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Title: Investigating the Impact of Context Choice on Learning Experience via Immersive Simulations in an Object-Oriented Programming Course
Researchers have looked into ways to make computer science assignments more engaging, practical, and beneficial to students to improve learning outcomes by increasing student appeal. Offering a pool of assignments and allowing students to choose their preferred assignments is considered as a potential method for improving learning outcomes. In this paper, we investigate the effect of context choice for assignments in an object-oriented programming course that covers various topics such as object-oriented programming concepts, database design and implementation, graphical user interface design, and web application development. Students complete three immersive simulation-based learning (ISBL) modules as course assignments. ISBL modules involve technology-enhanced problem-based learning where the problem context is represented via a three-dimensional (3D), animated discrete-event simulation model that resembles a real-world system or context, in this case, we have three simulated systems/contexts around which ISBL assignments are defined: an airport, a manufacturing system, and a hospital emergency department. The research experiments involve four groups: (1) students with no choice who use the same assigned simulated system for all three ISBL assignments; (2) students with no choice who are given a different simulated system for each ISBL assignment; (3) students who can choose their preferred simulated system at the beginning but cannot change their choice for future assignments; and, (4) students who can choose at the beginning and switch between the three simulated systems for subsequent assignments. Data are collected over multiple semesters and statistical analyses are conducted to compare the four groups in terms of motivation, experiential learning, and self assessment of learning. We also conduct qualitative assessments in the form of interviews to support and explain our statistical results.  more » « less
Award ID(s):
2000599
NSF-PAR ID:
10415732
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ASEE Annual Conference proceedings
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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