**Heutagogical Approach in Online English Pronunciation Learning: Student Awareness Survey** ABSTRACT *This study attempts to investigate the enthusiasm for heutagogy or self-determined learning among undergraduate students in learning English Pronunciation during Online Distance Learning (ODL). English language learners have difficulties in English in pronouncing fricative consonants, voiced and voiceless TH, plosive consonants and vowel sounds. Serious speech mistakes can lead to miscommunication since poor pronunciation abilities make it difficult for other people to hear what you are saying. Additionally, students may miss their lecturers' direct explanations of proper pronunciation when taking online English lessons with spotty Internet access. Proper pronunciation can help students participate in conversations, form connections, and improve graduates' employability abilities. In this survey, firstly we aim to get feedback from the students about their self-learning capabilities and experience during ODL in learning English pronunciation. Secondly, we want to know their intentions on using Text-to-Speech and Speech-to-Text in the online application. This survey is a preliminary survey towards the development of a Heutagogical English pronunciation application. An online questionnaire has been disseminated by students and lecturers in UiTM Cawangan Perlis. As a result, 424 participants from various higher education institutions took part in the survey. In conclusion, students had already practiced self-learning capabilities in their ODL and were willing to use online Text-to-Speech and Speech-to-Text features to assist their English pronunciation learning.* *Keywords: English, Heutagogy, Online Distance Learning, Pronunciation, Self-Determined Learning* # INTRODUCTION Online Distance Learning (ODL) has been implemented by higher educational institutions (HEI) amidst the Covid-19 outbreak for teaching and learning including English education. However, a study found that ODL is not fully adapted by the students due lack of self-directed learning skills (Chiu, Lin & Lonka, 2020). Focusing on English learning, Malaysian students have difficulties in English pronouncing fricative consonants, voiced and voiceless “th”, plosive consonants and vowel sounds (Shak, Lee & Stephen, 2016). They struggle to stay up during listening sessions because they are unable to comprehend English pronunciation when it is spoken at a native speaker's normal speed (Hasan, 2000). Because accurate pronunciation is not entirely taught in the classroom and students should make an effort to practice it at their own pace, the speaking syllabus can be thought of as informal learning. (O’neil, 2019). However, N Campos (2021) has claimed that one of the difficulties in learning English is when students become reliant on their teachers, regularly calling them for help or asking them to fix their grammar and pronunciation. According to Winarso (2016), while those who were less prepared for learning tasks struggled or felt frustrated, the students who were more prepared for learning tasks did better. Because of this problem, lecturers should play a significant part in developing strategy-based online instruction during ODL to improve students' skills to continue their studies virtually in productive ways (Qazi et al., 2021). According to the approach's accessibility, it can promote improved communication between lecturers and students (Plitnichenko, 2020). It can prevent students from perceived stress in the classroom if they have poor self-efficacy and finally it leads to an increase in the probability of early withdrawal Burger and Samuel (2017). Therefore, this paper will discuss heutagogical practice in English pronunciation learning among students. Supported by the survey, it could help better findings in this study. # ENGLISH LANGUAGE LEARNING # Since the word English itself is derived from Anglisc, one of three Germanic tribes that invaded England in the fifth century, the English language was known as the language of the Angles (Richard Nordquist, 2020). There are many diverse varieties of English spoken by people around the world, including American, British, and many others. English has long been a universal language. The English language is widely used today, including in education, where the majority of the curriculum has been taught in English since the students were in primary schools (Sheehan, 2021). In other words, students were also required to practice and learn the basics of English languages ever since they were in the first semester of study and most of the subjects that they were participating in were taught in the English language. # A study by Rao (2019) has stated that the reason the English language has been widely used is that it has become the principal medium used in numerous disciplines of education and the sole language where information is preserved in the form of books and journals in both written and electronic form. In Malaysia, it is a requirement for students in higher education institutions to be able to read and write English to grasp the information and educational materials that are mostly provided in that language. The English language has played a big role in education where the majority of publications and education research are published and conducted in English. # HEUTAGOGY Heutagogy or self-determined learning is a learner-centered instructional strategy or method. It acknowledges