Dear Colleagues,

Akin to collecting intelligence on individuals for national security purposes, learning analytics collects intelligence on humans and provides insightful frameworks for the proper application of the same. Observing learning engagements in study episodes, LA agents/sensors tend to collect ethically clean and time-sensitive data over these episodes. LA then exacts actionable insights from these securely communicated datasets to infer information and create models, yielding actionable insights. Specific actions can then be carried out considering these insights, with the impact of those actions measuring the quality of learning. Generating intelligence that helps humans succeed in learning is the singular goal of learning analytics.

Learning Science explores the nature of learning as an interweaving of theories, models, algorithms, traits, datasets, practices, and insights originating from areas such as computing, mathematics, health, education, sociology, humanities, and statistics. Learning Analytics(LA) is an emerging area of study in learning science. LA offers a meta-level view on the quality of human learning with respect to influencing variables of interest. Technology enhancement, cognition and meta-cognition, pedagogy and andragogy, end-user competences, learning environments as well as governance and policymaking are some of the key influencing variables.

In general, LA derives intelligence on the quality of human learning from educational data. Meta-level views of LA include the measurements and relations about the utility, optimality, generalizability, intervention, explainability, and customizability, among other ‘unknown common truths’ pertaining to the quality of human learning. For instance, LA intelligence concerns learners’ capacity, challenges, progress, accomplishments, and the effectiveness of learning contexts, approaching the elusive causality on learning quality as a function of contextual factors such as instructional effectiveness and adaptability. As another example, LA intelligence technology enhancement concerns computational entities, learning machines, and technological gadgets, among others, as well as their impact on the quality of learner interaction.

This Special Issue invites research contributions on learning analytics. Whether research focuses on tracing skills development or metacognition in human learning, whether it focuses on face-to-face, online or blended environments, this Issue seeks to share new insights in the field of LA.

Topics of interest include:

A. Learning analytic theory and science

1. Analysis of unstructured and semi-structured data;
2. Security, privacy, and veracity of analytics;
3. Ethics of learning analytics;
4. Scalability of machine learning and data mining for analytics;
5. Computing infrastructure for analytics—cloud, grid, autonomic, stream, mobile, high-performance computing;
6. Search in learning traces;
7. Artificial intelligence in learning analytics;
8. Uncertainty handling in analytics;
9. IoT and learning analytics.

B. Applications of learning analytics

1. Detecting students’ approach to learning;
2. Analytics in academic administration;
3. Analytics in complex training;
4. Gaming analytics and sports analytics;
5. Evidence-driven instruction in interdisciplinary and single-discipline areas;
6. Big data and educational technology;
7. Analytics in academic strategic planning;
8. Cultural analytics;
9. Large-scale social networks;
10. Data literacy;
11. Technological literacy and analytics;
12. Human literacy and analytics.

C. Techniques and technology in learning analytics

1. Evidence-driven mixed-initiative learning;
2. Data-intensive learning and instructional design;
3. Emerging standards in learning analytics;
4. Sentiment analysis;
5. Large-scale productivity analysis;
6. Analytic infrastructure for academic institutions;
7. Scalable knowledge management;
8. Research methods for learning analytics;
9. Immersive learning.

Prof. Dr. Vivekanandan Suresh Kumar
Prof. Dr. Kinshuk
Guest Editors

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