A Proposed Learner’s Data Model: Integrating Informal Learning and Enhancing Personalization and Interoperability

Nehiri, Nissrin; Aknin, Noura

doi:10.3991/ijet.v16i08.19833

Cited by 6 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, especially with such highly scaled numbers, integrating learning analytics can be worthwhile to provide individualized feedback to students [27] and give instructors a better overview [28]. And researchers can work on learner modeling based on the generated learning data [29].…”

Section: Discussionmentioning

confidence: 99%

xAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects

Heinemann

Ehlenz²,

Görzen³

et al. 2022

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Learning Analytics provides plenty of pedagogical uses. However, the integration of learning analytics must be accompanied by different perspectives: technical, organizational, and pedagogical. At this point, there are still gaps, e.g., the need to connect the various stakeholders and support the systematic, structured, and sustainable process. This paper presents different approaches to making the learning data standard xAPI for interdisciplinary projects easier by working on other starting points. Starting with a basic infrastructure to support the interdisciplinary collection of definitions for the standardized data format, it continues with a graphical user interface supporting different stakeholders. A modular tool for quickly connecting programming IDEs with the vocabulary is also presented. Last, a connector is shown for easier multi-modal data management using virtual reality as an example.

show abstract

Section: Discussionmentioning

confidence: 99%

xAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects

Heinemann

Ehlenz²,

Görzen³

et al. 2022

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…Moore [5] identified three components of critical interaction in educational contexts: learner-content interaction (L-C), learner-instructor interaction (L-I) and learner-learner interaction (L-L). Nehiri and Aknin underlined that the contemporary learning continuum comprises formal, informal, non-formal learning, and collaboration [6]. Marquès Puig et al listed self-regulated learning strategies "(a) cognition (strategies to remember or elaborate information); (b) metacognition (planning, setting goals, monitoring, and evaluating); (c) motivation (enhance self-efficacy, intrinsic task interest); and, (d) behaviour (help-seeking, time management, and creating a positive learning environment for learning task)" [7, p. 210].…”

Section: Literature Reviewmentioning

confidence: 99%

Educational behaviour and student agency in personalised digital learning

Yakovleva¹,

Kulikova²

2022

P Sci Edu

View full text Add to dashboard Cite

Introduction. Digital educational practices with the opportunities for personalisation cause attention to the issues of students' educational behaviour peculiarities in the new conditions. In the modern situation of uncertainty, the changes in students’ position toward increasing agency, independence, and reflexion are relevant. It is advisable to analyse the promising vectors for changing the strategies of educational behaviour in terms of interaction, cooperation and orientation of students' agency. Materials and methods. The methodological basis of the study was a psychodidactic approach to designing a digital educational environment, the concept of agency and personalisation of education. We conducted the study in the Herzen University among the undergraduate students with the experience of studying e-courses with personalisation capabilities (N=50). The authors’ questionnaire was developed, aimed at students’ educational behaviour self-assessment in the conditions of e-learning at the beginning and at the end of studying a new e-course in the following areas: personal learning goals, knowledge acquisition strategies, knowledge application strategies, learning interaction strategies, and self-regulation strategies. Research results. The analysis of the results showed that in the process of personalised e-learning, there is a critical awareness of personal educational goals; the acquisition of new knowledge relies on an expanded source base. Strategies for applying knowledge set by the teacher are more acceptable. The priority of instrumentalisation of interaction over the use of new opportunities for cooperation is detected. Self-regulation is predominantly associated with pragmatism and planning. Conclusion. The experience of personalised digital learning affects the change in the strategies of students’ educational behaviour. There is an awareness of new opportunities for digital interaction and cooperation, and several types of agency – individual, proxy and collective. Problematic aspects are associated with an insufficient experience of cooperation in the digital environment and the prevailing stereotypes of behaviour according to a model and algorithm.

show abstract

“…They used IMS-LIP, IMSACCLIP and IMS-RDCEO standards and incorporated their characteristics to the proposed learner. In another work [16], a learner model based on xAPI statements is built. It combines the formal and informal tools to enrich the learner profile and ensure interoperability between systems.…”

Section: Related Workmentioning

confidence: 99%

A Social Profile Ontology to Enhance Learner Experience in MOOCs

Zankadi

Hilal²,

Idrissi

et al. 2022

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Learners are more active and interactive interactive in social media as they freely express themselves and share their thoughts and feeling which generates much useful information about their characteristics. However, the same learners are not usually interactive in MOOCs as much as they do in social media. Therefore, focusing on the Learner’s social profile is a key component towards a successful and enriched experience of learning. Especially that MOOCs offer the possibility to sign up using social media accounts. Thereby, profile modelling is an essential step to capture the knowledge about the learners. In this paper, we aim to explore the information generated by the learner’s participation in social media and combine it with his characteristics in MOOCs. Our approach is based on implementing an ontological learner profile that combines other ontologies (IMS-LIP standard, FOAF, and SIOC ontology) as well as some of the major concepts that describe a user in social media. The result of our solution is a social profile ontology (SPOnt) that describes the main concepts of a learner in each environment (MOOCs and social media). We rely on ontology mapping and merging techniques to maintain the semantic links between the different concepts of the two implemented ontologies.

show abstract

A Proposed Learner’s Data Model: Integrating Informal Learning and Enhancing Personalization and Interoperability

Cited by 6 publications

References 8 publications

xAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects

xAPI Made Easy: A Learning Analytics Infrastructure for Interdisciplinary Projects

Educational behaviour and student agency in personalised digital learning

A Social Profile Ontology to Enhance Learner Experience in MOOCs

Contact Info

Product

Resources

About