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.

View the Call for Papers PDF⧉

Suggested Topics

Topics of interest for this special issue include, but are not limited to:

• Technology-supported data-driven interventions in different contexts (e.g., formal, informal) and spaces (e.g., physical, virtual) in SLEs;
• Systems based on learning analytics techniques (e.g., predictive models) to trigger interventions in SLEs;
• Tools based on visualization techniques (e.g., dashboards) and sensemaking approaches to trigger interventions in SLEs;
• Recommender systems supporting intervention processes in SLEs;
• Adaptive systems to optimize and personalize the learning and assessment process in SLEs;
• Multimodal and across-spaces systems based on learning analytics supporting technology-mediated interventions in SLEs;
• Internet of Things (IoT) technologies for data-driven interventions in SLEs;
• Technology-supported data-driven interventions based on analytics of data about teachers and their contexts in SLEs;
• Technological support of data-driven interventions in learning design and classroom orchestration in SLEs;
• Tools supporting data-driven interventions of students’ regulation in SLEs;
• Linked Data and Semantic Web technologies to improve interventions in SLEs;
• Technologies in SLEs relying on specific strategies for data-driven interventions such as conversational agents, gamification mechanics, etc.;
• Ethical issues (e.g., fairness, accountability, transparency) in the design of technologies for facilitating data-driven interventions in SLEs.

Note: TLT is somewhat unique among educational technology journals in that it is both a computer science journal and an education journal. In order to be considered for publication in TLT, papers must make substantive technical and/or design-knowledge contributions to the development of learning technologies as well as show how the technologies can be used to support learning. Papers that are concerned primarily with the evaluation of existing learning technologies and their applications are suitable for TLT only if the technologies themselves are novel, or if significant technical and/or design insights are offered.

Submission and Review Process
 
Abstracts may be submitted to the guest editors via email at [email protected]; this is not mandatory but will enable the editors to offer early feedback on the paper’s suitability with respect to the aims and scope of the special issue.
 
Full manuscripts should be prepared in accordance with the IEEE Transactions on Learning Technologies guidelines and submitted via the journal’s ScholarOne Manuscripts portal⧉, being sure to select the relevant special issue name during the submission process. Manuscripts must not have been published or currently be under consideration for publication elsewhere. Only full manuscripts intended for review, not abstracts, should be submitted via the ScholarOne portal, and conversely, full manuscripts cannot be accepted via email.
 
Each full manuscript that passes an initial prescreening will be subjected to rigorous peer review in accordance with TLT’s editorial policies and procedures. It is anticipated that 7 or 8 articles (plus a guest editorial) will ultimately be published in the special issue.

Important Dates

• Abstract submission (optional): 15 October 2021
• Feedback from guest editors to authors on abstracts: 31 October 2021
• Full manuscripts due: 15 December 2021
• Completion of first review round: 15 March 2022
• Revised manuscripts due: 30 April 2022
• Final decision notification: 30 June 2022
• Publication materials due: 15 September 2022
• Publication of special issue: Autumn/Fall 2022

Guest Editors

• Pedro J. Muñoz-Merino - Universidad Carlos III de Madrid, Spain
• Davinia Hernández-Leo - Universitat Pompeu Fabra, Spain
• Miguel L. Bote-Lorenzo - Universidad de Valladolid, Spain
• Dragan Gašević - Monash University, Australia
• Sanna Järvelä - University of Oulu, Finland

Please contact [email protected] with any questions, comments, or concerns. 

References

1. B. Tabuenca et al., “Affordances and core functions of smart learning environments: A systematic literature review,” IEEE Trans. Learn. Technol., vol. 14, no. 2, pp. 129–145, Apr. 2021, doi: 10.1109/TLT.2021.3067946⧉.

2. J. A. Ruipérez-Valiente, P. J. Muñoz-Merino, D. Leony, and C. Delgado Kloos, “ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform,” Comput. Human Behav., vol. 47, pp. 139–148, Jun. 2015, doi: 10.1016/j.chb.2014.07.002⧉.

