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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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Supporting the shift to digital with student-centered learning analytics.

Xavier Ochoa¹, Alyssa Friend Wise¹

¹Learning Analytics Research Network, New York University, 370 Jay Street, 5th Floor, New York, NY 11201 USA.

Educational Technology Research and Development : ETR & D

|November 30, 2020

Summary

This study proposes three shifts for effective learning analytics (LA): involve students in tool creation, develop explainable analytics, and empower student agency. These changes are crucial for ethical and responsible LA adoption in digital learning environments.

Keywords:

Data-informed decision-making Ethics Learning analytics Online learning Privacy Student agency

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Area of Science:

Educational Technology
Learning Analytics
Data Science in Education

Background:

Learning analytics (LA) offers potential for actionable educational insights, especially in digital learning environments.
LA can address information gaps and support self-regulation for online learners.
Ethical and responsible adoption of LA requires a student-centered approach, as highlighted by previous research on student perceptions of LA usefulness and privacy.

Purpose of the Study:

To address the gap in understanding the practical implications of student perceptions for learning analytics tool creation and adoption.
To propose three critical shifts in learning analytics practice to enhance student acceptance and effectiveness.
To advocate for a more participatory and student-empowering paradigm in learning analytics.

Main Methods:

This paper offers a practice-oriented response to existing research on student perceptions of learning analytics.
It outlines three necessary shifts in learning analytics practice.
The discussion is based on the implications of student-centered learning analytics.

Main Results:

Shift 1: Involve students in the co-creation of learning analytics tools designed to support them.
Shift 2: Develop learning analytics that are contextualized, explainable, and configurable to user needs.
Shift 3: Empower students' agency by integrating analytic tools into their broader learning processes.

Conclusions:

Rethinking learning analytics practice is essential for effective and ethical implementation.
Increased student involvement in the creation, interpretation, and impact of learning analytics is paramount.
These shifts are vital for fostering a student-centered paradigm in educational technology and learning analytics.