Koh, Elizabeth

Various learning resources are designed and used to enhance students’ English speaking skills because the speaking skill plays a significant role in communication and in language acquisition. For the past few decades, the rapid development of information and communication technologies have made a significant contribution to English language teaching and learning. In other words, the development of technology has been the driving force that leads to a transformation in English language learning. Particularly, the mushrooming online learning resources have been providing valuable support for students when it comes to learning English speaking skills. Nonetheless, not many studies have looked into how low-proficiency English learners utilized the online technology resources for English speaking skills. Thus, this study aims to study the online technology resources used by low-proficiency English learners to improve their communication and speaking skills. This study adopted a quantitative approach. The instrument used to elicit the data for the study was questionnaire. A total of 62 undergraduate students were recruited as the respondents of this study. The results of the study showed that YouTube (58%) was one of the most popular online technology resources used by students to improve their communication and speaking skills. Most of them perceived that online technology resources helped to build their confidence and reduce their speaking anxiety (32%). However, internet/Wi-Fi (26%) problem was the biggest challenge encountered by them. It is believed that this paper could make a contribution to the body of knowledge as it explored students’ experiences in using online technology resources. Lastly, the recommendations for future studies and limitations were also discussed in this paper.

Networked learning provides opportunities for learners to develop their critical thinking, an important 21st century competency, through dialogue with fellow learners to consider other perspectives and negotiate and critique ideas and arguments. However, much extant literature has not examined networked learning environments among younger learners nor the optimal conditions for enhancing critical thinking. Therefore, a study was carried out to investigate these conditions. A learning analytics networked learning environment was designed and 264 secondary three students participated in the 10-week long intervention as part of their English curriculum. Individual and collective social network metrics, critical reading scores, and self-reported survey data were used to quantitatively evaluate students’ critical reading performance in relation to their participation in networked learning. Results highlight several optimal conditions, notably that it is not just participation of the learner that enhances critical thinking but the learners’ reciprocity in replying and the distance of those posts in the network. Discussions and implications of the findings follow to provide insightful understanding of how the rich and complex settings of networked learning can enhance critical thinking capacities in secondary schooling.

Interest in Generative AI (GenAI) chatbots has exploded across the education industry, subsequently expanding in sophistication and usage. However, students' perceptions on utilizing this technology ultimately determines how well these chatbots promote learning. Hence, this study examines the factors influencing how Singapore secondary school-aged computing students perceive using MyBotBuddy (MBB), a GenAI chatbot, to assist them with programming tasks. A thematic analysis determined that students' perceptions were influenced by reliability, utility, cognitive effort needed, satisfaction, and enjoyment. The study contributes to the literature on GenAI chatbots supporting secondary school programming students and may guide the development of such tools in secondary school classrooms more effectively.

Artificial Intelligence (AI) in education and learning analytics (LA) tools are increasingly being developed and implemented to enhance teaching and learning within Singapore’s education landscape. This paper provides an overview of key AI in education and LA projects and initiatives in Singapore, organized by the types of technology. The identified projects and initiatives involve a range of techniques and systems to achieve personalized learning, improve student engagement, optimize resources, and also predict student success among a list of educational outcomes. We briefly describe each identified project before further discussing the collective impact and limitations, as well as the implications for Singapore and her education environments. Overall, this paper seeks to provide an overview of the state and use of AI and LA in education-related projects within Singapore and highlights the need for further research and development in this area to fully realize the potential of these technologies for improvement of teaching and learning.

We adapt a policy-oriented analytical framework to consider important teacher policy leverages that can help to generate successful learners - the teacher policy strategies framework (TPSF). The TPSF comprises a set of micro-layered strategies focusing on the personal growth of the individual teacher. At the same time, there are macro- layered strategies that are more cognizant of the wider system ecology. These micro- and macro-layer policy imperatives collectively and coherently drive a teacher development agenda. The micro-layer policies are: teacher recruitment; initial teacher preparation; compensation and incentives; career development structures; and, professional development and continuous learning. The micro-layer policies are supported by macro- layer policies that build a robust and coherent ecology of capacity building, identity formation, and change drivers needed to align and steer teacher education. For macro- layer policies we highlight: accountability, performance management and evaluation; school leadership; teacher symbolism; policy integration, alignment and coherence; and, collective teachers’ voice.

Teamwork is a necessary competency for students that is often inadequately assessed. Towards providing a formative assessment of student teamwork, an automated natural language processing approach was developed to identify teamwork dimensions of students’ online team chat. Developments in the field of natural language processing and artificial intelligence have resulted in advanced deep transfer learning approaches namely the Bidirectional Encoder Representations from Transformers (BERT) model that allow for more in-depth understanding of the context of the text. While traditional machine learning algorithms were used in the previous work for the automatic classification of chat messages into the different teamwork dimensions, our findings have shown that classifiers based on the pre-trained language model BERT provides improved classification performance, as well as much potential for generalizability in the language use of varying team chat contexts and team member demographics. This model will contribute towards an enhanced learning analytics tool for teamwork assessment and feedback.

WiREAD+ is a web-based collaborative critical reading and learning analytics environment to scaffold learning and motivate students to develop richer dialogue and quality interactions with peers around multimodal texts. This paper reports on the pilots to scale up the use of WiREAD+ beyond the original context of Secondary School English Language (EL) learning to three distinct educational settings, namely, EL in a primary school, English Literature in a junior college (pre-university), and a tertiary-level Discourse Studies course. We report on learners’ perceptions in response to the use of the system and reflect on the potential and challenges in scaling up the system across different educational contexts, specifically on the three augmentations to the system which we have designed to improve its scalability readiness. Drawing from the findings of the pilot studies, we briefly discuss how we can support the wider adoption and deployment of the system across schools and settings.

Educational success has been largely defined by academic scores in many educational systems, and teachers are frequently held accountable for their students’ scores. These accountability-driven school systems impinge on teachers to enact time-tested effective and efficient pedagogical approaches. In such a context, it is onerous for teachers to adopt alternative approaches. This paper traced how an experienced language teacher, schooled in the discourses and practices of neoliberalism, made a transformation into a teacher of constructivist bent. It explored the transformation of the teacher’s beliefs and practice as a result of reflecting collaboratively with a small team. The findings help to provide a broad understanding of how collaborative reflection can develop teachers’ ability to engage in reflection, and illuminate the potential it has in transforming the teaching practices set against the background of neoliberalism. This finding has relevance for Asian countries which are similarly engulfed in a neoliberal discourse.

ICT-based learning innovations have augmented learning in many ways; however, scaling innovations are complex. Scaling in education is not a linear replication of products but an iterative process with an emphasis on the capacity of people. To provide further insights, a case study of the spread of a learning initiative in Singapore is elaborated on. The resultant findings build on a translational and scaling framework, developed by researchers at the Office of Education Research (OER), NIE. The framework, Scaling Change through Apprenticing and Ecological Leadership (SCAEL), is a context-sensitive model demonstrating the approaches that learning innovations can diffuse and spread through the multiple leadership roles of stakeholders in the ecological system.