Multi-Level ICT integration for diffusing complex technology-mediated pedagogical innovations
This research seeks contemporary understanding of how we can develop teachers' Technological-Pedagogical-Content Knowledge (TPACK) when scaling pedagogical innovation to different contextual situations. Teaching with technology has long been a wicked problem as the nature of technology is ''protean'' (used in versatile ways), ''unstable'' (rapidly changing) and ''opaque'' (elusive backend mechanisms), resulting in multifarious complexities which are exacerbated when its use is scaled and situated within the broader socio-cultural context of diverse learning ecologies. Scaling innovations to new contexts is rarely a mere supplanting of what works at the seeding school to new pedagogic sites with less hospitable conditions. It entails the perpetual marshalling of resources to mitigate the enfolding tensions that can emanate from many incompatibilities at the new site. Herein lies the tensions of diffusion: the conflation between fidelity adherence and localised accommodation. The purpose of this research then is to study how teachers' three knowledge bases - technology, pedagogy and content - can be holistically developed so that the core ingredients of success at the seeding school can be sustained and not ''amputated'' at new innovation sites. Informed by complexity theory, the qualitative case study will employ the complexity constructs of ''distribution'', ''enaction'' and ''emergence'' to examine how teachers' epistemic resources are distributed during the knowledge creation process and how teachers leverage on TPACK to enact co-designed lessons or improvise their lessons in-situ. More importantly, by studying the diffusion process of Seamless Science Learning project from the seeding FutureSchool (ICT prototype school) to another non-affiliated mainstream primary school, the study aims to articulate how teachers' reified TPACK can emerge through feedback loops between components of TPACK and interaction with other actors in an ecological complex adaptive system. The study will also articulate the implications of such interaction on the translations of teachers' professional learning and the conceptual model related to challenges of nurturing readiness. It has the potential to inform policymakers on the theoretical principles of professional learning support which may culminate into ensuing successful uptake of innovations. By inter-meshing three domains: complexity theory, TPACK and scaling, this project can provide novel methodological perspective to how the inter-locking influences underpinning teacher's TPACK can be studied. Through cross-case analysis, the proposed study aims to reify both ''local divergence'' and the ''noncontextually bounded'' theoretical principles about scaling school-based intervention.
Development of a tool for decision making on subject placement in secondary schools
Investigating the generation-first-instruction-later method for its effects on learning and transfer: A proposal to study analogical reasoning as the generation task
Productive Failure1,2 studies have shown that working on generative and complex activities prepares students for learning from subsequent instruction (i.e., delayed instruction). Under a delayed instruction setting, this study investigated the degree of freedom of generation and the level of task complexity as two key attributes of a preparatory task. The purpose was to make preliminary exploration on whether there is a boundary at which the benefit of a more generative task over a less generative task, such as compare and contrast, may disappear when the task complexity reduces.
Cultivating laterality in learning communities in Singapore education system: Scaling of innovation through networked learning community
Cultivating teachers to be active and agentic learners is crucial for contemporary teacher education (Lipponen & Kumpulainen, 2011). Those teachers’ qualities are essential in preparing students’ future readiness in an increasingly complex world (P21 Framework Definitions, 2015). In fact, both learning principles and evidence from practice inform us that purposeful collaboration in networked learning communities (NLCs) encourage teacher agency to learn (Lieberman & Wood, 2003; Muijs, West & Ainscow, 2010). As a complement to the literature, we are interested in the development of social relationships among teachers, which enables and facilitates their learning. We propose “laterality” – the relations and networks among peers (e.g., teachers) as an important concept to characterize NLCs.
Studies on laterality, which have shown to support teacher learning, are usually found in the decentralized systems where individuals are the best entities to form these networks to support each other’s growth (Hargreaves & Goodman, 2006; Muijs et al., 2010). Thus, developing laterality from the bottom-up becomes natural in the decentralized contexts (Granovetter, 1973). Despite considerable theoretical promise of laterality and its increasing prevalence in practice, we wonder whether teacher laterality matters in the centralized education systems, and if it does, how it grows.
