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Leveraging Learning Analytics to Model Student Engagement in Graduate Statistics: A Problem-Based Learning Approach

Zhihong Xu1, Fahmida Husain Choudhury1, Shuai Ma1

  • 1Department of Agricultural Leadership, Education and Communications, Texas A&M University, College Station, TX 77843, USA.

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Graduate students

Keywords:
diverse learnerslearning management system (LMS)mixed methodsproblem-based learning (PBL)self-efficacystudent engagement

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Area of Science:

  • Educational Technology
  • Statistics Education
  • Learning Analytics

Background:

  • Graduate students with limited math backgrounds struggle with statistics.
  • Learning Management Systems (LMS) and Problem-Based Learning (PBL) are common but under-researched in mixed-methods studies.
  • Machine learning in Learning Analytics (LA) offers potential for understanding student behavior.

Purpose of the Study:

  • To examine student engagement patterns on Canvas and learning outcomes in a graduate statistics course.
  • To explore the relationship between LMS engagement, PBL, and academic performance.
  • To identify distinct student engagement behaviors using mixed methods.

Main Methods:

  • Explanatory sequential mixed methods design.
  • Collected LMS log data and survey responses from 31 graduate students.
  • Conducted K-means clustering on log data and thematic analysis of interviews with 19 students.

Main Results:

  • K-means clustering identified two groups: high-performing (lower LMS engagement) and low-performing (higher LMS engagement).
  • Thematic analysis revealed differences in engagement behavior, assessment roles, emotional struggles, self-efficacy, and perceived learning.
  • Low-performing students benefited from structured guidance and repeated exposure, engaging more frequently.

Conclusions:

  • Student engagement patterns on LMS vary significantly and correlate with performance in graduate statistics.
  • Low-performing students benefit from structured support and frequent engagement, while high-performers exhibit proactive habits.
  • Course design integrating PBL and LMS features is crucial for supporting diverse graduate learners in statistics.