the students’ capabilities in attempting to integrate knowledge into the learning process (Carpenter & Green, 2017). According to Moore (2020), heutagogy is a perspective for a self-directed approach to learning. The learners pursue the sources of information and will apply what they had learned to their new environment. Hase (2009) described the heutagogy approach as insufficiency in the educational system, particularly the need to offer to learn in a nonlinear manner that allows for more application to the real world. The heutagogy approach can make students be more absorbed with the learning since they spend more time with the learning process. (Hase & Kenyon, 2017). It urged a thinking strategy that optimizes the effort of students to dig knowledge over a material, which allows students to have a better understanding of their surrounding environment than teachers, as it has encouraged the lecturers to explore and think in a larger perspective for their courses. Heutagogy is an excellent practice that has helped students achieve more academic goals and improve their learning skills since it gives teachers the freedom to help students choose their learning goals for related courses. To match the efficacy of the students in their studies has inspired the lecturers to develop a new experimental method of teaching by using heutagogy (Blaschke, 2018; Hase & Kenyon, 2013). Heutagogy also helps students to discover which learning methods work best for them, which can aid in the development of learning abilities that have boosted their productivity.(Blaschke & Hase, 2015; Canning, 2010; Carpenter & Green, 2017; Durkin-Boyle, 2017; Stoszkowski & McCarthy, 2018). It can then be connected to a recent study that revealed that students' degrees of self-reliance have successfully increased as a result of their learning experience. (Brown & Mbati, 2015; Carpenter & Green, 2017; Pal & Roy,2019). **Informal Learning in Heutagogy** According to Training Industry (2021), informal learning is learning that occurs outside of a structured, formal classroom setting where it includes the activities like watching movies, doing self-study, reading articles, engaging in forums and chat rooms, getting help with performances, coaching sessions, and playing games. Hence, the activities of informal learning could be multiplied with the existence of technology which enables the students to receive and transmit information and ideas through visual, audial, and textual modes in improving abilities such as critical thinking, intercultural communication, and cooperative learning. (Zadorozhnyy & Yu, 2020). Once the integration of technology at all levels of education changed educational practices and, in particular, transformed the entire process of learning foreign languages like English, people started to explore informal learning from the perspective of digitalization (Lee, 2017; Lee & Dressman, 2017; Sylvén & Sundqvist, 2012). As a result, many academics claim that employing technology in informal settings for language acquisition allows students to customize their learning and select the most effective way to accomplish their tasks. (Ma, 2017). It is because students who are more exposed to informal learning activities in digital settings have higher levels of productive vocabulary (Sundqvist, 2009; Sundqvist & Wikström, 2015), better grades (Sundqvist & Wikström, 2015), and a higher level of oral proficiency (Sundqvist & Wikström, 2015). (Sundqvist, 2009; Lee & Dressman, 2017). Thus, the rise of technology allows students to acquire and communicate knowledge and ideas in visual, audial, and textual formats, enhancing skills like critical thinking, intercultural communication, and cooperative learning (Zadorozhnyy & Yu, 2020). As a result, it encourages students to learn in a flexible and context-sensitive manner even more (Cook & Gregory, 2018). # SPEECH RECOGNITION TECHNOLOGY Speech recognition, often known as speech-to-text, is the capacity of a machine or programmed to recognize and transform spoken words into legible text. Rudimentary voice recognition software can only identify a small number of words and phrases if they are spoken very clearly. More advanced software can recognize natural speech, multiple accents, and languages (Kiwak, 2021). To help the students to improve their English learning, especially pronunciation skills, many researchers have agreed to include these two tools which are Speech-to-Text (STT) and Text-to-Speech (TTS) in an application. Web Speech API allows an application to integrate voice data into web applications because the Web Speech API is divided into two sections: *SpeechSynthesis* (Text-to-Speech) and *SpeechRecognition* (Asynchronous Speech Recognition) (MDN, 2022). The *SpeechSynthesis* interface is a Text-To-Speech component that allows this project to read out their text content (normally via the device's default speech synthesizer), which was used to access speech synthesis. *SpeechSynthesisVoice* objects represent different voice types, and *SpeechSynthesisUtterance* objects represent different parts of the text that the students want to be spoken. In other words, by passing them to the *SpeechSynthesis.speak()* method, the students can finally speak. The *SpeechRecognition* interface was used to access speech recognition, which allows the device to recognize voice context from an audio input (normally via the device's default speech recognition service) and respond appropriately. In