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Suvadeep Mukherjee

Publications and source records attributed to Suvadeep Mukherjee.

2 recordsLinked to original sources

Can Theory-Informed Message Framing Drive Honest and Motivated Performance with Better Assessment Experiences in a Remote Assessment?

Remote unproctored assessments increasingly use messaging interventions to reduce cheating, but existing approaches lack theoretical grounding, focus narrowly on cheating suppression while overlooking performance and experience, and treat cheating as binary rather than continuous. This study examines whether messages based on 15 psychological concepts from self-determination, cognitive dissonance, social norms, and self-efficacy theories can reduce cheating while preserving performance and experience. Through an expert workshop (N=5), we developed 45 theory-informed messages and tested them with online participants (N=1232) who completed an incentivized anagram task. Participants were classified as non-cheaters (0% items cheated), partial-cheaters (1-99% cheated), or full-cheaters (100% cheated). Results show that concept-based messages reduced full-cheating occurrence by 42% (33% to 19%), increased non-cheating by 19% (53% to 63%), with no negative effects on performance or experience across integrity groups. Surprisingly, messages grounded in different theoretical concepts produced virtually identical effects. Analyses of self-rated psychological mechanisms revealed that messages influenced multiple mechanisms simultaneously rather than their intended targets, though these mechanisms predicted behavior, performance, and experience. These findings show that causal pathways are more complex than current theories predict. Practically, integrity interventions using supportive motivation rather than rule enforcement can reduce cheating without impairing performance or experience.

cs.HC

Balancing The Perception of Cheating Detection, Privacy and Fairness: A Mixed-Methods Study of Visual Data Obfuscation in Remote Proctoring

Remote proctoring technology, a cheating-preventive measure, often raises privacy and fairness concerns that may affect test-takers' experiences and the validity of test results. Our study explores how selectively obfuscating information in video recordings can protect test-takers' privacy while ensuring effective and fair cheating detection. Interviews with experts (N=9) identified four key video regions indicative of potential cheating behaviors: the test-taker's face, body, background and the presence of individuals in the background. Experts recommended specific obfuscation methods for each region based on privacy significance and cheating behavior frequency, ranging from conventional blurring to advanced methods like replacement with deepfake, 3D avatars and silhouetting. We then conducted a vignette experiment with potential test-takers (N=259, non-experts) to evaluate their perceptions of cheating detection, visual privacy and fairness, using descriptions and examples of still images for each expert-recommended combination of video regions and obfuscation methods. Our results indicate that the effectiveness of obfuscation methods varies by region. Tailoring remote proctoring with region-specific advanced obfuscation methods can improve the perceptions of privacy and fairness compared to the conventional methods, though it may decrease perceived information sufficiency for detecting cheating. However, non-experts preferred conventional blurring for videos they were more willing to share, highlighting a gap between the perceived effectiveness of the advanced obfuscation methods and their practical acceptance. This study contributes to the field of user-centered privacy by suggesting promising directions to address current remote proctoring challenges and guiding future research.

cs.HC