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"Students' knowledge on system for open and distance learning (ODL) is important since the information will be usefull in improving students' services. This article discusses students' understanding on ODL system and factors that affect it. The sample were 380 students from UPBJJ-UT Banda Aceh. Data collection was carried out from May to October 2011. Correlation test of Rank Spearman was used to analyze the data. The result showed that (1) students' knowledge on distance learning system was low (2) factors which influences students knowledge on ODL system were ability in using internet, study group and information access."

"This paper adresses the Universitas Terbuka (UT) students' perception on quality assurance (QA) system of distance education, using an online survey method involving 306 students. The UT students' perception on QA systm is analyzed in terms of profile of respondents, perception on important values of QA, students of satisfaction on the uquality of distance education programs and courses. The profile of the respondents shows that most of them are within the age of 25 to 30 years old. They study at UT mostly by means of reading printed materials and interactive online studies at home and at no particular place in the evenings. Students' difficulties in distance related to conflicts with work responsibilities, lack of time and self motivation. Students said that they needed both academic and social psychological support. Students' perception on important values on QA was expressed in terms of the availability and clear for QA system in the institution. In terms of institutional credibility, students stated thar external accreditation and qualified staff are key factors to institutional quality. In terms of learning process, students valued highly the importance of well structured courses and interactivity in the learning process. Students also stated that media technology supports, faculty support, and fair assessment are important in the quality of teaching learning at a distance. In terms of learning experience, they perceived that protection student rights, course content, and technology infrastructure were well facilitated by the institution."

"Open and distance learning (ODL) connects learners across geographical boundaries.Through the support of the internet and learning management systems (LSM),learners nowadyas are conveniently learning and communicating via the onlone mode....."

"Present seeks to analyze the impact of training programmes on the profesional development skills of the academics working primarily at the regional centers of Indira Gandhi National open university spread across the country...."

"Universitas Terbuka (UT) menyediakan student support services untuk meningkatkan hasil pembelajaran mahasiwa dan persistensi mahasiswa untuk tetap menyelesaikan studinya di Pendidikan Jarak Jauh. Namun, fakta lapangan menunjukkan rata-rata IPK dan IPS mahasiswa Sarjana dan Diploma Angkatan 20161 s/d 20182 masih di bawah standar IPK tuntutan pasar kerja (2.75). Solusi permasalahan tersebut adalah mendeteksi dini mahasiswa berisiko gagal menggunakan metode klasifikasi data mining berdasarkan data aktivitas Tutorial Online (Tuton) dan data pribadi mahasiswa. Pengklasifikasian mahasiswa berisiko gagal berdasarkan nilai IPS agar dapat mendeteksi lebih awal tidak hanya di semester awal tetapi juga di semester berikutnya. Selain itu, nilai IPS memiliki korelasi positif yang kuat terhadap nilai IPK sehingga nilai IPS dianggap dapat sebagai indikasi awal dari risiko kegagalan. Algoritma klasifikasi untuk model deteksi dini mahasiswa berisiko menggunakan naïve bayes, logistic regression, SVM, decision tree (CART, C5.0), random forest, dan adaboost. Tahap awal pengujian model menggunakan data aktivitas Tuton masa 20182-20191. Pembagian data training dan data testing menggunakan Stratified K-fold sebanyak 10 kali iterasi dan melakukan eksperimen metode tanpa sampling class imbalance dan metode random undersampling (50P:50N, 70P:30P, 66P:33P, 60P:40N) pada data training. Pada tahap awal pengujian model menunjukkan F1-score di minggu ke-empat tidak berbeda signifikan dengan minggu ke-delapan sehingga dianggap sebagai waktu yang tepat untuk mengintervensi lebih awal agar mahasiswa dapat berjuang di tugas berikutnya. F1-score tertinggi dari tahap awal pengujian model adalah tanpa sampling class imbalance di data training dengan algoritma random forest (90.20%), adaboost (89.20%), dan decision tree CART (88.10%). Ketiga algoritma terbaik akan diuji kembali pada tahap akhir menggunakan data testing aktivitas Tuton masa 20192. Hasil tahap akhir kinerja model deteksi dini mahasiswa berisiko kegagalan berdasarkan F1-score menunjukkan algoritma adaboost dengan nilai tertinggi (84.7%) diikuti oleh algoritma random forest (83.8%). Berdasarkan pengukuran recall, CART menunjukkan nilai tertinggi (99.9%) tetapi mengalami overfitting terhadap kelas positif sehingga tidak lebih baik dibandingkan melakukan intervensi ke seluruh mahasiswa. Kinerja terbaik untuk model deteksi dini mahasiswa berisiko gagal di UT adalah menggunakan algoritma adaboost.......Universitas Terbuka (UT) provides student support services to improve student academic outcomes and student persistence for their completion in Distance Education. However, the average cumulative and semester GPA of Bachelor and Diploma programs from academic year 20161-20182 show below labor market standard GPA (2.75%). Solution to this problem is early detection on academic failure risk through the implementation of classification data mining to predict student at-risk academic failure using Online Tutorial (Tuton) activities data and student’s personal information. Classification student at-risk academic failure based on their semester GPA in order to early detect not only on the initial semester but also on the following semester. Furthermore, semester GPA has a strong positive correlation to cumulative GPA so that semester GPA is considered as an early indication of the risk of failure. The classification algorithm for student at-risk failure early detection model using naïve bayes, logistic regression, SVM, decision tree (CART, C5.0), random forest, and adaboost. The initial model testing stage use data from Tuton activities on 20182-20191. Splitting method of training data set and testing data set using Stratified K-fold in 10 times iteration and experimenting without class imbalance sampling and random undersampling method (50P:50N, 70P:30P, 66P:33P, 60P:40N) on training data set. On The initial model testing stage shows that F1-scores on fourth week are not significantly different from the eighth week so early intervention on fourth week is the right time for student to study harder on the next assignments. The highest F1-score from the initial model testing stage is without sampling imbalance on training data set using random forest (90.20%), adaboost (89.20%), and decision tree CART (88.10%). The three best algorithms will be tested again on the final testing stage using Tuton activity on 20192 as testing data set. The F1-score results on the final student at-risk of failure early detection model stage shows that adaboost algorithm highest performance (84.7%) and followed by random forest (83.8%). Based on recall results, CART showed the highest performance (99.9%) but tend to positive class overfitting so that it was no better than intervening all of students. The best performance for student at-risk of failure early detection models at UT is using adaboost algorithm."