3. W. Matcha et al., “A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective,” IEEE Trans. Learn. Technol., vol. 13, no. 2, pp. 226–245, Apr.–Jun. 2020, doi: 10.1109/TLT.2019.2916802⧉.

4. B. Vesin, K. Mangaroska, and M. Giannakos, “Learning in smart environments: User-centered design and analytics of an adaptive learning system,” Smart Learn. Environ., vol. 5, Art. no. 24, 2018, doi: 10.1186/s40561-018-0071-0⧉.

5. E. Oliveira, P. G. de Barba, and L. Corrin, “Enabling adaptive, personalised and context-aware interaction in a smart learning environment: Piloting the iCollab system,” Australas. J. Educ. Technol., vol. 37, no. 2, pp. 1–23, 2021, doi: 10.14742/ajet.6792⧉.

6. G. Sedrakyan, J. Malmberg, K. Verbert, S. Järvelä, and P. A. Kirschner, “Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation,” Comput. Human Behav., vol. 107, Art. no. 105512, Jun. 2020, doi: 10.1016/j.chb.2018.05.004⧉.

7. H. Imran, K. Ballance, J. M. C. Da Silva, Kinshuk, and S. Graf, “Vatrubars: A visualization and analytical tool for a rule-based recommender system to support teachers in a learner-centered learning approach,” in State-of-the-Art and Future Directions of Smart Learning, Y. Li et al., Eds. Singapore: Springer, 2016, pp. 31–38, doi: 10.1007/978- 981-287-868-7_4⧉.

8. J. Bacca, Kinshuk, and D. Segovia-Bedoya, “An architecture for mobile based assessment systems in smart learning environments,” in Proc. 2019 Int. Conf. Smart Learning Environments (ICSLE’19), M. Chang et al., Eds. Denton, TX, USA, Mar. 18–20, 2019, pp. 25–34, doi: 10.1007/978-981-13-6908-7_4⧉.

9. R. Martinez-Maldonado, A. Clayphan, K. Yacef, and J. Kay. “MTFeedback: Providing notifications to enhance teacher awareness of small group work in the classroom,” IEEE Trans. Learn. Technol., vol. 8, no. 2, pp. 187–200, Apr.–Jun. 2014, doi: 10.1109/TLT.2014.2365027⧉.

10. L. Albó, J. Barria-Pineda, P. Brusilovsky, and D. Hernández-Leo, “Knowledge-based design analytics for authoring courses with smart learning content,” Int. J. Artif. Intell. Educ., to be published, doi: 10.1007/s40593-021-00253-3⧉.

11. C. Liang, R. Majumdar, and H. Ogata, “Learning log-based automatic group formation: System design and classroom implementation study,” Res. & Pract. Technol. Enhanced Learn., vol. 16, Art. no. 14, 2021, doi: 10.1186/s41039-021-00156-w⧉.

12. Z. Papamitsiou and A. A. Economides, “Learning analytics for smart learning environments: A meta-analysis of empirical research results from 2009 to 2015,” in Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy, M. J. Spector, B. B Lockee, and M. D. Childress, Eds. Cham, Switzerland: Springer, 2016, pp. 1–23, doi: 10.1007/978-3-319-17727-4_15-1⧉.

13. E. Hakami and D. Hernández-Leo, “How are learning analytics considering the societal values of fairness, accountability, transparency and human well-being?—A literature review,” in Proc.Learning Analytics Summer Institute Spain 2020 (LASI-SPAIN’20), A. Martínez-Monés, A. Álvarez, M. Caeiro-Rodríguez, and Y. Dimitriadis, Eds. Valladolid, Spain, Jun. 15–16, 2020, pp. 121–141. [Online]. Available: http://ceur-ws.org/Vol-2671/paper12.pdf⧉.