general, this project used the interface's function *Object()* { \[native code\] } to create a new *SpeechRecognition* object, which has several event handlers for detecting speech input via the device's microphone. Text-to-speech (TTS) is a technical tool that generates voice from the supplied input text (Hayashi et al., 2020). A few studies have reported that students achieve better results when the dictation is uttered by TTS instead of humans (Y et al., 2017; T et al., 2012). ![Text Description automatically generated](630f848336b54_media/media/image1.png) **Figure 1:** JavaScript codes Figure 2.1 shows the codes in JavaScript in a web application. JavaScript is essential to connect the codes with speech recognition codes. This way it detects the speech of the students. ![](630f848336b54_media/media/image2.png) **Figure 2:** The features of Text-to-Speech Figure 2.2 depicts the display of the JavaScript Text-to-Speech from the JavaScript results. The interface has a “speak” button where it detects the words from the student's input in the text field and voices them as a speech. This way, students have practiced the right pronunciation since speech recognition detects the English language. # METHODOLOGY This study is a preliminary survey for the development of heutagogical English pronunciation application. The questionnaire includes a few questions in Google Form and has been disseminated using social media and email. The survey was conducted during COVID19 pandemic in Semester Mac-August 2021. The objectives of this survey are to collect their readiness for self-determined learning of English pronunciation. # RESULT As a result, there were 424 respondents took part. In the survey, demographic data from the respondents have been collected and depicted in Figure 3. ![Table Description automatically generated](630f848336b54_media/media/image3.png) **Figure 3:** Respondents’ Demographic Information It can be retrieved that 99.3% of the respondents are university students and including undergraduates only. Instead of UiTM students, 42.7% of the respondents are from other higher educational institutions. Most of them were having their school study in national schools. According to Figure 4, the respondents owned the Malaysian University English Test (MUET) as their English language proficiency exam. ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image4.png) **Figure 4:** English Language Proficiencey Exam ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image5.png) **Figure 5:** Self Evaluation of Writing Skills ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image6.png) **Figure 6:** Self Evaluation of Speaking Skills ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image7.png) **Figure 7:** Self Evaluation of Listening Skills ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image8.png) **Figure 8:** Self Evaluation of Reading Skills As depicted in Figure 5, Figure 6, Figure 7 and Figure 8, we can retrieve 4 categories of English skills that have been self-evaluated by the respondents from rating values 1 (poor) to 5 (excellent) such as Writing, Speaking, Listening and Reading. Most of them are having good skills in all categories. ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image9.png) **Figure 9:** Happiness Index of English ODL During COVID19 In Figure 9, there are 18.6% from the respondents are less happy during their English ODL during COVID19 pandemic. Only 7.5% are very happy with their learning. ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image10.png) **Figure 10:** Effectiveness Index of English ODL During COVID19 In Figure 10, the respondents are mostly moderate to respond about how the English ODL during COVID19 are effective for them. ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image11.png) **Figure 11:** Problem in English ODL During COVID19 ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image12.png) **Figure 12:** ODL vs Face To Face According to Figure 12, respondents have been asked about the ODL is more effective compared to face-to-face. It can be retrieved that all of them have different responses to these questions. ![](630f848336b54_media/media/image13.png) **Figure 13:** Individual Self-Learning Capabilitieson on ODL English Pronunciation In Figure 13, the respondents agree that they are capable to own self-learning capabilities during ODL English pronunciation learning. ![Chart, bar chart Description automatically generated](630f848336b54_media/media/image14.png) **Figure 14:** Assistance Tools for ODL English Pronunciation In Figure 14, majority of respondents agree that they need assistant tool for their ODL English pronunciation. **DISCUSSIONS** This paper has discussed how to heutagogy can be used in ODL English pronunciation learning, From the survey, we can see that the students are very educated and can use English in their learning. The findings from the survey show that the students are already practises self learning in their ODL English pronunciation. They also agree that the need self motivation and self determined when the have class. Eventhough they are many problems they are facing, they can still happy and accept ODL approach in their teaching and learning. **CONCLUSION AND RECOMMENDATIONS** This paper has successfully found that students are not facing problems in having self-determined learning during ODL especially in English pronunciation. However, there are not well equipped with tools to