A systematic review in understanding stakeholders’ role in developing adaptive learning systems

Alajlani, Nawaf; Crabb, Michael; Murray, Iain

doi:10.1007/s40692-023-00283-x

Cited by 5 publications

(5 citation statements)

References 45 publications

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“…Although we noticed some of the information was missing from a small set of papers, there was no significant impact on the coding quality. Moreover, there was not a single paper where more than one aspect of reporting the details was missing; this goes in line with some of the other systematic reviews done in the field of SRL and ADLEs (eg, Alajlani et al, 2023;Alzahrani et al, 2020;Heikkinen et al, 2023).…”

Section: High-level Reporting Detailsupporting

confidence: 54%

“…Recently, there have been several systematic reviews in the field of ADLEs focusing on various aspects such as personal traits (Normadhi et al, 2019), roles of different stakeholders (Alajlani et al, 2023); personalization in ADLEs (Shemshack & Spector, 2020), from the point of view of computing education (Jamal et al, 2020), use of artificial intelligence (AI, Kabudi et al, 2021), flipped classrooms (Ainulluluah et al, 2022), and virtual assistants (Gubareva & Lopes, 2020), enhancing student performance (Anindyaputri et al, 2020), prediction of learning paths (Alzahrani et al, 2020) and anatomy of student models (Nakic et al, 2015) to name a few. Similarly, there have been numerous systematic reviews focusing on various aspects of SRL, such as SRL strategies within e-learning (Garcia et al, 2018), distance learning (Edisherashvili et al, 2022) and massive open online courses (MOOCs, Lee et al, 2019), SRL in higher education (Roth et al, 2016) and high schools (Kesuma et al, 2020), open learner models in SRL (Hooshyar et al, 2020), SRL in flipped classrooms (Rasheed et al, 2020), and the impact of learning analytic intervention (Heikkinen et al, 2023), to name a few.…”

Section: What This Paper Addsmentioning

confidence: 99%

“…While the literature features numerous systematic reviews on ADLEs (eg, Alajlani et al, 2023;Shemshack & Spector, 2020) and SRL (eg, Heikkinen et al, 2023;Kesuma et al, 2020), reviews focusing on both SRL and SSRL in collaborative contexts are less common. Importantly, there is a distinct gap in systematic reviews examining the intersection of SRL and SSRL within ADLEs.…”

Section: Socially Shared Regulation Of Learning (Ssrl) In Adlesmentioning

confidence: 99%

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Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Sharma,

Nguyen,

Hong

2024

Brit J Educational Tech

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Adaptive learning technologies are closely related to learners' self‐regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self‐regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self‐regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self‐assessment, and help‐seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self‐regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development. Practitioner notesWhat is already known about this topic By providing personalized and learner‐centric adaptive learning environments (ADLEs), adaptive learning technology can support and foster self‐regulated learning (SRL) practices. It is possible to create a more student‐centred and effective learning environment by combining adaptive learning and collaborative learning. Socially shared regulatory activities can involve planning, monitoring, controlling and reflecting on a group's learning processes. What this paper adds Provides a systematic literature review of empirical evidence on ADLEs, SRL and socially shared regulation of learning (SSRL) in collaborative contexts. Summarizes the insights on (S)SRL through ADLEs in collaborative learning. Identifies challenges and opportunities for ADLEs to support (S)SRL in collaborative learning. Implications for practice and/or policy Learning analytics and educational technology researchers will be able to use the systematic review as a guide for future research. Learning analytics and educational technology practitioners will be able to use the systematic review as a summary of the field's current state.

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Section: High-level Reporting Detailsupporting

confidence: 54%

Section: What This Paper Addsmentioning

confidence: 99%

Section: Socially Shared Regulation Of Learning (Ssrl) In Adlesmentioning

confidence: 99%

See 1 more Smart Citation

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Sharma,

Nguyen,

Hong

2024

Brit J Educational Tech

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“…A systematic literature review investigating stakeholders' roles in the development of adaptive learning systems highlighted significant opportunities for future research involving key stakeholders [68]. The review identified a notable gap in educator involvement in the initial design of these systems and emphasized the need for further exploration into whether increased stakeholder inclusion at different stages would improve the effectiveness and user experience of adaptive learning systems.…”

Section: Ecosystem Roles and Potential Use Casesmentioning

confidence: 99%

Lightlore: An Adaptation Framework for Design and Development of xAPI-Based Adaptive Context-Aware Learning Environments

Hasanov,

Laine,

Kim

et al. 2024

Electronics

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The age of pervasive computing has initiated a boom in the development of adaptive context-aware learning environments (ACALEs), i.e., systems that are capable of detecting a learner’s context and providing adaptive learning services based on this context. Many of the existing educational systems were developed as standalone applications for specific or a small range of adaptive educational scenarios. It would be extremely helpful for developers and educators to have a unified framework that provides an infrastructure for the development of ACALEs. In this study, we propose Lightlore—an adaptation framework that enables the development of different types of ACELEs for a wide range of learning scenarios in formal and informal settings. We first used scenario-based design (SBD) as the design methodology for creating a conceptual model of Lightlore. Educational scenarios were adopted from the results of a previous literature review. We then developed a proof-of-concept implementation of Lightlore, with a hypermedia system for learning data structures that uses the adaptation service of Lightlore. This implementation is essentially an adaptation infrastructure and a programming API for creating new (or transforming existing) adaptive and context-aware educational services. It exploits the experience API (xAPI), a modern e-learning standard and learning record store, thus making coupling with existing learning environments easier. We expect that diverse types of users will benefit from using Lightlore, such as learners, educators, learning environment developers, and researchers on educational technologies.

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“…However, they differ in their specific focus. For example, the study by Kabudi, Pappas and Olsen [86] focuses on AI-enabled adaptive learning systems, while the study by Alajlani, Crabb and Murray [4] focuses on the role of stakeholders in developing adaptive learning systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive learning: a cluster-based literature review (2011-2022)

Fadieieva

2023

Educ. Technol. Q.

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Adaptive learning is a personalized instruction system that adjusts to the needs, preferences, and progress of learners. This paper reviews the current and future developments of adaptive learning in higher education, especially in relation to the digital education strategy of the European Union. It also uses a cluster analysis framework to explore the main themes and their relationships in the academic literature on adaptive learning. The paper highlights the potential of emerging technologies such as AI, eye-tracking, and physiological measurements to improve the personalization and effectiveness of adaptive learning systems. It presents various methods, algorithms, and prototypes that incorporate learning styles into adaptive learning. It also stresses the importance of continuous professional development in e-learning, media literacy, computer security, and andragogy for teachers who use adaptive learning systems. The paper concludes that adaptive learning can promote creativity, innovation, and lifelong learning in Ukrainian higher education, but it also acknowledges the challenges and suggests further research to assess its impact.

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A systematic review in understanding stakeholders’ role in developing adaptive learning systems

Cited by 5 publications

References 45 publications

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

Lightlore: An Adaptation Framework for Design and Development of xAPI-Based Adaptive Context-Aware Learning Environments

Adaptive learning: a cluster-based literature review (2011-2022)

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