EDM aims to leverage data analysis techniques to gain insights into student learning, identify patterns, and make data-driven decisions to enhance educational practices.

Teacher evaluation data is typically not collected in EDM, as the focus is primarily on student-related data.

LA aims to collect, analyze, and interpret data about students' learning to gain insights into their progress, identify areas of improvement, and make data-driven decisions to enhance learning outcomes.

Action research is a qualitative research method that involves collecting and analyzing data to inform and improve educational practices. It is not typically used in EDM and LA, which focus on quantitative data analysis.

Data-driven decision-making refers to the process of using data to inform educational practices and make evidence-based decisions about teaching and learning.

EDM and LA can help streamline administrative processes and reduce workload by automating tasks and providing data-driven insights for decision-making.

Educational data science is a field that combines EDM and LA techniques with educational research to improve teaching and learning.

EDM and LA implementation costs can vary depending on the scale and complexity of the project, but they are generally not considered to be prohibitively high.

Learning analytics involves the collection, analysis, and interpretation of data about students' learning to gain insights into their progress and identify areas for improvement.

While EDM and LA can be used to evaluate the effectiveness of online courses, this is not a common application in higher education.

Personalized learning involves tailoring instruction and learning experiences to meet the individual needs, strengths, and interests of each student.

Decision trees are not typically used for data visualization in EDM and LA. They are more commonly used for machine learning and data mining tasks.

Early warning systems use data to identify students who are at risk of academic failure or dropping out, allowing for early intervention and support.

Cost-effectiveness is not typically considered an ethical concern in EDM and LA, as the focus is primarily on the ethical use and interpretation of data to improve teaching and learning.

Impact evaluation involves using data to assess the effectiveness of educational interventions and programs in achieving their intended outcomes.