Title: Questionnaire: Enablers of learning analytics-based educational interventions
Course: Learning Analytics > Section: Ethical aspects and personal data protection

Share your perspective on the key enablers needed for implementing educational interventions based on trustworthy learning analytics. In the first part, you should assign weights — i.e., relative importance — to the seven enablers, such that the sum of all weights equals 100. If one enabler is more important than another, it should be assigned a higher weight. In the second part of the task, there is an open-ended question where you may provide additional comments and suggestions.

The prerequisites were identified and their definitions developed as part of the HRZZ TRUELA research project, within the doctoral research of Barbi Svetec, and should be cited accordingly. They are part of a scientific article prepared for publication.

Upitnik: Preduvjeti za obrazovne intervencije temeljene na vjerodostojnoj analitici učenja

Q: Question 1. Review the list and definitions of the enablers for implementing educational interventions based on trustworthy learning analytics. In the following seven questions, assign a relative importance (i.e., weight) to each enabler so that the sum of all assigned weights equals 100.

Institutional strategic orientation

The extent to which strategic planning and leadership at the level of a higher education institution (HEI) support, embed and streamline the implementation of learning analytics (LA) and the application of insights from LA to inform educational interventions. This includes devising strategic priorities, leadership engagement, defining accountabilities and governance structures that support integration of LA into institutional decision-making, as an integral part of continuous improvement processes.
Q: Pedagogical and other research foundation

The extent to which study programs and courses are founded on contemporary pedagogical principles, learning theories and educational research findings. This includes sound, student-centred learning design based on learning outcomes and constructive alignment, ensuring coherence between intended learning outcomes, teaching and learning activities, and assessment, as well as the application of innovative digital pedagogies. On the one hand, LA can provide feedback which supports development of study programs and courses in line with this pedagogical foundation. On the other hand, LA can be interpretable and provide meaningful and actionable insights if it is based on data from pedagogically transparent study programs and courses.
Q: Available resources

The extent to which an HEI (or another relevant institution) ensures that educators have at their disposal the necessary resources to introduce educational interventions informed by LA. This includes the sufficient financial resources, data management and analytics capacities, human capacities, expertise and time for educators to engage with LA, develop data and AI literacy, plan and implement interventions based on LA insights. It also encompasses the contemporary digital tools and infrastructure that support teaching and learning, including learning management systems (LMS), learning design tools, LA systems and dashboards for educators and students, AI tools, as well as their interoperability and the related technical support.
Q: Pedagogical support

The extent to which an HEI ensures that educators have direct expert support and continuous professional development and training in pedagogical theory, educational technologies (EdTech), learning design, application and interpretation of LA, responsible use of AI in education, and access to research insights. It also encompasses opportunities for mentorship, peer learning and experience sharing among educators, consultation and feedback.
Q: Ethics & Data governance

The extent to which an HEI has established policies and practices for the ethical collection, analysis, sharing and storage of learning data, including students' demographic data, LMS log data, assessment data, and other learning records (possibly multimodal data). This includes ensuring data privacy, user consent and control over the use of teaching and learning data. It covers clear principles on data ownership and sharing with third parties. It also includes algorhitmic decision-making and issues pertaining to accuracy, fairness and bias mitigation, transparency, and explainability of algorithms, including AI, used in LA.
Q: Stakeholder engagement

The extent to which the key stakeholders (students, educators, and educational decision-makers at instituitional or system levels), and possibly external stakeholders (e.g. employers), are involved in the development and implementation of LA at institutional or broader levels. This includes considering user needs and preferences in the developement of human-centred LA policies and systems. It involves collecting a variety of perspectives to enhance the understanding, acceptance of and trust in LA.
Q: Quality Assurance

The extent to which LA is integrated in internal and external quality assurance (QA) procedures. This refers to the use of LA results to support data-informed development and improvement of study programs, courses, and teaching and learning practices, and the development of quality culture. It also includes the QA of LA systems themselves, in terms of monitoring their implementation, effectiveness and contribution to the achievement of strategic and pedagogical goals.
Q: Question 2. If you believe that there are other enablers not covered by this list, please specify them and provide an explanation.
Q: Pitanje 2. Ako smatrate da postoje i drugi preduvjeti koji ovim popisom nisu obuhvaćeni, navedite ih i obrazložite.