Huang, David Junsong

Successful analogical reasoning requires an analogue in a source domain to have high degrees of structural and surface similarity with a learning task in a target domain. It also requires learners to have sufficient source- and target-domain knowledge. We review the literature and speculate that “less” might create “more”; in some situations, analogies that have fewer degrees of similarity may be more effective for learning. In this exploratory study, we engaged eight school leaders in dyads to develop a bottom-up perspective on innovation diffusion through analogical reasoning. The qualitative data in the study appears to echo our speculation. The dyads that have less prior target-domain knowledge face challenges with regard to innovation diffusion when they learn with analogues that have more degrees of similarity – both structural and surface. They, however, are able to learn with analogues that have fewer degrees of similarity. Learning was shown to take place when the dyads reflected on an analogue first, before they compared the analogue and innovation diffusion to make any analogical inferences. Although constrained by the exploratory nature of the study, the findings provide preliminary evidence that “less” is possible to create “more” in analogical reasoning under certain conditions, implying an interesting direction for experimental examination in future.

If school diffusion of innovation is viewed as complex adaptive process, how shall we prepare school leaders to be effective diffusion decision makers? Coming from the epistemological belief that knowledge is subjective and embodied, this paper proposes to use Agent Based Modeling (ABM) for learning by focus on learning to “do” diffusion of innovation rather than learning about diffusion of innovation. We therefore recommend to engage school leaders in iterative agent based model development process and to couple it with their interaction in real world diffusion system. With feedback from real world system used for iterative model calibration and validation, the affordances of the agent based model allow school leaders to participate, experience, appropriate, perform and therefore to learn to make effective diffusion decisions in their schools.

A noticeable surge in students’ widespread adoption of ChatGPT in the past year brought attention to the need for a deeper understanding of their interactions with this new technology. While attempts at theorising learner-ChatGPT interactions have been made, few studies offer empirical accounts of the interactions between learners and ChatGPT. This study aims to address this gap by utilising Emirbayer and Mische’s Choral Triad of Agency as an analytical framework to investigate secondary school students’ self-initiated interactions with ChatGPT in the context of historical inquiry. Through an in-depth examination of three cases, we unpacked three distinct types of learner-ChatGPT interactions—ChatGPT-as-historical source, ChatGPT-as-feedback, and principled non-use. Although students presented unique interaction patterns with ChatGPT, each case was found to have limited routined interactions with ChatGPT. Our analysis revealed that the students held static agentic orientations in their use of ChatGPT due to their limited experiences with ChatGPT and inadequate ideation for alternative ways of utilising it. Implications of this study propose the need for deliberate interventions to encourage students to have more diverse and meaningful interactions with ChatGPT.

To effectively foster innovation diffusion, school leaders need to learn innovation diffusion as Complex Adaptive Systems (CAS). In this study, two school leaders formed a dyad to learn both the knowledge about innovation diffusion and the knowledge in fostering innovation diffusion. Agent-based model building, model simulation, game play of a simulation game and reflections were designed as learning activities in this study. In the learning process, the learners developed the following understanding in innovation diffusion: teachers’ adoption decisions are based on limited rationality and local information; teachers have nonlinear influence on each other through social networks; teachers are heterogonous agents; and diffusion is a process of emergence. The learners also learnt to leverage on social networks to foster effective innovation diffusion. While agent-based model building faces challenges for learning CAS in the social science domain, this study shows that engaging learners in reflection activities helps to overcome the challenges.

Conceiving innovation diffusion as a Complex Adaptive System (CAS) broadens the approaches to foster scalable and sustainable diffusion of innovation in schools. This study adopted an Interpretivist Paradigm through multiple case studies to understand education leaders’ learning with regard to innovation diffusion as a CAS. There were four cases involving eight education leaders learning in four dyads. The purpose was to seek a descriptive and interpretative understanding of their learning processes, which included the knowledge development trajectories and the learning patterns through which the learning activities induced the knowledge development trajectories. Cognitive conflict was engaged as the learning strategy and learning activities included building an agent-based model, simulating the researcher’s model and playing a first-person role-play game. Analogical reasoning was engaged in reflections.

The grounded theory coding and analysis and episodic uptake analysis revealed nonlinearity of the knowledge development trajectories. The findings suggest that, prior to the learning, the dyads maintained ten simple Newtonian knowledge elements. After learning, the dyads made progression in developing ten comprehensive CAS knowledge elements. Analyzing the knowledge development trajectories indicated that the dyads experienced difficulties in constructing the CAS knowledge elements when they maintained prior Newtonian knowledge elements. The development trajectory of each knowledge element was gradual, progressing from conceptualization to comprehension and towards knowledge application in the real world. The development trajectories of the different knowledge elements were intertwined.

Further data analysis on how cognitive conflict enabled the learning processes implied nonlinear learning patterns. Cognitive conflict induced by analogical reasoning helped the dyads conceptualized the first CAS knowledge element, whereas cognitive conflict induced by model building and simulation did not. For the dyads who had conceptualized the first CAS knowledge element through analogical reasoning, the discrepancies generated from model building and simulation, from knowledge incompatibility and from game play induced cognitive conflicts and sustained the intertwined knowledge development trajectories.

There are sources of evidence in this study that could potentially extend the conventional conditions for analogical reasoning and cognitive conflict. The literature of analogical reasoning suggests that effective analogical reasoning requires learners’ adequate knowledge on the analogy and the target domain, and analogy’s sufficient degrees of similarities with the target. The learning patterns in this study seemed to suggest that, when the dyads, who lacked CAS knowledge, were conceptualizing the first CAS knowledge element, they did not learn through analogies that had more degrees of similarities; however, they learnt through analogies that had fewer degrees of similarities. Similarly, the findings may also suggest alternative views about the premises for cognitive conflict. The literature suggests that meaningful cognitive conflict requires discrepancies to be credible and relevant, and learners to have more prior knowledge. In this study, the agent-based models provided by the researcher were direct modeling of innovation diffusion, whereas the analogies used in reflections were from domains different from innovation diffusion and were only plausible to be mapped for the dyads to learn innovation diffusion. While the analogies induced valid discrepancy and meaningful cognitive conflict in this study, the model building and simulation did not. The findings in this study also alluded that salient knowledge that mediates learning Complex Systems is perhaps learner dependent and domain specific. The study thus advocates that research on analogical reasoning and cognitive conflict should focus on understanding learners’ meaning-making processes, rather than conditions for analogical reasoning and cognitive conflict.

Based on the findings, this study also espouses that learning Complex Systems perhaps involves meta-complexity: the learning of individual knowledge elements is complex, and the intertwined knowledge development trajectories also exhibit characteristics similar to scale-free network topologies. The implications of meta-complexity for learning design are also discussed to complement the design principles for learning Complex Systems.

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.

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.