Model Analisis Aktivitas Tutor Dalam Learning Management System Berdasarkan Data Log Menggunakan K-Means Dan Deteksi Outlier

Pembelajaran tutor di LMS menyimpan data berupa log yang dapat dimanfaatkan menjadi pengetahuan tentang kinerja tutor. Kinerja tutor yang lemah akan berdampak pada kinerja mahasiswa, dan kinerja institusi secara keseluruhan. Oleh karena itu, tujuan penelitian ini untuk (1) mendeskripsikan dan memantau kinerja tutor yang lemah pada aplikasi LMS berbasis moodle berdasarkan data log, (2) mendeteksi tutor yang termasuk dalam kategori outlier berdasarkan aktivitas yang dilakukan di LMS. Tahapan penelitian ini terdiri atas 4 tahap, yaitu melakukan pengambilan data log aktivitas tutor selama satu semester (n =25), melakukan analisis deskriptif, dan analisis clustering dengan k-means dan deteksi outlier. Hasil temuan penelitian ini menunjukkan bahwa sebagian besar tutor memanfaatkan LMS hanya untuk mengumpulkan tugas, menampilkan materi dan sedikit yang disertai dengan aktivitas-aktivitas lain seperti forum, diskusi, quiz dan lainnya. Tutor dalam menampilkan standar isi atau konten hanya memberikan instruksi pengumpulan tugas dan menampilkan materi dengan menyertakan link atau video dari sumber lain. Fakta lain menunjukkan bahwa tutor jarang memberikan feedback (umpan balik) baik secara narasi maupun penilaian ketika memberikan tugas sehingga totur dalam melakukan pembelajaran online belum memenuhi standar proses pada pembelajaran online. Oleh karena itu, sebagian besar tutor underperformed karena isi dan proses pembelajaran onlinenya belum sesuai standar. AbstractThe Tutors’ learning in LMS stores data in logs which can be used as knowledge to find out about tutor performance. The tutors were weak performance have been an impact on student performance, and the performance of the institution as a whole. Therefore, this study aims to (1) describe and monitor the weak performance of tutors in the LMS application based on log data, (2) detect tutors who are included in the outlier category based on the activities in the LMS. The stages of this research were 4 stages, namely collected log data of tutors' activities for one semester (n = 25), conducted descriptive analysis, clustered analysis with K-means, and outlier detection. The results were most tutors used LMS only to collected assignments, presented material and a few were activities such as forums, discussions, quizzes and others. Tutors presented standard content was only provide instructions for submitted assignments and displayed material by including links or videos from other sources. Another fact showed who were tutors rarely provided feedback both in narrative and assessment when giving assignments so that the tutors in doing online learning has not met the standard process in online learning. Therefore, most of the tutors have underperformed because the content and learning process have not up to standard

Analyzing student inquiry data using process discovery and sequence classification

This paper reports on results of applying process discovery mining and sequence classifcation mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and social networks. As an example of our current research efforts, we applied temporal data mining analysis techniques to a PSLC DataShop data set [17, 18, 19, 20]. First, we show that process mining techniques allow for discovery of learning processes from student behaviours. Second, sequential pattern mining is used to classify students according to skill. Our results show that considering sequences of activities as opposed to single events improved classifcation by up to 230%.Peer reviewed: YesNRC publication: Ye