support their learning. Therefore, it is recommended that an application that uses a heutagogical approach such as Speec-to-Text and Text-to\_Speech need to be developed. **REFERENCES** Bogach, N., Boitsova, E., Chernonong, S., Lamtev, A., Lesnichaya, M., Lezhenin, I., . . . Blake, J. (2021). Speech Processing for Language Learning: A Practical Approach to Computer-Assisted Pronunciation Teaching. *Electronics, 10*(3). doi:10.3390/electronics10030235 Mann, S., Ker, G., Ellis, K., & Franklin, T. (2018). Self-Determined Computing Education: An Independent Learning Pathway Approach. *2018 International Conference on Learning and Teaching in Computing and Engineering (LaTICE)*. doi:10.1109/latice.2018.000-1 Moore, R. (2020). Developing lifelong learning with heutagogy: contexts, critiques, and challenges. *Distance Education, 41*(3), 381-401. doi:10.1080/01587919.2020.1766949 Proenca, J., Raboshchuk, G., Costa, A., Lopez-Otero, P., & Anguera, X. (2019). Teaching American English pronunciation using a TTS service. *SLaTE 2019: 8th ISCA Workshop on Speech and Language Technology in Education*. doi:10.21437/slate.2019-11 Kementerian Kesihatan Malaysia. (2021, May 9). *Covid-19 Malaysia*. Situasi Terkini COVID-19 Di Malaysia. Subramaniam, P. (2020b, August 17). *4th industrial revolution-Urgency to fast track 4IR*. The Edge Markets. https:/[/www.theedg](http://www.theedgemarkets.com/article/4th-)e[markets.com/article/4th-](http://www.theedgemarkets.com/article/4th-) industrial-revolution-urgency-fast-track-4ir Mann, S., Ker, G., Ellis, K., & Franklin, T. (2018). Self-Determined Computing Education: An Independent Learning Pathway Approach. *2018 International Conference on Learning and Teaching in Computing and Engineering (LaTICE)*. Published. Huang, X., Baker, J., & Reddy, R. (2014). A historical perspective of speech recognition. Communications of the ACM, 57(1), 94–103. Chen, H. H. J. (2011). Developing and evaluating an oral skills training website supported by automatic speech recognition technology. ReCALL, 23(1), 59– 78. Nidhom, A. M., Smaragdina, A. A., Putra, A. B. N. R., Syafrudie, H. A., Suswanto, H., & Putro, S. C. (2020). Framework for Intelligent Application Heutagogy Based Education 3.0 and Lesson Study Components for VHS students. 2020 International Seminar on Application for Technology of Information and Communication (ISemantic). Published. Relationship Between Assessment for Learning and English Learning Motivation of University Students in Blended Learning Environment. (2020). Journal of Education and Practice. Published. Northrop, L., & Andrei, E. (2018). More Than Just Word of the Day: Vocabulary Apps for English Learners. The Reading Teacher, 72(5), 623–630. Nasir, N. F. W. M., & Ismail, I. S. (2016). Student Readiness of a Newly-Designed Blended Learning English Language Proficiency Course in UiTM. *7th International Conference on University Learning and Teaching (InCULT 2014) Proceedings*, 777–788. Küfi, E. Z., & ÖZgür, B. (2009). Web 2.0 in learning English: the student perspective. Procedia - Social and Behavioral Sciences, 1(1), 326–330. Daud, N. A., Sahari@Ashaari, N., & Muda, Z. (2013). An Initial Model of Persuasive Design in Web based Learning Environment. Procedia Technology, 11, 895–902. Kahraman, H. T., Sagiroglu, S., & Colak, I. (2010). Development of adaptive and intelligent web-based educational systems. *2010 4th International Conference on Application of Information and Communication Technologies*. Published. Liakin, D., Cardoso, W., & Liakina, N. (2014). Learning L2 pronunciation with a mobile speech recognizer: French /y/. CALICO Journal, 32(1), 1–25. Moore, R. L. (2020). Developing lifelong learning with heutagogy: contexts, critiques, and challenges. Distance Education, 41(3), 381–401. Marin, R., Vila, P., Sanz, P., & Marzal, A. (2002). Automatic speech recognition to teleoperate a robot via Web. IEEE/RSJ *International Conference on Intelligent Robots and System*. Published. Nisimura, R., Miyake, J., Kawahara, H., & Irino, T. (2008). Speech-to-text input method for web system using JavaScript. *2008 IEEE Spoken Language Technology Workshop*. Published. 4th industrial revolution - Urgency to fast track 4IR. (2020, August 16). The Edge Markets. E. (2019, July 30). What is Informal Learning? LearnUpon. P. (2021, April 19).How Can Social Media Help Education? Psychreg. Lisa, Marketing Manager. (2021, May 20). 10 Challenges of E-Learning during COVID-19. Jellyfish.Tech. [https://jellyfish.tech/10-challenges-of-e-learning- during-covid-19/](https://jellyfish.tech/10-challenges-of-e-learning-%20during-covid-19/) MySQL Tutorial - Tutorialspoint. (2021). Tutorialspoint. Rungta, K. (2021, July 1). Your First PHP Web Application using MySQL and PHP with Examples. Guru99. [https://www.guru99.com/php-practical- example.html](https://www.guru99.com/php-practical-%20example.html) Creating a Simple Webpage with HTML and CSS. (2016, January 18). Self-Taught Coders. (2021, August 9). JavaScript Speech Recognition Example (Speech to Text). Studytonight. What is CSS? (2021). Tutorialspoint.Com. Web Speech API - Web APIs | MDN. (2022, January 19). MDN Web Docs. Hayashi, T., Yamamoto, R., Inoue, K., Yoshimura, T., Watanabe, S., Toda, T., Takeda, K., Zhang, Y., & Tan, X. (2020). Espnet-TTS: Unified, Reproducible, and Integratable Open-Source End-to-End Text-to-Speech Toolkit. ICASSP 2020 - 2020 IEEE International Conference on Acoust Speech and Signal Processing (ICASSP). Published.