Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI technology has been applied to learning analytics (LA), aiming at a more accurate, consistent and scalable understanding of learning to compensate for challenges that human intelligence faces. However, machine intelligence has been criticized for lacking contextual understanding and difficulties dealing with complex human emotions and social cues. In this work, we aim to understand learners' internal cognitive processes based on the external behavioural cues of learners in a digital reading context, using a hybrid intelligence (HI) approach, bridging human and machine intelligence. Based on the behavioural frameworks and the insights from human experts, we scope specific behavioural cues that are known to be relevant to learners' attention regulation, which is highly relevant for learners' cognitive processes. We utilize the public WEDAR dataset with 30 subjects' video data, behaviour annotation and pre–post tests on multiple choice and summarization tasks. We apply the explainable AI (XAI) approach to train the machine learning model so that human evaluators can also understand which behavioural features were essential for predicting the usage of the cognitive processes (ie, higher-order thinking skills [HOTS] and lower-order thinking skills [LOTS]) of learners, providing insights for the next-round feature engineering and intervention design. The result indicates that the dominant use of attention regulation behaviours is a reliable indicator of low use of LOTS with 79.33% prediction accuracy, while reading speed is a valuable indicator for predicting the overall usage of HOTS and LOTS, ranging from 60.66% to 78.66% accuracy, highly surpassing random guess of 33.33%. Our study demonstrates how various combinations of behavioural features supported by HI can inform learners' cognitive processes accurately and interpretably, integrating human and machine intelligence.
What is already known about this topic
In this editorial, we introduce the special section focusing on ‘Internationalisation at a Distance’ (IaD), highlighting how educational technologies contribute to internationalisation practices and processes in higher education. These practices exist in transitional spaces which interrogate conventional distinctions between mobility/immobility and home/abroad by establishing ‘third spaces’ where students and staff interact with institutions across borders without the necessity of geographic relocation. This special section evaluates these practices through a critical lens, reflecting on limitations and inequalities associated with IaD, despite its often-assumed transformative potential for expanding access to global higher education. Here, this special section argues that IaD fundamentally opposes fixed representations of place and interrogates simplistic binary frameworks, positioning itself as a concept that is inherently post-structuralist. This reframing necessitates a sophisticated comprehension of essential concepts including mobility, home, and cultural exchange. Collectively, this special section addresses these issues by underscoring how IaD continues to reinforce pre-existing disparities in resource access, epistemic (in)justices, and power imbalances. The findings discussed herein underscore the significance of ethical considerations for educational technologies in the realm of IaD and the necessity for evidence-based strategies aimed at fostering more inclusive and sustainable practices. Ultimately, IaD signifies a profound reconsideration of global higher education, presenting technology-influenced avenues for innovation while necessitating a critical examination of its functions within an unequal world.
While offering the potential to support learning interactions, emerging AI applications like Large Language Models (LLMs) come with ethical concerns. Grounding technology design in human values can address AI ethics and ensure adoption. To this end, we apply Value-Sensitive Design—involving empirical, conceptual and technical investigations—to centre human values in the development and evaluation of LLM-based chatbots within a high school environmental science curriculum. Representing multiple perspectives and expertise, the chatbots help students refine their causal models of climate change's impact on local marine ecosystems, communities and individuals. We first perform an empirical investigation leveraging participatory design to explore the values that motivate students and educators to engage with the chatbots. Then, we conceptualize the values that emerge from the empirical investigation by grounding them in research in ethical AI design, human values, human-AI interactions and environmental education. Findings illuminate considerations for the chatbots to support students' identity development, well-being, human–chatbot relationships and environmental sustainability. We further map the values onto design principles and illustrate how these principles can guide the development and evaluation of the chatbots. Our research demonstrates how to conduct contextual, value-sensitive inquiries of emergent AI technologies in educational settings.
What is already known about this topic
What this paper adds
Implications for practice and/or policy
A distinct feature of educational games using augmented reality (AR) is that the game is played through physically interacting with the environment, whereas physical interaction is typically rather limited in other digital games. Understanding and performing the interactive game mechanics can be cognitively demanding. Adding pre-training could help students manage cognitive load during in-game performance. However, traditional approaches of pre-training (eg, paper-based or video sequences) might not be sufficient, given the crucial role of physical interactions in educational AR games. In the present study, primary and early secondary school students (N = 255) were randomly assigned to an active pre-training, which involved students practising the movements needed in the game or a passive pre-training, where students watched a video explaining the game. The aim was to investigate whether active pre-training reduces students' cognitive load and improves in-game performance. It was also examined whether these effects were dependent on students' visuospatial working memory capacity (WMC). Results showed no significant differences between the conditions regarding students' cognitive load and in-game performance. However, visuospatial WMC predicted students' dropout of the game. This suggests that observing the movements in the passive pre-training might be similarly effective as enacting the movements. Nevertheless, more research is needed to gain a better understanding of how different levels of WMC impact learning with educational AR games and how students with low visuospatial WMC can be efficiently supported.
What is already known about this topic
What this paper adds
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：