This sub-topic covers various learning analytics strategies and processes that might apply to the investigation stage of learning engineering including:
- Quantitative and Qualitative Data Analysis: Learning engineering teams analyze both quantitative data (e.g., learning experience and performance data) and qualitative data (e.g., surveys, focus groups, data jams) to understand the experiences, conditions, learner differences that might contribute to or hinder the desired learning. Some analytics techniques required fall into the more general data analytics category, others are learning analytics-specific. (See “Knowledge Area 8: Learning Analytics”)
- Environmental Context Analysis: Analysis of the physical, technological, and social contexts in which learning will occur, such as classroom settings, online platforms, or hybrid environments. This ensures that the solution will be compatible with the resources and conditions available.
- Other Factors: There are an unlimited number of factors that might hinder or prevent a person from learning. The quantitative and qualitative data analysis involved in an investigation stage of the learning engineering process may involve analysis of datasets unrelated directly to learning. For example, transportation routes to get all students to a physical school or staffing scheduled for educators or network traffic analysis to discover solutions to latency issues impacting online learning.
Refer to Knowledge Area 8 for specific learning analytics topics and subtopics that might be employed during investigation.