VIDEO REPORT USING ANDROID APPLICATION FOR IMPROVING STUDENTS' INFORMATION LITERACY IN LEARNING SOLAR SYSTEM

This study aims to investigate the effect of making a video report in learning Solar System can improve the information literacy skill of 7th-grade students. This study was conducted in a private school in Bandung. This research is used weak experimental research used one group pre-test post-test design. A sample of 32 students was selected conveniently from one class available in the school. All of the samples are treated by making the video report in the moon phases model simple project. The data was collected via the pre-test and post-test administration and Questionnaire to strengthen the result of students’ information literacy skills. The video report product also assesses by using the video report rubric. The results were statistically analyzed by using SPSS software by employing a paired sample t-test. The result shows 0.55 which is categorized into middle based on the category in Normalized Gain. Results indicate that after applied the treatment to the students, there is an improvement of students’ Information Literacy skills after making the video report. Therefore, the result of students’ information literacy showed that there was a significant difference between the result of the post-test and the result of the standard average of the knowledge dimension. It is concluded that making a video report is useful to conduct as the learning strategy to improve students’ Information Literacy Skills

Towards a hybrid recommendation approach using a community detection and evaluation algorithm

In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy