This paper examines how emergent technologies could influence the design of learning environments. It will pay particular attention to the roles of educators and learners in creating networked learning experiences on massive open online courses (MOOCs). The research shows that it is possible to move from a pedagogy of abundance to a pedagogy that supports human beings in their learning through the active creation of resources and learning places by both learners and course facilitators. This pedagogy is based on the building of connections, collaborations, and the exchange of resources between people, the building of a community of learners, and the harnessing of information flows on networks. This resonates with the notion of emergent learning as learning in which actors and system co-evolve within a MOOC and where the level of presence of actors on the MOOC influences learning outcomes.

Some might argue that the analytics tools at our disposal are currently mainly used for boring purposes, such as improving processes and making money. In this paper we will try to define learning analytics and their purpose for learning and education. We will ponder on the best possible fit of particular types of research methods and their analysis. Methodological concerns related to the analysis of Big Data collected on online networks as well as ethical and privacy concerns will also be highlighted and a case study of the use of learning analytics in a Massive Open Online Course explored.

Psychiatric disorders affect millions, yet diagnosis depends on subjective assessments and uneven access to care. To address these challenges, there is a growing need for Contestable AI (CAI), a framework that extends beyond Explainable AI (XAI) by allowing clinicians to inspect, question, and revise algorithmic outputs, thereby reducing automation bias and strengthening accountability. We present Heart2Mind (Our implementation is available at: https://github.com/Analytics-Everywhere-Lab/heart2mind ), a human-centered CAI system for psychiatric disorder prediction that provides objective evidence while preserving clinical oversight. Heart2Mind collects R–R Interval (RRI) time series from Polar H9/H10 wearable ECG sensors via a Cardiac Monitoring Interface and analyzes them using a Multi-Scale Temporal-Frequency Transformer (MSTFT) that combines time-domain and frequency-domain features. For contestability, the Contestable Diagnosis Interface integrates model explanations with dialogue. Self-Adversarial Explanations compare attention-based and gradient-based explanation maps to flag inconsistent predictions, and a collaboration chatbot helps users verify and challenge outputs. On the HRV-ACC dataset, MSTFT achieved 91.7% accuracy under leave-one-out cross-validation, outperforming benchmark methods. Human-centered evaluation with the Human–CAI Consensus Rate showed experts and CAI could confirm correct decisions and correct errors through readable, efficient dialogues ( \(FKGL\approx 15\) , median 8.3 minutes, 4 turns). These results support low-cost wearable CAI screening with objective biomarkers, safeguards, and an interactive path for clinicians to refine recommendations.

Basic literacy skills are fundamental building blocks of education, yet for a very large number of adults tasks such as understanding and using everyday items is a challenge. While research, industry, and policy-making is looking at improving access to textual information for low-literacy adults, the literacy-based demands of today's society are continually increasing. Although many community-based organizations offer resources and support to adults with limited literacy skills, current programs have difficulties reaching and retaining those that would benefit most from them. To address these challenges, the National Research Council of Canada is proposing a technological solution to support literacy programs and to assist low-literacy adults in today's information-centric society: ALEX\ua9 \u2013 Adult Literacy support application for EXperiential learning. ALEX\ua9 has been created together with low-literacy adults, following guidelines for inclusive design of mobile assistive tools. It is a mobile language assistant that is designed to be used both in the classroom and in daily life, in order to help low-literacy adults become increasingly literate and independent.Peer reviewed: YesNRC publication: Ye