Evolution of Interests in the Learning Context Data Model

Thüs, Hendrik; Chatti, Mohamed Amine; Brandt, R. van den; Schroeder, Ulrik

doi:10.1007/978-3-319-24258-3_43

Cited by 8 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Uptake has been described as the smallest unit in collaborative learning that can be observed as an interaction (Suthers & Dwyer, 2014). In our evaluation studies, uptake occurred at several places.…”

Section: An Aan To Support the Knowledge Creation Metaphormentioning

confidence: 86%

“…These visual abstractions are suitable for learners and trainers to visualize "uptake relations," which have been defined as the smallest units of meaning-making activities in knowledge creation (Suthers & Dwyer, 2014). For example, Figure 6 shows how the Dashboard represents these meaning-making activities by building a social network that is generated from the uptake relations arising from reciprocal contributions to a particular artifact.…”

Section: Sss Dashboardmentioning

confidence: 99%

“…Reuse of tags occurred in CC-15 and RW-16. A powerful analytic framework has recently been created that allows interactions to be analyzed in "contingency graphs" (Suthers & Dwyer, 2014). With the uptake framework, a big step has been taken toward making collaborative learning activity traceable for LA.…”

Section: An Aan To Support the Knowledge Creation Metaphormentioning

confidence: 99%

See 2 more Smart Citations

An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

et al. 2019

View full text Add to dashboard Cite

In this paper, we propose the Social Semantic Server (SSS) as a service-based infrastructure for workplace and professional learning analytics (LA). The design and development of the SSS have evolved over eight years, starting with an analysis of workplace learning inspired by knowledge creation theories and their application in different contexts. The SSS collects data from workplace learning tools, integrates it into a common data model based on a semantically enriched artifact-actor network, and offers it back for LA applications to exploit the data. Further, the SSS design's flexibility enables it to be adapted to different workplace learning situations. This paper contributes by systematically deriving requirements for the SSS according to knowledge creation theories, and by offering support across a number of different learning tools and LA applications integrated into the SSS. We also show evidence for the usefulness of the SSS extracted from 4 authentic workplace learning situations involving 57 participants. The evaluation results indicate that the SSS satisfactorily supports decision making in diverse workplace learning situations and allow us to reflect on the importance of knowledge creation theories for this analysis. Notes for Practice• We propose the Social Semantic Server (SSS) as a service-based infrastructure for workplace and professional learning analytics (LA) that focuses on knowledge creation theories.• We identify the requirements for the SSS and present its design and development.• We evaluated the SSS by integrating a set of learning tools and LA applications into the SSS and using it in 4 authentic workplace learning situations involving 57 participants.

show abstract

Section: An Aan To Support the Knowledge Creation Metaphormentioning

confidence: 86%

Section: Sss Dashboardmentioning

confidence: 99%

See 1 more Smart Citation

An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

et al. 2019

View full text Add to dashboard Cite

show abstract

“…This system thus could track the entire interest development process, right from triggered situational to well-developed individual interest. Thüs, Chatti, Brandt, and Schroeder (2015) in their model to track interest evolution acknowledged that the relationship between interest and object was quite fluid. This fluidity was further evident in their contribution, where they proposed a way to track and grow learner interest dynamically over time.…”

Section: Rq1: Formalization Of Interest Inmentioning

confidence: 99%

A Systematic Review of Technology-Driven Interest-Based Learning Environments

Kulkarni,

Chu

2024

Preprint

View full text Add to dashboard Cite

Personalized learning has been rising to the forefront in the past few decades. One promising form of personalized learning is Interest-Based Learning or IBL. IBL leverages a learner’s interests and integrates them into traditional learning to create relevance with the learning content. It seeks to positively impact learning-related variables such as motivation, attention, and self-efficacy. This approach, although explored in specific applications and contexts, lacks a holistic review of its development over the years, particularly when dealing with its manifestation through technology. This systematic review aims to fill in some of the critical gaps by analyzing: (i) the conceptualization of interest within IBL; (ii) the implementation of IBL strategies; and (iii) the integration of technologies in technology-driven IBL environments. Based on a review of the literature, existing problems and challenges for technology-driven IBL are identified, along with potential research gaps and future directions.

show abstract

“…Thus, during the Prime Project, Thüs et al [4] developed the Learning Context Data Model (LCDM), which is a learning record data definition aiming to provide a structural way to collect information not only on the learning process itself but also on the context of the learner [5,6]. Furthermore, this model is not only platform agnostic, it provides a structured pattern for storing the platform, source, session, action and general learning setting (work, academic, private); it introduced a hierarchical category system, with entities to model user interests [7] and generic entities.…”

Section: Intention Of the Learning Context Data Modelmentioning

confidence: 99%

A Learning Analytics Approach in Web-Based Multi-user Learning Games

Ehlenz

Leonhardt

Schroeder

2019

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

As technology changes, learning games are adapted to target audience and available devices. Analytics methods must keep up with keeping the learner in focus. This work presents the Multi-Touch Learning Game (MTLG) framework, designed to implement cross platform educational games with support for cooperative, collaborative and competitive settings. It shows adaption of a user-centred learning analytics data model, the learning data context model, to fit circumstantial requirements of multi-user settings on a shared device in games implemented using the MTLG framework. A first field study has been conducted, and the results, challenges and lessons learned are discussed.

show abstract

Evolution of Interests in the Learning Context Data Model

Cited by 8 publications

References 6 publications

An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

A Systematic Review of Technology-Driven Interest-Based Learning Environments

A Learning Analytics Approach in Web-Based Multi-user Learning Games

Contact Info

Product

Resources

About