This study aims to investigate the collaboration processes of immigrant families as they search for online information together. Immigrant English-language learning adults of lower socioeconomic status often work collaboratively with their children to search the internet. Family members rely on each other’s language and digital literacy skills in this collaborative process known as online search and brokering (OSB). While previous work has identified ecological factors that impact OSB, research has not yet distilled the specific learning processes behind such collaborations.
Design/methodology/approach: For this study, the authors adhere to practices of a case study examination. This study’s participants included parents, grandparents and children aged 10–17 years. Most adults were born in Mexico, did not have a college-degree, worked in service industries and represented a lower-SES population. This study conducted two to three separate in-home family visits per family with interviews and online search tasks.
Findings: From a case study analysis of three families, this paper explores the funds of knowledge, resilience, ecological support and challenges that children and parents face, as they engage in collaborative OSB experiences. This study demonstrates how in-home computer-supported collaborative processes are often informal, social, emotional and highly relevant to solving information challenges.
Research limitations/implications: An intergenerational OSB process is different from collaborative online information problem-solving that happens between classroom peers or coworkers. This study’s research shows how both parents and children draw on their funds of knowledge, resilience and ecological support systems when they search collaboratively, with and for their family members, to problem solve. This is a case study of three families working in collaboration with each other. This case study informs analytical generalizations and theory-building rather than statistical generalizations about families.
Practical implications: Designers need to recognize that children and youth are using the same tools as adults to seek high-level critical information. This study’s model suggests that if parents and children are negotiating information seeking with the same technology tools but different funds of knowledge, experience levels and skills, the presentation of information (e.g. online search results, information visualizations) needs to accommodate different levels of understanding. This study recommends designers work closely with marginalized communities through participatory design methods to better understand how interfaces and visuals can help accommodate youth invisible work.
Social implications: The authors have demonstrated in this study that learning and engaging in family online searching is not only vital to the development of individual and digital literacy skills, it is a part of family learning. While community services, libraries and schools have a responsibility to support individual digital and information literacy development, this study’s model highlights the need to recognize funds of knowledge, family resiliency and asset-based learning. Schools and teachers should identify and harness youth invisible work as a form of learning at home. The authors believe educators can do this by highlighting the importance of information problem solving in homes and youth in their families. Libraries and community centers also play a critical role in supporting parents and adults for technical assistance (e.g. WiFi access) and information resources.
Originality/value: This study’s work indicates new conditions fostering productive joint media engagement (JME) around OSB. This study contributes a generative understanding that promotes studying and designing for JME, where family responsibility is the focus.
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Automatic text generation using deep learning: providing large-scale support for online learning communities
Participating in online communities has significant benefits to students learning in terms of students’ motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to explore the use of deep learning algorithms for automatic text generation in providing emotional and community support for a massive online learning community, Scratch. Particularly, state-of-art deep learning language models GPT-2 and recurrent neural network (RNN) are trained using two million comments from the online learning community. We then conduct both a readability test and human evaluation on the automatically generated results for offering support to the online students. The results show that the GPT-2 language model can provide timely and human-written like replies in a style genuine to the data set and context for offering related support.
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- Award ID(s):
- 1901704
- NSF-PAR ID:
- 10315141
- Date Published:
- Journal Name:
- Interactive learning environments
- ISSN:
- 1049-4820
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/10494820.2021.1993932