Leadership in times of pandemics: Reflections from Singapore
The COVID-19 pandemic is compressing the timeline for Singapore’s digital transformation in education. Reflecting on the implementation of Home-Based Learning (HBL) during the pandemic, we examine three barriers that inhibit digital transformation and technological implementation in education with leadership considerations: the first order barrier is infrastructural and can be mitigated by leadership foresight; the second order barrier concerns design capabilities of teachers which can be mitigated by tight-but-loose calibration; and the third order barrier deals with sustainability which can be mitigated by ecological leadership. The tight-but-loose calibration optimises the ‘tight’ system-led innovations such as Student Learning Space (SLS) for efficient deployment and for equitable access of high quality online resources for students; and ‘loose’ opportunities for teacher-led innovations on learning designs within and beyond system-led innovations to nurture teacher agency and professionalism. We posit that ecological leadership is key to sustaining deep change together with the ‘tight-but-loose’ system calibration.
Integrating artificial intelligence into science lessons: Teachers’ experiences and views
Background
In the midst of digital transformation, schools are transforming their classrooms as they prepare students for a world increasingly automated by new technologies, including artificial intelligence (AI). During curricular implementation, it has not made sense to teachers to teach AI as a stand-alone subject as it is not a traditional discipline in schools. As such, subject matter teachers may need to take on the responsibility of integrating AI content into discipline-based lessons to help students make connections and see its relevance rather than present AI as separate content. This paper reports on a study that piloted a new lesson package in science classrooms to introduce students to the idea of AI. Specifically, the AI-integrated science lesson package, designed by the research team, provided an extended activity that used the same context as an existing lesson activity. Three science teachers from different schools piloted the lesson package with small groups of students and provided feedback on the materials and implementation.
Findings
The findings revealed the teachers’ perceptions of integrating AI into science lessons in terms of the connection between AI and science, challenges when implementing the AI lesson package and recommendations on improvements. First, the teachers perceived that AI and science have similarities in developing accurate models with quality data and using simplified reasoning, while they thought that AI and science play complementary roles when solving scientific problems. Second, the teachers thought that the biggest challenge in implementing the lesson package was a lack of confidence in content mastery, while the package would be challenging to get buy-in from teachers regarding curriculum adaptation and targeting the appropriate audience. Considering these challenges, they recommended that comprehensive AI resources be provided to teachers, while this package can be employed for science enrichment programs after-school.
Conclusions
The study has implications for curriculum writers who design lesson packages that introduce AI in science classrooms and for science teachers who wish to contribute to the development of AI literacy for teachers and the extension of the range of school science and STEM to students.
Closing teaching and learning gaps in mathematics classrooms
While national assessments are often held responsible for streaming and students’ placement for their academic advancement, school-based assessment is increasingly recognised as an important and effective way to identify and close teaching and learning gaps. This chapter presents examples to illustrate how assessment is used in the secondary mathematics classroom to enhance students’ learning and at the same time close the teaching and learning gaps. The chapter comprises two parts. Firstly, it differentiates theories of assessment and underscores the importance of using assessment to enhance students’ learning. Secondly, the chapter illustrates how assessment strategies can be devised as an enhancement to students’ learning. Examples of strategies are presented to reveal how teachers identify teaching gaps, adjust their teaching approaches and give targeted feedback to encourage students to take ownership of their learning. In the process, students are encouraged not only to be self-directed in their learning but also serve as instructional resource for their peers in their learning journey.
Unveiling the dynamics of learning behaviors in learning K-12 math: An exploration of an assistments dataset
This study delves into the dynamics between diverse learning behaviors among K-12 students and their learning gains using a dataset of 508 students learning three math skills in ASSISTments. Employing K-means clustering based on students’ initial and final skill mastery alongside their engagement level, three distinct clusters emerged for each skill, revealing varying degrees of learning from ASSISTments. By analyzing decision tree classification models for each skill using affective labels such as boredom and frustration, we hypothesize that students within the same cluster of a skill may exhibit heterogeneous learning patterns that affect their subsequent learning of new skills. Further exploration demonstrates that students who transit between clusters when learning new skills differ significantly in their initial and final mastery of previously learned skills and their affective labels associated with those skills. Regression analysis underscores that students’ initial and final mastery of antecedent skills have some influence on their subsequent mastery of new skills. Unraveling the intricate relationship between student learning behaviors and the effectiveness of ASSISTments offers valuable insights into tailoring AI-enhanced educational tools, not only for learning the current skill but also for preparing for the future learning of new skills.