Using Online Digital Data to Infer Valuable Skills for the Modern Workforce

Using Online Digital Data to Infer Valuable Skills for the Modern Workforce

Sofia Strukova, José A. Ruipérez-Valiente
DOI: 10.4018/978-1-6684-3996-8.ch005
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This chapter uncovers the opportunities that online media portals like content sharing and consumption sites or photography sites have for informal and formal learning. The authors explored online portals that can provide evidence of evaluating, inferring, measuring skills, and/or contributing to the development of competencies and capabilities of the 21st century with two case studies. The first one is focused on identifying data science topical experts across the Reddit community. The second one uses online Flickr data to apply a model on the photographs to rate them as aesthetically attractive and technically sound, which can serve as a base for measuring the photography capabilities of Flickr users. The presented results can serve as a base to replicate these methodologies to infer other competencies and capabilities across online portals. This novel approach can be an effective alternative evaluation of key 21st century skills for the modern workforce with multiple applications.
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In today’s context, there are more people that strongly believe that a significant proportion of learning, both intentional and unintentional, is happening online. They believe online learning is increasing due to two different reasons: the ever-growing popularity of internet usage by all segments of the population and the appearance of more and more socio-political and climate displacements as well as health emergencies like COVID, that are demanding remote work and education modalities. According to the UNESCO report regarding the impact of COVID-19 on education (UNESCO, 2021), almost half of the world’s students are still impacted by partial or complete school closures. In this sense, the education and training stakeholders have had to adapt to students’ new needs and abilities by creating modern new media and online training techniques to provide flexible and personalized educational approaches. Moreover, the pandemic has caused traumatic circumstances in many people’s lives, making it harder for learners struggling to adapt to online learning (Carter et al., 2020). One of the solutions to this problem is to create easy-to-access, engaging, online educational opportunities, including resources like electronic books, recorded lectures, quizzes, podcasts, discussion forums, engendering social connectedness and/or facilitating one-on-one check-ins or group interviews. Even with the latter list, finer nuances are needed to be investigated, implemented, monitored, and evaluated for successful educational engagement.

Due to the pandemic context, many schools and universities around the world are integrating online learning into their courses, starting from formal and structured Learning Management Systems (LMSes), such as Moodle or Sakai, to informal social networking sites (SNSs), such as Facebook, Pinterest, Reddit, YouTube, or Flickr (Ulla & Perales, 2021), Question and Answer (Q&A) portals like Quora, and many others. The use of these environments is becoming more common as it can help improve missing social aspects and academic social connectedness (Mentor, 2018), which refers to a sense of affiliation, belonging, and not working alone (Thai et al., 2019; Turki et al., 2018). However, many LMSes do not provide the functionality to facilitate engaging ways to interact with peers, cultivate and maintain academic social connectedness (Mentor, 2018), or exchange feedback synchronously. More than that, with the use of SNSs, students can present themselves, articulate their thoughts to their social networks, develop or preserve connections with peers and share information, knowledge, and artifacts within a community. Moreover, SNSs promote the creation of shared interest groups that make users feel a sense of community engagement (McCarthy, 2017).

Therefore, if research thus far has shown that students benefit from learning methods that involve the use of SNSs as part of their curriculum (Shih, 2012), could users learn in these and similar sites without being part of a formal educational program? In this chapter, the researchers will focus their attention on a trending and controversial issue of particular importance related to the informal learning happening across various online portals capable of hosting evidence of users’ developing competencies and capabilities. The authors believe that learning online, interpreting, or analyzing photographs as forms of media that can be transferable to other aspects of learning and working life, and data science, and self-directed learning capabilities are important to successfully function in the 21st- century society. Therefore, this chapter will discuss the opportunities that online digital media provide to evaluate and generate online learning, self-directed learning, offer formative assessments and feedback on learners’ skills. The purpose of this study is to solicit discussion and will help to deliberate the affordances of engaging with new online media tools to transfer 21st-century skills and show how the modern workforce can benefit from these approaches.

Thus, the objectives of this chapter are as follows:

Key Terms in this Chapter

Self-Regulated Learning: the ability of individuals to understand and control their learning environment.

Informal Learning: Learning happening outside of a structured, formal classroom environment.

Application Programming Interface (API): A set of functions that allows building and integrating applications’ software.

Information Retrieval: An automatic process, methods, and procedures of searching and obtaining data that are relevant to an information need.

Machine Learning (ML): A branch of computer science that uses data and specific algorithms to imitate how humans think and learn.

Formative Assessment: A wide variety of methods that teachers use to evaluate students’ comprehension, learning needs, and academic progress during an educational process.

Data Science: An interdisciplinary field whose objective is to extract and interpret knowledge and insights using scientific methods, processes, algorithms, and systems.

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