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"Dalam era digital ini, pembelajaran dengan metode e-learning menjadi solusi yang umum diimplementasikan pada pendidikan jarak jauh. Kekurangan dari metode e- learning ini yaitu minimnya informasi pengajar mengenai antusiasme dan tingkat partisipasi siswa dalam pembelajaran. Masalah tersebut dapat diselesaikan dengan sistem yang mampu mendeteksi engagement siswa. Tingkat engagement siswa pada e-learning dapat ditentukan dari pandangan siswa dan ekspresi wajah siswa dalam pembelajaran. Sistem pendeteksi engagement siswa bekerja dengan cara mendeteksi arah mata siswa dan ekspresi wajah siswa menggunakan teknologi OpenCV dengan metode CNN (convolutional neural network) pada input file berupa video atau webcam secara real-time. Sistem akan memberikan output berupa nilai engagement siswa “engaged” berdasarkan durasi mata siswa menatap layar dan ekspresi wajah siswa berupa ekspresi netral atau positif. Sistem akan memberikan output berupa nilai kehadiran siswa “disengaged” berdasarkan durasi mata siswa tidak menatap layar dan ekspresi wajah siswa menunjukkan ekspresi negatif. Sistem menganalisis reaksi emosi siswa yang direpresentasikan dalam parameter nilai persentase reaksi netral, positif, dan negatif menggunakan dataset FER-2013. Sistem pendeteksi engagement siswa dapat mengukur presensi, status attendance siswa memperhatikan layar, emosi, impresi dan status engagement siswa dengan tingkat akurasi sebesar 83,33%, presisi sebesar 100%, recall sebesar 66,67% dan f1 score sebesar 80,00%.

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In this digital era, the e-learning method is a common solution implemented on distance learning. The disadvantage of the e-learning process is the facilitator has no idea about students' enthusiasm and participation rate during a lecture. This problem could be solved by a student engagement detection system. Student engagement can be determined by capturing the student's eye-gazing focus rate and student's facial expression during an online lecture. The student engagement detection system works by detecting student eye gaze and facial expression using OpenCV technology and CNN (convolutional neural network) method, receiving input through video file input or real-time webcam feed. The system will report on the student engagement level “engaged” if the student's eyes are staring at the screen and student facial expression showing a neutral or positive impression. The system will report on the student engagement level “disengaged” if the student's eye gaze were away from the screen and student facial expression showing a negative impression. This system will analyze student's emotional reactions which represented by neutral, positive, or negative reaction percentage value using the FER-2013 dataset. Student Engagement Detection System could calculate student presence, attendance rate calculated through eye gaze focus rate, emotional reaction, impression and engagement status with an accuracy of 83,33%, a precision of 100%, recall of 66,67%, and f1 score 80,00%."

"Penelitian ini bertujuan untuk melihat peran self-regulated learning terhadap student engagement pada siswa atlet di Sekolah Khusus Olahragawan Ragunan DKI Jakarta. Partisipan penelitian ini adalah 96 siswa atlet di Sekolah Khusus Olahragawan Ragunan DKI Jakarta yang berada pada jenjang pendidikan SMA dengan rentang usia 15 sampai 18 tahun. Data yang diperoleh diolah menggunakan metode kuantitatif, variabel self-regulated learning diukur dengan Academic Self-Regulated Learning Scale (A-SRL-S) dan variabel student engagement diukur menggunakan Student Engagement Scale (SES). Hasil analisis regresi linear menunjukkan bahwa self-regulated learning (F = 65.417, p < .05) dapat memprediksi student engagement dengan R² = .404 artinya 40% varians skor student engagement dapat dijelaskan oleh self-regulated learning. Hasil penelitian ini memperjelas arah hubungan peran self-regulated learning terhadap student engagement adalah positif. Semakin tinggi skor self-regulated learning yang diperoleh partisipan maka semakin tinggi juga skor student engagement partisipan.

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This study aims to examine the role of self-regulated learning on student engagement in student athletes at the Ragunan Special School for Athletes, Jakarta, Indonesia. The participants of this study were 96 high school level student athletes at the Special School for Athletes in Ragunan, Jakarta, Indonesia with an age range of 15 to 18 years. The data obtained were processed using quantitative methods, self-regulated learning variables were measured using the Academic Self-Regulated Learning Scale (A-SRL-S) and student engagement variables were measured using the Student Engagement Scale (SES). The results from the linear regression analysis showed that self-regulated learning (F = 65,417, p < .05) could predict student engagement with R² = .404, meaning that 40% of the variance in student engagement scores could be explained by self-regulated learning. The results of this study clarify that the relationship between the role of self-regulated learning and student engagement is positive. The higher the self-regulated learning score obtained by the participants, the higher the participant's student engagement score."