Sharing Student Projects

Exploring an Effective Workplace Learning Analytics Solution


Degree:
PGT
Programme:
MA Digital Technologies, Communication, and Education
Researcher:
Hwei San Seow
Keywords:
  • Qualitative
  • Workplace learning analytics (WPLA)
  • Learning and Development (L&D)
  • Focus group interviews
  • Behavioural shifts
  • Artificial Intelligence (AI)
  • Large Language Models
  • Tech start-up industry
Summary:

In the tech start-up industry, learning in the workplace is imperative for the workforce to remain relevant and adaptable to change for the business to succeed. While workplace learning (WPL) generally consists of formal learning using digital platforms and informal learning through communities of practice, most workplace learning analytics (WPLA) solutions do not reflect the holistic learning journey of the individual. There have been different studies about Workplace Learning and Learning Analytics. However, there are few works of literature on which both topics intersect, and even fewer considering the perspective of stakeholders. The purpose of this study is to explore an effective workplace learning analytics solution from the perspective of Learning and Development (L&D) Professionals and Learners presenting the perspective of ‘two sides of the same coin’ related to WPL. Employing qualitative research methods, such as email questionnaires and focus group interviews, this study found that there is a need to align individual growth with organisational goals within WPL. It advocates for an expanded scope of metrics incorporating qualitative insights and behavioural shifts beyond the conventional focus on quantitative data. This study proposes a WPLA framework that integrates micro-meso and macro-level analytics to provide a comprehensive view of the organisation’s overall learning program and the quality of the individual’s learning experience. In addition to the current quantitative data sets, WPLA should include data from dialogues between managers and team members as well as self and peer evaluation of skills. With the introduction of large sets of qualitative data, there is the potential of using Artificial Intelligence, in particular, Large Language Models, to support the analysis. Considering the complexity of the WPL process, the collaborative effort among learners and various stakeholders to collect relevant learning data is also crucial for WPLA to be effective.

Impact:

This research offers transformative insights into the workplace learning landscape, paving the way for future research and impactful Learning & Development practices.