PROFIL GELOMBANG OTAK DAN PERFORMA KOGNITIF MAHASISWA SITH-S ITB TERPAPAR STRES AKADEMIK MELALUI ANALISIS PENGUKURAN ELEKTROENSEFALOGRAM (EEG)
Several factors influence students' stress levels and well-being during their studies, one of which is the academic workload. However, the stress caused by academic demands can either be motivating or mentally burdensome, further affecting students' cognitive performance. This study aims t...
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Format: | Theses |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/87625 |
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Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | Several factors influence students' stress levels and well-being during their studies, one of which is the academic workload. However, the stress caused by academic demands can either be motivating or mentally burdensome, further affecting students' cognitive performance. This study aims to analyze the stress profile of SITH-S ITB students due to academic workload and its impact on cognitive performance, as measured by brain wave activity recordings using electroencephalography (EEG). The study began by distributing the Perception of Academic Stress Scale (PASS) questionnaire to categorize participants into high-stress (HS) and low-stress (LS) groups. Data on students' well-being were then collected using the College Student Wellbeing Questionnaire (CSWQ), followed by information on sleep duration and the most recent semester GPA. A total of 21 participants were classified as HS and 18 participants as LS, then underwent a series of EEG measurements, including pre-baseline (initial resting state), stress-state (academic stress induction), a cognitive test called the Paced Auditory-Visual Serial Addition Test (PAVSAT) to assess working memory (WM) as an indicator of cognitive performance, and post-baseline (final resting state). The entire experiment lasted 10 minutes. Brain wave recordings were conducted using the Muse™ EEG device to capture theta, alpha, and beta wave activity in the anterior frontal (AF) and temporoparietal (TP) regions of both brain hemispheres. The recorded brain waves were extracted into Power Spectral Density (PSD) values to analyze their absolute power distribution and were further used to calculate Frontal Alpha Asymmetry (FAA) and Theta/Beta Ratio (TBR). Qualitative data (PASS and CSWQ scores, sleep duration) were used for correlation analysis and served as predictor variables in multiple linear regression models to predict EEG features related to academic performance (based on GPA) and cognitive performance (based on PAVSAT results). The qualitative analysis showed that CSWQ scores were positively correlated with GPA, whereas both were negatively correlated with PASS scores. Academic year was found to be a more significant predictor of academic performance than questionnaire scores or sleep duration. The analysis of theta wave distribution showed the highest values during the PAVSAT test across all channels, indicating that increased theta activity plays a role in WM, with both groups demonstrating similar WM capacity. The distribution of alpha waves fluctuated in response to stress conditions, and the values in the TP10 channel were a significant predictor of PASS scores. The beta wave PAVSAT’s value distribution differed in the AF8 channel, reflecting differences in cognitive processing between the two groups. FAA analysis in the LS group suggested more motivated behavior compared to the HS group. TBR analysis in the HS group indicated increased mind wandering or lack of focus, likely due to academic stress, as supported by their lower average PAVSAT scores and longer response latencies. The TBR
index in the AF region was a significant predictor of cognitive performance latency. Overall, there was no significant difference in EEG PSD values between the HS and LS groups, suggesting that all participants experienced stress within a normal range. This study highlights EEG activity profiles that suggest academic stress influences both academic and cognitive performance. The novelty of this research lies in the use of EEG analysis to predict the effects of natural academic stress exposure on cognitive performance, evaluated alongside questionnaire results. Thus, this study can serve as a reflection for developing better learning methods to enhance student motivation and performance.
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