IEEE Transactions on Learning Technologies (IEEE TLT) Special Issue Call for Papers

TLT Special Issue on Technologies for Data-Driven Interventions in Smart Learning Environments

Abstract Submission (Optional): 15 October 2021
Full Manuscripts Due: 15 December 2021

Smart learning environments (SLEs) have been recently defined [1] as learning ecologies wherein students perform learning tasks and/or teachers define them with the support provided by tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners’ and educators’ actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out, from sources such as learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wide range of automatic, human-mediated, or hybrid interventions that involve learners and teachers in the decision-making behind the interventions.

Data-driven interventions in SLEs can be mediated by technologies that are not only oriented to learners but also to teachers with the aim of helping learners succeed in their learning and educational goals. Examples of such technologies oriented to learners include dashboards supporting the self-regulation of their own learning processes [2], [3], systems adapting learning contents and learning activities [4], [5], feedback systems [6], and recommender systems [7], [8] of learning activities that are available in the context of the students and match their interests. Examples of technologies oriented to teacher interventions include tools and systems that support real-time classroom orchestration [9], redesign of learning paths [10], the improvement of contents, gamification, and formation of groups of students that are expected to engage in fruitful and productive collaboration [11].

Even though there is already a significant body of research around learning analytics and educational data mining techniques that can be applied to learning and teaching interventions [12], the specific theme of how to create and use technologies that facilitate the data-driven initiation, management, adaptation, and evaluation of such interventions in pedagogically sound and ethically defensible [13] ways within the context of SLEs still remains underexplored. This special issue aims to highlight and showcase new perspectives and advances in that regard, and to that end especially welcomes contributions reporting rigorous inquiries and investigations into the technical and design dimensions of technological solutions for enabling various types of intervention that can improve the efficiency and effectiveness of SLEs.

Mark J.W. Lee
Editor-in-Chief of IEEE TLT

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