This paper reports a design science research (DSR) study that develops, demonstrates and evaluates a set of design principles for information systems (IS) that utilise learning analytics to support learning and teaching in higher education. The initial set of design principles is created from theory-inspired conceptualisation based on the literature, and they are evaluated and revised through a DSR process of demonstration and evaluation. We evaluated the developed artefact in four courses with a total enrolment of 1,173 students. The developed design principles for learning analytics information systems (LAIS) to establish a foundation for further development and implementation of learning analytics to support learning and teaching in higher education.

The advancement of artificial intelligence in education (AIED) has the potential to transform the educational landscape and influence the role of all involved stakeholders. In recent years, the applications of AIED have been gradually adopted to progress our understanding of students’ learning and enhance learning performance and experience. However, the adoption of AIED has led to increasing ethical risks and concerns regarding several aspects such as personal data and learner autonomy. Despite the recent announcement of guidelines for ethical and trustworthy AIED, the debate revolves around the key principles underpinning ethical AIED. This paper aims to explore whether there is a global consensus on ethical AIED by mapping and analyzing international organizations’ current policies and guidelines. In this paper, we first introduce the opportunities offered by AI in education and potential ethical issues. Then, thematic analysis was conducted to conceptualize and establish a set of ethical principles by examining and synthesizing relevant ethical policies and guidelines for AIED. We discuss each principle and associated implications for relevant educational stakeholders, including students, teachers, technology developers, policymakers, and institutional decision-makers. The proposed set of ethical principles is expected to serve as a framework to inform and guide educational stakeholders in the development and deployment of ethical and trustworthy AIED as well as catalyze future development of related impact studies in the field.

Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could

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This paper proposes a systematic framework to integrate learning analytics into serious games for people with intellectual disabilities. Serious games for an inclusive learning environment need to be prudently designed with adaptive and measurable competencies to meet the needs of the target users. The recent emergence of learning analytics provides a capability to capture important data in real time from within the highly interactive nature of serious games to better understand and enhance the learning process. Although previous research has addressed different applications of learning analytics in serious games, few studies have investigated the needs of individuals with disabilities. This paper proposes a framework for serious games analytics specialised for people with intellectual disabilities (SGAPID) for the purpose of supporting the integration of learning analytics within serious games to create an inclusive learning environment. We provide a framework based on previous studies in both learning analytics, serious games and educational technologies for people with intellectual disabilities. The framework consists of three central components, namely learner profiling, learning adaptation and learning evaluation. It provides needed guidance for educational application developers and reflects the contemporary trends in educational technologies. The SGAPID framework will also be valuable for the design, implementation, evaluation and adaptation of serious games for inclusive learning and teaching at the group or individual level.

Recently, the coronavirus disease 2019 (COVID-19) pandemic has led to rapid digitalisation in education, requiring educators to adopt several technologies simultaneously for online learning and teaching. Using a large-scale survey (N = 1740), this study aims to construct a model that predicts teachers’ extensive technology acceptance by extending the Technology Acceptance Model (TAM) with their technological pedagogical content knowledge (TPACK) and innovativeness. TAM has been a valuable tool to measure the adoption of new technology in various contexts, including education. However, TAM has been designed and principally applied to assess user acceptance of a specific technology implementation. This study has extended TAM to measure teachers’ technology-enabled practice (online teaching) with the adoption of various technologies. The proposed model explains teachers’ behavioural intention to teach online with a good fit. Our findings revealed the collective effects of TPACK, perceived usefulness (PU) of technology, and innovativeness on teachers’ behavioural intention to teach online post-pandemic. Moreover, the study identified training and support from school as a significant predictor for both teachers’ TPACK and PU. The novelty of this study lies in its model conceptualisation that incorporates both information-technology-based constructs and personal-competence-based features, including TPACK and innovativeness. Furthermore, our study contributes to the growing body of literature that addresses the online teaching adoption by schoolteachers in the post-pandemic era.

The paper motivates, presents and demonstrates a methodology for developing and evaluating learning analytics information systems (LAIS) to support teachers as learning designers. In recent years, there has been increasing emphasis on the benefits of learning analytics to support learning and teaching. Learning analytics can inform and guide teachers in the iterative design process of improving pedagogical practices. This conceptual study proposed a design approach for learning analytics information systems which considered the alignment between learning analytics and learning design activities. The conceptualization incorporated features from both learning analytics, learning design, and design science frameworks. The proposed development approach allows for rapid development and implementation of learning analytics for teachers as designers. The study attempted to close the loop between learning analytics and learning design. In essence, this paper informs both teachers and education technologists about the interrelationship between learning design and learning analytics.

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human‐AI collaboration by introducing a novel trigger concept and a hybrid human‐AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human‐AI collaboration in facilitating effective SSRL in teaching and learning. Practitioner notesWhat is already known about this topic For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor. Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning. It is questionable whether traditional theories of how people learn are useful in the age of AI. What this paper adds Introduces a trigger concept and a hybrid Human‐AI Shared Regulation in Learning (HASRL) model to offer insights into how the human‐AI collaboration could occur to operationalize SSRL research. Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning. Provides clear suggestions for future human‐AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills. Implications for practice and/or policy Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning. Researchers and practitioners could benefit from methodological development incorporating human‐AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.