three dataset distribution settings: unbalanced, balanced, and augmented with 8513, 6632, and 15503 total data counts, respectively. Furthermore, by incorporating balancing and augmentation techniques, the research surpasses previous studies, contributing to the advancement of clickbait detection accuracy, contributing to the advancement of clickbait detection accuracy with 95% accuracy in f1-score with unbalanced distribution. However, the augmentation method in this study only improved the RoBERTa model. Moreover, performance might be boosted by gathering more varied datasets. This work highlights the value of leveraging pre-trained Transformer models and specific dataset-handling techniques. The implications include the necessity of dataset balancing for accurate detection and the varying impact of augmentation on different models. These insights aid researchers and practitioners in making informed decisions for clickbait detection tasks, benefiting content moderation, online user experience, and information reliability. The study emphasizes the significance of utilizing state-of-the-art models and tailored approaches to improve clickbait detection performance.
ANALISIS KEMAMPUAN LITERASI DAN NUMERASI DITINJAU DARI SOAL AKM BERORIENTASI KONTEKS SAINTIFIK DAN SOSIAL BUDAYA PADA MATERI KESETIMBANGAN ASAM DAN BASA

Currently, the MCA (Minimum Competency Assessment) program is part of the National Assessment to measure minimum literacy and numeracy competencies. MCA questions consist of various content, various contexts and several levels of cognitive levels. This quantitative descriptive research is focused on analyzing the literacy and numeracy abilities of students in solving MCA questions that are integrated with scientific and socio-cultural contexts. Data collection was carried out using a questionnaire and MCA test instruments. The results of this study indicate that the minimum competence of students is mostly at the proficient level of 42%. The greatest achievement of the cognitive level of reading literacy is at the level of evaluating and reflecting because the discourse and phenomena presented in the questions are often encountered by students. The lowest achievement is at the understanding level because it requires the ability to analyze and associate information with the concepts that students have. The achievement of the highest cognitive level of numeracy literacy is at the knowing level because students can carry out basic calculation operations and change variables according to the information in the items. The lowest achievement at the reasoning level was because students had never found the type of questions that required the ability to analyze, predict, and take strategies to solve the items..
Measurement Enhancement of Ultrasonic Sensor using Pelican Optimization Algorithm for Robotic Application

HC-SR04 ultrasonic sensor is one of the famous low-cost sensors. It is measured distance from 2 to 400 cm. It depends on ultrasonic sound waves that are sent by an electronic device to determine the distance of the object, and the reflected sound is converted into an electronic signal. This paper proposed a flowchart of the modern optimization method Pelican Optimization Algorithm (POA) to enhance the distance measurement of ultrasonic sensor kind HC-SR04. In addition, the experimental system designed and interfacing between MATLAB with Ardunio is implemented to easily save the measured and desired distances and enter these distances into the POA as pelicans. The error comparison between two methods implemented, one method called the classical method, and another method is proposed named POA. The results show the minimum distance error between ultrasonic and object in POA is less than the error without POA. The best-measured distance results were achieved and approximately equal to the desired distances. The proposed method of POA for ultrasonic distance sensor enhanced by 99.97%. This sensor accurately detects objects from 2 to 400 cm which can be used in a robotic application.
The Application of Fuzzy Logic With the Mamdani Method to Determine the Risk Zone for The Spread of Covid-19 in North Sumatra

The method fuzzy is one part of the fuzzy inference system that is useful for drawing conclusions or the best decision in uncertain problems. Currently, about 200 countries in the world are experiencing the Covid-19 pandemic. Various policies were carried out to overcome this. In addition to the health protocols that are always implemented, the community must also know the conditions of the area where they live so that they can anticipate any activities carried out. The method is used fuzzy to determine the risk zone for the spread of Covid-19 in North Sumatra Province based on 3 variables, namely positive cases, suspected cases, and dead cases. The data used in this study is weekly Covid-19 data from March 2021 to July 2021 for 4 selected areas, namely Medan City, Pematang Siantar City, Simalungun Regency, and Central Tapanuli Regency. All variables are represented using triangular curves and their membership functions are determined. Then the implication function of the min is used to determine the rules used. From the results of the implication function, the composition between all the rules obtained is carried out by taking the maximum rule then that value is used to modify the fuzzy area so that a new membership function is obtained. The method is used centroid to obtain a crisp or the final result is the value of the risk zone. This research also utilizes the Matlab to calculate the results. The results from the use of the Mamdani method will be compared with the real risk zone data so as to produce a 75% average percentage similarity for the data for 4 regions in North Sumatra so it can be concluded that the model made is good enough to determine the risk zone for the spread of Covid-19 in North Sumatra Province.