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6 references
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- Vol 5 RE: Chapter 12
human-centred designlearning analyticsartificial intelligencestakeholder participationhuman controlautomationprivacytrustworthinessJournal article Evidence summaryAt a glance
This systematic review examines how human-centred principles are represented in learning analytics and artificial-intelligence systems for education.
Key ideas
The authors reviewed 108 papers, focusing on stakeholder participation, the balance between human control and automation, and attention to safety, reliability, and trustworthiness. Although many systems acknowledged a need for human control, students and other target users were often only lightly involved in actual design and development. Safety and trustworthiness also received less attention than usability or technical performance. The review recommends involving educators and learners throughout design and deployment, making decisions about automation explicit, and treating reliability, agency, privacy, and trust as core design requirements.
Why it matters
For schools, the study cautions against adopting data-driven tools solely for efficiency: educational stakeholders should help define the purposes, limits, and acceptable consequences of the technology.
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- Vol 4 RE: Chapters 6, 12, App C
learning analyticsself-regulated learning measurementmultimodal datadigital trace datatemporal alignmentlearner modelingphysiological signalsJournal article Evidence summaryAt a glance
Azevedo and Gašević examine the opportunities and difficulties involved in using multimodal, multichannel data to study self-regulated learning with advanced learning technologies.
Key ideas
Digital environments can capture fine-grained traces such as navigation, eye movements, dialogue, physiological signals, and performance, potentially revealing how regulation unfolds over time. Yet more data do not automatically produce valid inferences. Researchers must connect observable traces to a clear theory of regulation, align channels in time, distinguish meaningful processes from noise, and account for differences among learners and tasks. The article calls for collaboration across learning science, measurement, data science, and system design, together with transparent analytic decisions.
Why it matters
Its practical message is that learning analytics should be designed around educational questions rather than available sensors: evidence from multiple channels is valuable when it helps explain learners' goals, strategies, monitoring, and adaptation.
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- Vol 5 RE: Chapter 8
artificial intelligenceself-regulated learninglearner agencylearning analyticsadaptive feedbacktheoretical alignmentethical reportingJournal article Evidence summaryAt a glance
This systematic mapping review analyses 84 studies at the intersection of artificial intelligence and self-regulated learning.
Key ideas
Using a framework organised around who is involved, what theories and constructs are addressed, how the research is conducted, and why AI is applied, the authors map publication patterns, stakeholders, methods, technologies, and objectives. The review finds rapid growth but uneven theoretical grounding and limited attention to some phases and actors in self-regulation. Much work focuses on higher education and on using learner data to predict, recommend, prompt, or provide feedback. The authors call for clearer links between AI functions and established self-regulated-learning theory, broader contexts, stronger ethical and methodological reporting, and more attention to learner agency.
Why it matters
For educators, the review offers a structured way to judge whether an AI tool genuinely supports planning, monitoring, reflection, or adaptation.
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- Vol 5 RE: Chapters 12, App C
formative assessmentlearning analyticsfeedback processeslearning intentionsevidence elicitationself-assessmentinstructional decision-makingJournal article Evidence summaryAt a glance
Banihashem and colleagues systematically review 93 studies published from 2011 to 2023 to examine how learning analytics supports formative assessment.
Key ideas
Their coding framework combines three actors—teachers, peers, and learners as self-assessors—with three questions: where learning is going, where it is now, and how to move forward. The review finds that analytics can clarify goals, diagnose current understanding, and support feedback or instructional action, but coverage across the framework is uneven. A central concern is alignment: dashboards or predictions are useful only when their outputs connect to a defensible model of formative assessment and can be interpreted and acted upon.
Why it matters
For educators and designers, the study recommends building analytics around learning intentions, evidence elicitation, feedback processes, and concrete decisions rather than treating data visualisation as formative assessment by itself.
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- Vol 7 RE: Chapter 9
student engagementlearning analyticsdigital trace databehavioural engagementcognitive engagementemotional engagementsocial engagementproxy validityJournal article Evidence summaryAt a glance
Bergdahl and colleagues systematically review how higher-education learning-analytics research conceptualises and measures student engagement through digital trace data.
Key ideas
Engagement is multidimensional, including behavioural, cognitive, emotional, and social participation, yet studies often equate it with easily counted actions such as logins, clicks, submissions, or time online. The review examines the dimensions, indicators, data sources, analytic methods, and theoretical frameworks used across the field. It warns that platform activity is an indirect proxy whose meaning depends on task, design, context, and complementary evidence; more activity is not necessarily deeper engagement.
Why it matters
For educators and analytics designers, the study recommends defining engagement before selecting data, aligning indicators with learning design, combining traces with qualitative or self-report evidence where appropriate, and avoiding interventions or labels that overinterpret what digital behaviour can establish.
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- Vol 7 RE: Chapters 11, 14
hybrid intelligencelearning analyticseducational artificial intelligencecognitive augmentationhuman agencyai literacyhuman–ai collaborationJournal article Evidence summaryAt a glance
Cukurova challenges the narrow view of educational AI as a collection of tools, especially generative systems that externalize human cognitive work. He reconnects artificial intelligence, learning analytics, and learning science through a human-centred framework with three possibilities: AI can externalize cognition by performing tasks, shape cognition as people internalize its models and outputs, or extend cognition through tightly coupled human–AI systems.
Key ideas
The last possibility supports hybrid intelligence, in which complementary human and machine capabilities produce outcomes neither could achieve alone. AI and analytics can also make learning processes visible and provide material for scientific inquiry, feedback, awareness, and regulation. Yet prediction alone does not explain learning, and indiscriminate delegation may weaken important human competencies. The article therefore asks researchers and educators to examine which cognitive work should remain human, how agency and meaning-making are preserved, and how systems affect competence over time.
Why it matters
It calls for broader AI literacy, educational-system innovation, and research that evaluates augmentation, ethics, and human development rather than merely tool performance.
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Edition abbreviations
- TE
- Teacher Edition
- PE
- Practice Edition
- RE
- Research Edition
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