Designing Culturally Aware Learning Analytics: A Value Sensitive
Perspective
- URL: http://arxiv.org/abs/2212.09645v1
- Date: Mon, 19 Dec 2022 17:23:03 GMT
- Title: Designing Culturally Aware Learning Analytics: A Value Sensitive
Perspective
- Authors: Olga Viberg, Ioana Jivet, Maren Scheffel
- Abstract summary: This chapter aims to stress the importance of addressing culture when designing and implementing learning analytics services.
We argue for a need to carefully consider one of these factors, namely cultural values when designing and implementing learning analytics systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This chapter aims to stress the importance of addressing culture when
designing and implementing learning analytics services. Learning analytics have
been implemented in different countries with the purpose of improving learning
and supporting teaching; yet, largely at a limited scale and so far with
limited evidence of achieving their purpose. Even though some solutions seem
promising, their transfer from one country to another might prove challenging
and sometimes impossible due to various technical, social, contextual and
cultural factors. In this chapter, we argue for a need to carefully consider
one of these factors, namely cultural values when designing and implementing
learning analytics systems. Viewing culture from a value-sensitive perspective,
in this chapter, we: 1)exemplify two selected values (i.e. privacy and
autonomy) that might play a significant role in the design of learning
analytics systems, and 2)discuss opportunities for applying culture-and
value-sensitive design methods that can guide the design of culturally aware
learning analytics systems. Finally, a set of design implications for
culturally aware and value-sensitive learning analytics services is offered.
Related papers
- CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts [45.77570690529597]
We introduce CROPE, a visual question answering benchmark designed to probe the knowledge of culture-specific concepts.
Our evaluation of several state-of-the-art open Vision and Language models shows large performance disparities between culture-specific and common concepts.
Experiments with contextual knowledge indicate that models struggle to effectively utilize multimodal information and bind culture-specific concepts to their depictions.
arXiv Detail & Related papers (2024-10-20T17:31:19Z) - Extrinsic Evaluation of Cultural Competence in Large Language Models [53.626808086522985]
We focus on extrinsic evaluation of cultural competence in two text generation tasks.
We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts.
We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
arXiv Detail & Related papers (2024-06-17T14:03:27Z) - CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection.
It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs.
We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
arXiv Detail & Related papers (2024-05-24T01:49:02Z) - Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense [98.09670425244462]
Large language models (LLMs) have demonstrated substantial commonsense understanding.
This paper examines the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks.
arXiv Detail & Related papers (2024-05-07T20:28:34Z) - What You Use is What You Get: Unforced Errors in Studying Cultural Aspects in Agile Software Development [2.9418191027447906]
Investigating the influence of cultural characteristics is challenging due to the multi-faceted concept of culture.
Cultural and social aspects are of high importance for their successful use in practice.
arXiv Detail & Related papers (2024-04-25T20:08:37Z) - Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
This paper introduces a novel approach for massively multicultural knowledge acquisition.
Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages.
Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
arXiv Detail & Related papers (2024-02-14T18:16:54Z) - Navigating Cultural Diversity: Barriers and Potentials in Multicultural
Agile Software Development Teams [2.2044574002571182]
The aim of this study is to identify barriers and potentials that may arise in multicultural agile software development teams.
Our results suggest that the cultural characteristics at the team level need to be analyzed individually in intercultural teams.
Third, we derived strategies supporting the potentials of cultural diversity in agile software development teams.
arXiv Detail & Related papers (2023-11-18T19:27:48Z) - Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in
Large Language Models [89.94270049334479]
This paper identifies a cultural dominance issue within large language models (LLMs)
LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
arXiv Detail & Related papers (2023-10-19T05:38:23Z) - Introducing Practicable Learning Analytics [0.0]
This book introduces the concept of practicable learning analytics to illuminate what learning analytics may look like from the perspective of practice.
We use the concept of Information Systems Artifact (ISA) which comprises three interrelated subsystems: the informational, the social and the technological artefacts.
The ten chapters in this book are presented and reflected upon from the ISA perspective, clarifying that detailed attention to the social artefact is critical to the design of practicable learning analytics.
arXiv Detail & Related papers (2023-01-26T21:20:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.