Why are learning analytics systems still stuck in pilot mode?
Despite a decade of research and development, learning analytics (LA) systems have not yet crossed the chasm from pilot projects to routine institutional use. A systematic literature review of 14 critical factors for LA adoption found that most developments remain in the pilot phase without reaching institutional adoption [3]. The review identified that adoption is hindered by multiple dimensions: lack of senior management commitment, poor system integration with legacy systems, and insufficient cross-organizational design [3]. This means that even when a tool works well in a small test, scaling it across an entire university or school system requires coordinated effort that most institutions are not yet equipped to handle.
A capability model tested with 26 participants across five educational institutions found that practitioners—program managers, policymakers, and senior management—need a comprehensive overview of necessary capabilities to plan LA adoption [4]. The model was perceived as useful, but the very need for such a model underscores that institutions lack a clear roadmap. In Kenya, a review of LA research found limited studies and noted that no model had been developed using raw student behavioral data from learning management systems, indicating that even basic infrastructure for LA is missing in many regions [8].
What are the biggest obstacles to trust and adoption?
Privacy and trust are the most consistently cited barriers across the evidence. A study of health professions students found that while they were generally aware of data collection and agreed to its use for learning, they expressed strong concerns about privacy, confidentiality, and data security [1]. Similarly, a case study of a small public university implementing a commercial LA system found the institution was in the early stages and needed to be more proactive in developing privacy policies and procedures [5]. The study highlighted that privacy considerations are often an afterthought rather than a foundational design principle.
Trust is not just a student issue. A large UK university study involving surveys and focus groups with teaching staff and students identified three areas of distrust: the subjective nature of numbers, fear of power diminution (e.g., LA being used to surveil or punish), and poor design and implementation approaches [7]. The authors emphasized that trust must be actively cultivated by engaging with tensions arising from the social process of LA, not just by writing policies. In Morocco, teachers interviewed about LA adoption cited ethical and confidentiality concerns as key challenges, along with a heavy workload and lack of necessary resources [10]. Across these studies, the message is clear: without addressing privacy and trust, LA systems will face resistance from both students and educators.
Are institutions and staff actually ready to use learning analytics?
The evidence suggests a significant readiness gap. A study across four colleges in a higher education institution found that both staff and students rated 'improving teaching quality' and 'improving individual students' educational experience' as top reasons for collecting student data—but both groups also reported a lack of awareness about what data is actually collected and for what purpose [9]. This disconnect between aspiration and awareness indicates that institutions have not yet built the foundational understanding needed for effective LA use.
In Morocco, interviews with 10 teachers from a multidisciplinary faculty revealed that few professors use LA tools on their own initiative, and they expressed a need for guidance, support, and user-friendly tools [10]. Teachers also reported difficulties in aligning LA with their teaching practices, limited technological and pedagogical skills, and challenges in operating LA tools [10]. A study on formative assessment found that while LA can foster personalized and responsive assessment, teachers' limited data literacy and institutional readiness remain key challenges [6]. The Norwegian Expert Commission on Learning Analytics identified four dilemmas—data, learning, governance, and competence—that signal where greater knowledge, awareness, and reflection are needed before LA can be implemented at scale [2]. Together, these findings show that readiness is not just about having the technology; it requires investment in people, processes, and governance.
About These Sources
This answer is built on 10 peer-reviewed studies — published from 2021 to 2025, 6 from 2024 or later, 5 in Q1 journals, collectively cited 85 times — selected as the most relevant from 15 studies that passed quality screening, drawn from 86 papers retrieved from a database of over 500 million.
Sources used in this answer
Health professions students’ acceptance and readiness for learning analytics: lessons for educators
A mixed-method study of health professions students (survey and 18 focus group participants) found students were aware of data collection and agreed to its use for learning, but expressed concerns about privacy, confidentiality, and data security.
Implementing Learning Analytics in Norway
The Norwegian Expert Commission on Learning Analytics identified four dilemmas—data, learning, governance, and competence—that require greater knowledge and reflection before LA can be implemented at scale in primary, secondary, higher, and vocational education.
Adoption of learning analytics in higher education institutions: A systematic literature review
A systematic literature review found that most LA developments remain in the pilot phase without reaching institutional adoption, identifying 14 critical factors for adoption including senior management commitment, goal setting, cross-organizational design, and system integration.
Supporting Learning Analytics Adoption: Evaluating the Learning Analytics Capability Model in a Real-World Setting
An ex-post evaluation of a learning analytics capability model using pluralistic walk-throughs with 26 participants at five institutions and a survey (n=23) found the model helps practitioners plan LA adoption, but the need for such a model underscores current lack of institutional readiness.
Student Privacy and Learning Analytics
A single-site case study of a small public university implementing a commercial LA system found the institution was in early stages and needed to be more proactive in implementing privacy considerations in policies and procedures.
Evaluating the Use of Learning Analytics in Formative Assessment
A mixed-methods study on LA in formative assessment found that LA fosters personalized and responsive assessment, but key challenges include teachers' limited data literacy, institutional readiness, and ethical concerns about data privacy.
More Than Figures on Your Laptop: (Dis)trustful Implementation of Learning Analytics
A study using surveys and focus groups with teaching staff and students at a large UK university identified three areas of distrust in LA: the subjective nature of numbers, fear of power diminution, and poor design and implementation approaches.
Review of Literature on the Use of Learning Analytics and Learning Analytical Dashboard (LAD) in Improving Student Performance in Higher Education Institutions in Kenya
A review of LA literature in Kenyan higher education found limited research and noted that no study had used raw student behavioral data from Moodle to develop a model for improving student performance.
Learning Analytics: What’s the Use?
A contextual study using surveys and focus groups across four colleges in a higher education institution found that both staff and students rated improving teaching quality and student experience as top reasons for LA, but both groups lacked awareness of what data is collected and for what purpose.
Implementing learning analytics in higher education: A case study on challenges and requirements
Qualitative interviews with 10 teachers at a Moroccan multidisciplinary faculty found that few use LA tools on their own initiative, citing challenges including inability to align LA with teaching practices, ethical concerns, lack of resources, and insufficient technological and pedagogical skills.