Learning by Asking Questions

We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA training in that most questions are not observed during training time, and the learner must ask questions it wants answers to. Thus, LBA more closely mimics natural learning and has the potential to be more data-efficient than the traditional VQA setting. We present a model that performs LBA on the CLEVR dataset, and show that it automatically discovers an easy-to-hard curriculum when learning interactively from an oracle. Our LBA generated data consistently matches or outperforms the CLEVR train data and is more sample efficient. We also show that our model asks questions that generalize to state-of-the-art VQA models and to novel test time distributions

PERSEPSI GURU PAMONG TERHADAP KETERAMPILAN DASAR MENGAJAR MAHASISWA MAGANG TIGA DI SMA SWASTA SANTU XAVERIUS GUNUNGSITOLI TAHUN PELAJARAN 2020/2021

ABSTRACT - This study aims to: (1) knowing the perception of the civil servant teacher on the basic skills of teaching three apprentice students. (2) fiind some basic teaching skills that must be possessed by interns in helping the success of the teaching process in the classroom. (3) knowing the efforts given by the civil servant teacher to overcome weaknesses in mastering basic teaching skills.. The results showed that: (1) basic teaching skills taught to students help in carrying out learning activities. (2) There are still some weaknesses of the third apprentice students in mastering basic teaching skills. (3) The efforts and suggestions given by the civil servant teacher are that students must be able to guide the lesson plans well, and can invite students to be involved in learning by asking questions and opportunities to express their opinions.Key words: Basic Teaching Skills, Perception of Pamong Teacher

Group Leadership in Online Collaborative Learning

The innovations in computer and communications technologies combined with on-going needs to deliver educational programs to students regardless of their physical locations, have lead to the innovation of distance education programs and technologies. To keep up with recent developments in both areas of technologies and techniques related to distance education programs, educators, administrators, and researchers are in constant needs of learning about issues, solutions, and challenges of these technologies. The Encyclopedia of Distance Learning offers the most comprehensive coverage of the issues, concepts, trends, and technologies of distance learning. With more than 2,000 terms and definitions and over 6,000 additional references, this authoritative 4-volume encyclopedia is considered the foremost reference source for the latest understanding, discoveries, and research in the field of distance learning

Evaluating Text-to-Image Matching using Binary Image Selection (BISON)

Providing systems the ability to relate linguistic and visual content is one of the hallmarks of computer vision. Tasks such as text-based image retrieval and image captioning were designed to test this ability but come with evaluation measures that have a high variance or are difficult to interpret. We study an alternative task for systems that match text and images: given a text query, the system is asked to select the image that best matches the query from a pair of semantically similar images. The system's accuracy on this Binary Image SelectiON (BISON) task is interpretable, eliminates the reliability problems of retrieval evaluations, and focuses on the system's ability to understand fine-grained visual structure. We gather a BISON dataset that complements the COCO dataset and use it to evaluate modern text-based image retrieval and image captioning systems. Our results provide novel insights into the performance of these systems. The COCO-BISON dataset and corresponding evaluation code are publicly available from \url{http://hexianghu.com/bison/}