BackgroundWith increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction.ObjectiveThe paper reviews the current research on intelligent learning technology designed to make models of student learning and instruction transparent to teachers, an area we call model‐based learning analytics. We intended to gain an insight into the coupling between the knowledge models that underpin the intelligent system and the knowledge used by teachers in their classroom decision making.MethodsUsing a systematic literature review methodology, we first identified 42 papers, mainly from the domain of intelligent tutoring systems and learning analytics dashboards that conformed to our selection criteria. We then qualitatively analysed the context in which the systems were applied, models they used and benefits reported for teachers and learners.Results and ConclusionsA majority of papers used either domain or learner models, suggesting that instructional decisions are mostly left to teachers. Compared to previous reviews, our set of papers appeared to have a stronger focus on providing teachers with theory‐driven insights and instructional decisions. This suggests that model‐based learning analytics can address some of the shortcomings of the field, like meaningfulness and actionability of learning analytics tools. However, impact in the classroom still needs further research, as in half of the cases the reported benefits were not backed with evidence. Future research should focus on the dynamic interaction between teachers and technology and how learning analytics has an impact on learning and decision making by teachers and students. We offer a taxonomy of knowledge models that can serve as a starting point for designing such interaction.
This paper presents a bibliometric systematic review on model-based learning analytics (MbLA), which enable coupling between teachers and intelligent systems to support the learning process. This is achieved through systems that make their models of student learning and instruction transparent to teachers. We use bibliometric network analysis and topic modelling to explore the synergies between the related research groups and the main research topics considered in the 42 reviewed papers. Network analysis depicts an early stage community, made up of several research groups, mainly from the fields of learning analytics and intelligent tutoring systems, which have had little explicit and implicit collaboration but do share a common core literature. Th resulting topics from the topic modelling can be grouped into the ones related to teacher practices, such as awareness and reflection, learning orchestration, or assessment frameworks, and the ones related to the technology used to open up the models to teachers, such as dashboards or adaptive learning architectures. Moreover, results show that research in MbLA has taken an individualistic approach to student learning and instruction, neglecting social aspects and elements of collaborative learning. To advance research in MbLA, future research should focus on hybrid teacher–AI approaches that foster the partnership between teachers and technology to support the learning process, involve teachers in the development cycle from an early stage, and follow an interdisciplinary approach.
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