Modeling and Visualizing Student Flow

Raji, Mohammad; Duggan, John; DeCotes, Blaise; Huang, Jian; Zanden, Bradley Vander

doi:10.1109/tbdata.2018.2840986

Cited by 7 publications

(10 citation statements)

References 33 publications

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“…Yet, these matrix-based models can offer greater insights into specific courses and provide the ability to incorporate teaching and learning components. The same can be said with more complex models focusing on intercourse relationships and student pathways (Raji et al, 2017), where the focus was to visualize student trajectories of their university experience.…”

Section: Integrating Course-level Elementsmentioning

confidence: 98%

“…Therefore, it is dangerous to make assumptions associated with the student reasoning behind the pattern. Data associated with behaviours needs to be considered and complimentary models created to better understand the diversity of student behaviours (e.g., Brennan et al, 2019;Raji et al, 2017). Use of this model should focus on cohort-level patterns and responses to course-and program-level actions.…”

Section: Using the Model: Identifying And Incorporating Solutionsmentioning

confidence: 99%

“…However, courses do not exist in isolation, and the connectivity and progression between courses is a challenging component of learning analytics (Raji, Duggan, DeCotes, Huang, & Vander Zanden, 2017). At the within-course scale, learning analytics can identify engagement as well as immediate actions that an instructor can undertake to improve student outcomes within that course.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between course-level variables such as instructor and student engagement, and program-level variables such as program design and retention of skills by students, requires first to understand student progression and the definition of relevant metrics. While the data may be available at institutions, visualizing student flows is challenging (Raji et al, 2017), let alone understanding the drivers behind the observed patterns.…”

Section: Introductionmentioning

confidence: 99%

“…The progression of students through a program is a relatively new topic within program-level analytics (Raji et al, 2017). It is useful for identifying the typical progression of students (Jeffreys, 2015;Shah & Burke, 1999) and identifying significant courses (Asif, Merceron, Ali, & Haider, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Scaling the Student Journey from Course-Level Information to Program Level Progression and Graduation: A Model

Munguia

Brennan²

2020

JLA

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No course exists in isolation, so examining student progression through courses within a broader program context is an important step in integrating course-level and program-level analytics. Integration in this manner allows us to see the impact of course-level changes to the program, as well as identify points in the program structure where course interventions are most important. Here we highlight the significance of program-level learning analytics, where the relationships between courses become clear, and the impact of early-stage courses on program outcomes such as graduation or drop-out can be understood. We present a matrix model of student progression through a program as a tool to gain valuable insight into program continuity and design. We demonstrate its use in a real program and examine the impact upon progression and graduation rate if course-level changes were made early on. We also extend the model to more complex scenarios such as multiple program pathways and simultaneous courses. Importantly, this model also allows for integration with course-level models of student performance.

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Section: Integrating Course-level Elementsmentioning

confidence: 98%

Section: Using the Model: Identifying And Incorporating Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%