Rekomendasi Berdasarkan Nilai Pretest Mahasiswa Menggunakan Metode Collaborative Filtering dan Bayesian Ranking

Abstract

Abstract- Self-Regulated Learning (SRL) skill can be improved by improving students’ cognitive and metacognitive abilities. To improve metacognitive abilities, metacognitive support in learning process using e-learning needs to be included. One of the example is assisting students by giving feedbacks once students had finished doing specific avtivities. The purpose of this study was to develop a pedagogical agent with the abilities to give students feedbacks, particularly recommendations of lesson sub-materials order. Recommendations were given by considering students pretest scores (students’ prior knowledge). The computations for recommendations used Collaborative Filtering and Bayesian Ranking methods. Results obtained in this study show that based on MAP (Mean Average Precision) testings, Item-based method got the highest MAP score, which was 1. Computation time for each method was calculated to find runtime complexity of each method. The results of computation time show that Bayesian Ranking had the shortest computation time with 0,002 seconds, followed by Item-based with 0,006 seconds, User Based with 0,226 seconds, while Hybrid has the longest computation time with 0,236 seconds.

Keyword- self-regulated learning, metacognitive, metacognitive support, feedback, pretest (prior knowledge), Collaborative Filtering, Bayesian Ranking, Mean Average Precision, runtime complexity.