Using Virtual Reality to Enhance Forensic Science Education: Effects on CSI Learning Achievements, Situational Interest and Cognitive Load

Abstract

This study examined the effectiveness of an interactive VR-based platform (IVR-CSI) for crime scene investigation training in forensic science education. The IVR-CSI utilized real-case scenarios and head-mounted displays. University students majoring in criminal investigation (N = 71) participated, with 36 using IVR-CSI and 35 in the control group with traditional training. Pre-test, post-test, and delayed test assessments to measure learning achievements, situational interest, and cognitive load. Statistical analysis revealed that the experimental group demonstrated significantly better immediate learning outcomes and retained knowledge more effectively over time compared to the control group. The IVR-CSI also elicited higher situational interest across most dimensions, particularly in novelty, exploration intention, attention demand, and instant enjoyment. Importantly, IVR-CSI did not increase students’ intrinsic and extraneous cognitive load but enhanced their germane cognitive load. These results highlight the positive impact of IVR-CSI on CSI training, offering valuable insights for enhancing professional training programs and instructional methods in this field.

Highlights

1. The IVR-CSI platform was developed using virtual reality and real cases in forensic science education.

2. Experimental group showed better learning outcomes and knowledge retention than control group.

3. IVR-CSI increased situational interest dimensions such as novelty, enjoyment, and exploration.

4. Using IVR-CSI did not increase the intrinsic/extraneous load but improved the germane load.

5. This study shows the potential of VR to enhance professional skill training in CSI via situated learning.

Keywords:

• Forensic science
• crime scene investigation
• interactive virtual reality
• situational interest
• cognitive load

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Science and Technology Council, Taiwan (grant numbers 108-2511-H-261-001-MY2 and 110-2511-H-261-001).

Notes on contributors

Rong-Chi Chang

Rong-Chi Chang is an Associate Professor of the Department of Technology Crime Investigation at the Taiwan Police College. He received his M.S. and Ph.D. in computer science and information engineering from Tamkang University, Taiwan. His research interests include digital learning (AR/VR), cyber security, pattern recognition, and digital crime scene analysis.