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LIVE Research Projects

Learning Analytics for Data-Informed Decision-Making

A project that explores data-informed decision-making in education.

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Project at a Glance

This project explores data-informed decision-making in education, focusing on how teachers and students work with learning analytics to reflect on and adjust their teaching and learning activities. Through investigation of how they access analytics, make sense of the information provided, and translate it into action, the research identifies key factors shaping analytics use in authentic educational contexts and develops models to inform human-centered analytics design, implementation, and support.

Project Description

Learning analytics use educational data to help educators and learners better understand teaching and learning processes, presenting significant opportunities for more informed decision-making. Yet simply providing new types and greater amounts of data is not enough to ensure that the information becomes useful in practice. Effective processes of translating system-provided information into locally meaningful knowledge and subsequently using it to guide teaching or learning requires interpretation, judgment, and attention to context.
 
This project investigates the processes through which instructors and learners actually work with analytics in practice. For instructors, this includes elements of asking questions, reading and interpreting data, explaining patterns, using contextual knowledge to frame interpretation, and combining the insight generated with their pedagogical knowledge to decide if and how to take action. For students, the work examines aspects how analytics are accessed as well as how sensemaking, and action-taking fit into students' larger learning experiences rather than as isolated moments of tool adoption.
 
Across this work, analytics use is studied as situated within larger networks of teaching and learning practices. The work examines how use is shaped by existing routines, motivations and goals, as well as opportunities for reflection and dialogue. Findings are synthesized into both process and design models that identify key activities and leverage points for analytics design and implementation, with the goal of creating tools that are contextually relevant, actionable, and meaningfully integrated into teaching and learning.

The translation from information to insight to action is not a straightforward one.

Alyssa Wise, PI
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Learning Analytics for Data-Informed Decision-Making's Impact

Learning analytics have the potential to improve teaching and learning by helping educators and students use detailed information about recent learning activities to guide what they do next. But the difference between "nice to know" interesting information and "need to know" actionable insight is a central challenge. Understanding how people engage with and interpret analytic information, and the conditions under which that information becomes useful, is critical to realizing this potential.
 
For instructors, analytics can support a variety of activities, from understanding patterns in student learning and identifying student needs to providing individual or whole-class scaffolding, revising course design, and even at times reflecting on broader pedagogy. Research also shows that effective use depends on more than technical knowledge: instructors integrate pedagogical knowledge and contextual understanding into their analytics practices, and benefit from ongoing opportunities for guidance, feedback, reflection, and collaborative interpretation.
 
For students, analytics-informed support can provide timely information based on their learning activity and help them notice productive opportunities for engagement. Studies show that analytics can influence not only immediate actions but also awareness, reflection, and later adjustments to learning strategies and behaviors. At the same time, recent findings show that short-term engagement gains do not necessarily become sustained practices once support is removed, underscoring the importance of either designing analytics to build durable learning practices or treating them as tools for ongoing use rather than scaffolds intended to be faded.

Relevant Publications

Li, Q., Jung, Y., & Wise, A. F. [2026]. How instructors use learning analytics: the pivotal role of pedagogy. Journal of Computing in Higher Education, 38[1], 227-255.
 
Jung, Y., & Wise, A. F. [2025]. How students engage with learning analytics: Access, action-taking, and learning routines with message-based information to support collaborative annotation. Computers & Education, 232, 105280.
 
Wise, A. F., & Jung, Y. [2019]. Teaching with analytics: Towards a situated model of instructional decision-making. Journal of Learning Analytics, 6[2], 53-69.

The Team Behind the Work

Alyssa Wise | Vanderbilt University

Yeonji Jung | Texas A&M University

Alyssa Wise, PI